Intelligent processing method and device for concurrent travel order

Abstract

The invention belongs to the technical field of intelligent processing methods, and particularly discloses an intelligent processing method and device for concurrent travel orders, and the method comprises the steps: firstly, receiving and integrating the orders, collecting the travel orders of all channels in real time, and classifying the travel orders according to travel modes; and performing dynamic load balancing, screening order processing node error times according to a preset error rule, analyzing a processing efficiency defect degree index in combination with response time data in a database, and judging whether the processing efficiency is qualified or not and performing adjustment. And then, priority allocation: extracting customer consumption amounts and times from a database, and analyzing a priority index to determine customer priorities so as to dispatch orders. And finally, carrying out business travel order distribution, extracting the order processing quantity and time of the processing personnel from the database, analyzing the processing proportion and efficiency to determine the dominant order type, and analyzing the matching degree index in combination with the collected order quantity to realize reasonable distribution of various business travel orders and the processing personnel in each channel.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent processing methods, and relates to an intelligent processing method and device for concurrent travel orders. Background Art

[0002] With the expansion of corporate scale and global business, travel management faces unprecedented complexity. Corporate travel needs are becoming increasingly diversified and personalized, including multiple links such as air ticket booking, hotel arrangement, car rental service, and catering reservation, and each link involves numerous suppliers and service providers. Traditional manual processing methods can no longer meet the needs of such complexity, and intelligent and automated solutions are urgently needed.

[0003] For example, a method and system for processing an order for a travel service according to Chinese patent publication number CN115660791A includes: a user places an order on a user terminal to form travel order information; the user terminal uses a first time setting model to obtain a first preset time interval, and sends the travel order information to a business travel platform at regular intervals according to the first preset time interval; after receiving the travel order information, the business travel platform compares the travel order information currently received with the travel order information last received in real time to obtain a consistency judgment result; the business travel platform feeds back a time setting model to set a second preset time interval, and sends a response instruction to the user terminal according to the second preset time interval; after receiving the response instruction, the user terminal determines whether to verify the user's travel order information according to the response instruction, obtains valid travel order information through verification, and sends the valid travel order information to the business travel platform.

[0004] The above existing technologies have the following problems: 1. The existing technologies cannot effectively identify order processing nodes with low processing efficiency or frequent errors. The inability to timely identify and process inefficient nodes means that these nodes will continue to drag down the overall processing speed, resulting in an extension of the order processing cycle, which in turn affects the overall operational efficiency of the enterprise.

[0005] 2. Existing technologies make it difficult to effectively allocate service resources when resources are limited. Without a clear priority system, resources are often evenly distributed or non-critical tasks are prioritized, causing key customers to wait too long, which in turn affects business cooperation and customer experience.

[0006] 3. Existing technologies often adopt relatively simple allocation strategies, such as polling, random allocation, or average allocation based on workload. These methods ignore the differences in expertise and efficiency of different processors in handling specific types of travel orders. Therefore, even if some processors are good at handling a certain type of order, they may be assigned to areas where they are not good at, resulting in inefficient processing. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, an intelligent processing method and device for concurrent travel orders are proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an intelligent processing method and device for concurrent travel orders, including: S1, order reception and integration: real-time collection of travel orders from various channels, classification based on travel modes, and acquisition of various types of travel orders from various channels.

[0009] S2. Dynamic load balancing: based on the preset error rules, filter out the number of errors in each order processing node in each type of travel orders in each channel and extract the response time data of each order processing node in each type of travel orders in each channel from the database, analyze the processing efficiency defect degree index of each order processing node in each type of travel orders in each channel, determine whether the processing efficiency of the order processing nodes in each type of travel orders in each channel is qualified, and then make adjustments.

[0010] S3. Priority allocation: Based on extracting the consumption amount and consumption frequency of each customer corresponding to each type of travel order from each channel from the database, analyze the priority index of each customer corresponding to each type of travel order from each channel, obtain the priority level of each customer corresponding to each type of travel order from each channel, and then assign the order.

[0011] S4. Travel order allocation: Based on the number and processing time of each type of travel orders processed by each processing personnel extracted from the database, the processing proportion and processing efficiency of each type of travel orders processed by each processing personnel are analyzed, and then the dominant travel order types of each processing personnel are obtained. Based on the number of each type of travel orders collected from each channel, the matching degree index of each type of travel orders from each channel and each processing personnel is analyzed, and then the various types of travel orders from each channel are allocated to each processing personnel.

[0012] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention can achieve comprehensive control over travel business by collecting travel orders from various channels in real time. It is no longer necessary to manually query and sort orders from multiple scattered channels one by one, which greatly saves time and labor costs. At the same time, by classifying travel orders based on travel modes, different types of travel orders can be clearly seen at a glance, which is convenient for managers to quickly understand travel situations and make more reasonable decisions, thereby significantly improving the efficiency of travel management.

[0013] (2) The present invention can accurately identify which order processing nodes in various travel orders of various channels have errors through preset error rules, so as to quickly locate the problem. Based on the analysis of the number of errors and response time data, it can identify nodes with low processing efficiency and then optimize the processing flow of these nodes to reduce the occurrence of errors, improve the processing speed, and promptly discover and correct errors in order processing, which can reduce customer complaints and refunds caused by errors and improve customer satisfaction.

[0014] (3) The present invention can ensure that high-value or important customers receive faster and higher-quality services by accurately evaluating the priority of customers, which helps to improve customer satisfaction and loyalty. The priority system helps companies to reasonably allocate service resources under limited resources and ensure that key customers receive priority treatment, which helps to improve overall service efficiency and quality. Prioritizing services for high-value customers makes them feel the company's attention and care, thereby enhancing customer stickiness and promoting long-term cooperation.

[0015] (4) The present invention can identify the dominant travel order types of each processing personnel by analyzing the processing proportion and processing efficiency of each type of travel order. This helps to assign orders to the most suitable processing personnel, thereby improving the overall processing efficiency. The calculation of the matching index enables enterprises to allocate human resources more reasonably and assign orders to processing personnel with high matching degrees, which can reduce order processing time, improve customer satisfaction, and reduce errors and costs caused by improper processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a schematic diagram for implementing the method steps of the present invention.

[0018] Figure 2 A schematic diagram of the process by which the system of the present invention obtains the processing proportion and processing efficiency of each processing personnel in processing various types of travel orders.

[0019] Figure 3 It is a schematic diagram of the connection of each module of the system of the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] See also Figure 1 As shown, the present invention provides an intelligent processing method and device for concurrent travel orders, the method comprising: S1, order reception and integration: collecting travel orders from various channels in real time, classifying them based on travel modes, and obtaining various types of travel orders from various channels.

[0022] As a preferred embodiment, the channels include: the company's internal system (such as enterprise resource planning system ERP), official website, mobile application, telephone service center, third-party online travel agency, etc.

[0023] The various types of travel orders include: air travel orders, train travel orders and car rental travel orders, etc.

[0024] The present invention can achieve comprehensive control over travel business by collecting travel orders from various channels in real time. It is no longer necessary to manually query and organize orders from multiple scattered channels one by one, which greatly saves time and labor costs. At the same time, classification based on travel mode makes different types of travel orders clear at a glance, which is convenient for managers to quickly understand travel situations and make more reasonable decisions, thereby significantly improving the efficiency of travel management.

[0025] S2. Dynamic load balancing: based on the preset error rules, filter out the number of errors in each order processing node in each type of travel orders in each channel and extract the response time data of each order processing node in each type of travel orders in each channel from the database, analyze the processing efficiency defect degree index of each order processing node in each type of travel orders in each channel, determine whether the processing efficiency of the order processing nodes in each type of travel orders in each channel is qualified, and then make adjustments.

[0026] As a preferred embodiment, the preset error rules include: data fields are missing or in incorrect format, the order amount exceeds a preset threshold, the order processing time exceeds the allowable range, the order status conversion does not conform to the expected process, and the order does not match the inventory or payment system, etc.

[0027] The order processing nodes include: order receipt, review, payment, confirmation, ticket issuance, refund and change, etc.

[0028] Further, the analysis of the processing efficiency defect index of each order processing node in each type of travel order of each channel includes: extracting the total number of operations of each order processing node in each type of travel order of each channel from the database, dividing the number of errors of each order processing node in each type of travel order of each channel by the total number of operations of the corresponding order processing node, and obtaining the error rate of each order processing node in each type of travel order of each channel, which is recorded as Wherein j represents the number corresponding to the j-th order processing node, j=1,2,3,...J, q represents the number corresponding to the q-th channel, q=1,2,3,...Q, and l represents the number corresponding to the l-th type of travel order, l=1,2,3,...L.

[0029] The processing start time and processing end time are extracted from the response time data of each order processing node in each type of travel order in each channel.

[0030] The processing end time and processing start time of each order processing node in each type of travel order in each channel are compared to obtain the response time of each order processing node in each type of travel order in each channel.

[0031] The response time of each order processing node in each type of travel order in each channel is compared with the preset allowed response time of the order processing node, and the response time deviation value of each order processing node in each type of travel order in each channel is obtained, which is recorded as

[0032] Analyze the processing efficiency defect index of each order processing node in each type of travel order in each channel Where α1 and α2 represent the weights corresponding to the error rate and response time deviation value of the order processing node in the travel order, respectively, and α1+α2=1.

[0033] The α1 and α2 can be set to 0.6 and 0.4 respectively. The error rate of the order processing node in the travel order has a more significant impact on the processing efficiency because it is directly related to the accuracy of the order and the customer experience. The response time deviation value may be affected by a variety of external factors, such as system load, network delay, etc., and sometimes even if the response time is slightly longer, it may not directly lead to business problems. Therefore, the weight corresponding to the error rate of the order processing node in the travel order is greater than the weight corresponding to the response time deviation value.

[0034] Furthermore, the specific process of judging whether the processing efficiency of the order processing nodes in various types of travel orders in various channels is qualified is: comparing the processing efficiency defect degree index of each order processing node in various types of travel orders in various channels with a preset processing efficiency defect degree index threshold; if the processing efficiency defect degree index of a certain order processing node in a certain type of travel order in a certain channel is greater than the preset processing efficiency defect degree index threshold, then the processing efficiency of the order processing node in this type of travel orders in the channel is unqualified, and the order processing nodes with unqualified processing efficiency in various types of travel orders in various channels are screened out.

[0035] The present invention can accurately identify which order processing nodes in various travel orders of various channels have errors through preset error rules, so as to quickly locate the problem, and based on the analysis of the number of errors and response time data, it can identify nodes with low processing efficiency, and then optimize the processing flow of these nodes, reduce the occurrence of errors, improve the processing speed, and promptly discover and correct errors in order processing, which can reduce customer complaints and refunds caused by errors and improve customer satisfaction.

[0036] S3. Priority allocation: Based on extracting the consumption amount and consumption frequency of each customer corresponding to each type of travel order from each channel from the database, analyze the priority index of each customer corresponding to each type of travel order from each channel, obtain the priority level of each customer corresponding to each type of travel order from each channel, and then assign the order.

[0037] Furthermore, the analysis of the priority index of each customer corresponding to each type of travel order of each channel includes: recording the consumption amount and consumption frequency of each customer corresponding to each type of travel order of each channel as and Wherein g represents the number corresponding to the g-th customer, g = 1, 2, 3, ...G.

[0038] Substitute the consumption amount of each customer corresponding to each type of travel order in each channel into the average calculation formula to obtain the reference consumption amount of the customer corresponding to each type of travel order in each channel, recorded as

[0039] According to the analysis method of the reference consumption amount of customers corresponding to various types of travel orders in each channel, the reference consumption times of customers corresponding to various types of travel orders in each channel are obtained in the same way, recorded as

[0040] Analyze the priority index of each customer corresponding to each type of travel order in each channel Among them, δ1 and δ2 represent the weights corresponding to the number of consumptions and the consumption amount of the customer corresponding to the travel order, respectively, and δ1+δ2=1.

[0041] As a preferred embodiment, δ1 and δ2 can be set to 0.7 and 0.3 respectively, because the consumption amount can usually better reflect the customer's economic strength and the value of his contribution to the enterprise. Customers with high consumption amounts often bring more revenue to the enterprise. Although the consumption frequency reflects the customer's activity, its direct contribution to the enterprise is smaller than the consumption amount. Therefore, the weight corresponding to the number of consumption times of the customer corresponding to the travel order is smaller than the weight corresponding to the consumption amount.

[0042] Furthermore, the step of obtaining the priority level of each customer corresponding to each type of travel order of each channel includes: comparing the priority index of each customer corresponding to each type of travel order of each channel with the priority index range corresponding to each preset priority level; if the priority index of a customer corresponding to a certain type of travel order of a certain channel belongs to the priority index range corresponding to a preset priority level, then the priority index of the customer corresponding to the travel order of this type in the channel belongs to the priority level, thereby obtaining the priority level of each customer corresponding to the various types of travel orders of each channel.

[0043] The present invention can ensure that high-value or important customers receive faster and higher-quality services by accurately evaluating the priority of customers, which helps to improve customer satisfaction and loyalty. The priority system helps companies to reasonably allocate service resources under limited resources and ensure that key customers receive priority treatment, which helps to improve overall service efficiency and quality. Prioritizing high-value customers makes them feel the company's attention and care, thereby enhancing customer stickiness and promoting long-term cooperation.

[0044] S4. Travel order allocation: Based on the number and processing time of each type of travel orders processed by each processing personnel extracted from the database, the processing proportion and processing efficiency of each type of travel orders processed by each processing personnel are analyzed, and then the dominant travel order types of each processing personnel are obtained. Based on the number of each type of travel orders collected from each channel, the matching degree index of each type of travel orders from each channel and each processing personnel is analyzed, and then the various types of travel orders from each channel are allocated to each processing personnel.

[0045] See also Figure 2 As shown further, the processing proportion and processing efficiency of each processing personnel in processing each type of travel orders include: B1. Based on the number of each type of travel orders processed by each processing personnel, the total number of travel orders processed by each processing personnel is summarized.

[0046] B2. Divide the number of travel orders processed by each processing staff by the total number of travel orders processed by the corresponding processing staff to obtain the processing ratio of each travel order processed by each processing staff, which is recorded as Wherein d represents the number corresponding to the d-th processing personnel, d = 1, 2, 3...D.

[0047] B3. Based on the processing time of each processing staff for each type of travel order, the total processing time of each processing staff for each type of travel order is obtained. The processing time of each processing staff for each type of travel order is divided by the total processing time of the travel order to obtain the processing efficiency of each processing staff for each type of travel order, which is recorded as

[0048] Further, the analysis of the advantage index of each type of travel order of each processing personnel Among them, Propor′ and effic′ l They respectively represent the reference processing proportion and reference processing efficiency of travel orders processed by the processing personnel, β1 and β2 respectively represent the weights corresponding to the processing proportion deviation value and processing efficiency deviation value of travel orders processed by the processing personnel, and β1+β2=1.

[0049] As a preferred embodiment, the β1 and β2 can be set to 0.4 and 0.6 respectively. The processing ratio deviation value reflects the workload of the processing personnel to a certain extent, but it is not comprehensive enough to measure the degree of advantage based solely on the processing ratio deviation value. The processing efficiency deviation value is directly related to the processing speed and timeliness of travel orders, which is crucial to meeting customer needs and improving the overall operational efficiency of the enterprise. Efficient processing personnel can complete the processing of travel orders in a shorter time, reduce customer waiting time, and improve customer satisfaction. Therefore, the weight corresponding to the processing ratio deviation value of travel orders processed by the processing personnel is less than the weight corresponding to the processing efficiency deviation value.

[0050] The specific setting process of the reference processing ratio of the processing personnel for handling travel orders is: substitute the processing ratio of each type of travel orders handled by each processing personnel into the average calculation formula, obtain the average processing ratio of each type of travel orders handled by the processing personnel, and use it as the reference processing ratio of the processing personnel for handling travel orders.

[0051] Based on the analysis method of the reference processing ratio of travel orders handled by processing personnel, the reference processing efficiency of travel orders handled by processing personnel is obtained in the same way.

[0052] From the advantage degree indexes of various types of travel orders of each processing personnel, the travel order type corresponding to the maximum advantage degree index of each processing personnel is screened out, and the travel order type corresponding to the maximum advantage degree index of each processing personnel is used as the advantageous travel order type of each processing personnel.

[0053] Furthermore, the analysis of the matching index between each type of travel orders of each channel and each processing personnel includes: summing up the number of each type of travel orders of each channel to obtain the total number of travel orders of each channel.

[0054] Divide the number of each type of travel orders in each channel by the total number of travel orders to obtain the ratio of each type of travel orders in each channel.

[0055] The advantage index of each type of travel order of each processing personnel is multiplied by the ratio of each type of travel orders of each channel to obtain the matching index of each type of travel orders of each channel and each processing personnel.

[0056] Furthermore, the specific process of allocating various types of travel orders from various channels to various processing personnel is: screen out travel orders and processing personnel corresponding to the largest matching degree index from the matching degree index of various types of travel orders from various channels and processing personnel, allocate this type of travel orders of the channel to the corresponding processing personnel, and then allocate various types of travel orders from various channels to corresponding processing personnel.

[0057] The present invention can identify the dominant travel order types of each processing personnel by analyzing the processing proportion and processing efficiency of each type of travel order handled by each processing personnel. This helps to assign orders to the most suitable processing personnel, thereby improving the overall processing efficiency. The calculation of the matching degree index enables enterprises to allocate human resources more reasonably and assign orders to processing personnel with high matching degrees, which can reduce order processing time, improve customer satisfaction, and reduce errors and costs caused by improper processing.

[0058] See also Figure 3 As shown, the second aspect of the present invention provides an intelligent processing device for concurrent travel orders, including: an order receiving and integrating module, a dynamic load balancing module, a priority allocation module, a travel order allocation module and a database.

[0059] The dynamic load balancing module is connected to the order receiving integration module and the priority allocation module respectively, the priority allocation module is connected to the dynamic load balancing module and the travel order allocation module respectively, and the database is connected to the dynamic load balancing module, the priority allocation module, and the travel order allocation module respectively.

[0060] Order receiving and integration module: used to collect travel orders from various channels in real time, classify them based on travel modes, and obtain various types of travel orders from various channels.

[0061] Dynamic load balancing module: used to screen out the order processing nodes with errors in various types of travel orders in various channels based on preset error rules and extract the response time data of each order processing node in various types of travel orders in various channels from the database, analyze the processing efficiency defect degree index of the order processing nodes of various types of travel orders in various channels, determine whether the processing efficiency of each order processing node in each channel is qualified, screen out the order processing nodes in each channel with unqualified processing efficiency, and then make adjustments.

[0062] Priority allocation module: It is used to extract the consumption amount and consumption frequency of each customer corresponding to each type of travel order from each channel from the database, analyze the priority index of each customer corresponding to each type of travel order from each channel, obtain the priority level of each customer corresponding to each type of travel order from each channel, and then assign orders.

[0063] Travel order allocation module: It is used to analyze the processing proportion and processing efficiency of each processing personnel for each type of travel orders based on the number and processing time of each type of travel orders processed by each processing personnel extracted from the database, and then obtain the dominant travel order type of each processing personnel, and then analyze the matching degree index of each type of travel orders from each channel and each processing personnel based on the number of each type of travel orders collected from each channel, and then allocate each type of travel orders from each channel to each processing personnel.

[0064] Database: used to store the total number of operations of each order processing node in each type of travel orders from each channel, store the response time data of each order processing node in each type of travel orders from each channel, store the consumption amount and consumption frequency of each customer corresponding to each type of travel orders from each channel, and store the number and processing time of each type of travel orders processed by each processing personnel.

[0065] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent processing method for concurrent travel orders, characterized in that: The method includes: S1. Order receiving and integration: collect travel orders from various channels in real time, classify them based on travel modes, and obtain various travel orders from various channels; S2. Dynamic load balancing: based on preset error rules, the number of errors of each order processing node in each type of travel order in each channel is screened out, and the response time data of each order processing node in each type of travel order in each channel is extracted from the database, and the processing efficiency defect degree index of each order processing node in each type of travel order in each channel is analyzed to determine whether the processing efficiency of the order processing node in each type of travel order in each channel is qualified, and then make adjustments; S3, priority allocation: based on extracting the consumption amount and consumption frequency of each customer corresponding to each type of travel order of each channel from the database, analyzing the priority index of each customer corresponding to each type of travel order of each channel, obtaining the priority level of each customer corresponding to each type of travel order of each channel, and then dispatching the order; S4. Travel order allocation: Based on the number and processing time of each type of travel orders processed by each processing personnel extracted from the database, the processing proportion and processing efficiency of each type of travel orders processed by each processing personnel are analyzed, and then the dominant travel order types of each processing personnel are obtained. Based on the number of each type of travel orders collected from each channel, the matching degree index of each type of travel orders from each channel and each processing personnel is analyzed, and then the various types of travel orders from each channel are allocated to each processing personnel.

2. According to claim 1, a method and device for intelligently processing concurrent travel orders, characterized in that: The analysis of the processing efficiency defect index of each order processing node in each type of travel order in each channel includes: The total number of operations of each order processing node in each type of travel order of each channel is extracted from the database. The number of errors in each order processing node in each type of travel order of each channel is divided by the total number of operations of the corresponding order processing node to obtain the error rate of each order processing node in each type of travel order of each channel, which is recorded as Where j represents the number corresponding to the j-th order processing node, j = 1, 2, 3, ... J, q represents the number corresponding to the q-th channel, q = 1, 2, 3, ... Q, l represents the number corresponding to the l-th type of travel order, l = 1, 2, 3, ... L; Extract the processing start time and processing end time from the response time data of each order processing node in each type of travel order in each channel; Compare the processing end time and processing start time of each order processing node in each type of travel order in each channel to obtain the response time of each order processing node in each type of travel order in each channel; The response time of each order processing node in each type of travel order in each channel is compared with the preset allowed response time of the order processing node, and the response time deviation value of each order processing node in each type of travel order in each channel is obtained, which is recorded as Analyze the processing efficiency defect index of each order processing node in each type of travel order in each channel Where α1 and α2 represent the weights corresponding to the error rate and response time deviation value of the order processing node in the travel order, respectively, and α1+α2=1.

3. According to claim 1, a method and device for intelligently processing concurrent travel orders, characterized in that: The specific process of judging whether the processing efficiency of the order processing nodes in various travel orders of various channels is qualified is as follows: The processing efficiency defect degree index of each order processing node in each type of travel orders in each channel is compared with the preset processing efficiency defect degree index threshold. If the processing efficiency defect degree index of a certain order processing node in a certain type of travel orders in a certain channel is greater than the preset processing efficiency defect degree index threshold, the processing efficiency of the order processing node in this type of travel orders in the channel is unqualified, and the order processing nodes with unqualified processing efficiency in various types of travel orders in each channel are screened out.

4. According to claim 2, a method and device for intelligently processing concurrent travel orders, characterized in that: The analysis of the priority index of each customer corresponding to each type of travel order in each channel includes: The consumption amount and consumption frequency of each customer corresponding to each type of travel order in each channel are recorded as and Where g represents the number corresponding to the g-th customer, g = 1, 2, 3, ... G; Substitute the consumption amount of each customer corresponding to each type of travel order in each channel into the average calculation formula to obtain the reference consumption amount of the customer corresponding to each type of travel order in each channel, recorded as According to the analysis method of the reference consumption amount of customers corresponding to various types of travel orders in each channel, the reference consumption times of customers corresponding to various types of travel orders in each channel are obtained in the same way, recorded as Analyze the priority index of each customer corresponding to each type of travel order in each channel Among them, δ1 and δ2 represent the weights corresponding to the number of consumptions and the consumption amount of the customer corresponding to the travel order, respectively, and δ1+δ2=1.

5. According to claim 1, a method and device for intelligently processing concurrent travel orders, characterized in that: The priority level of each customer corresponding to each type of travel order of each channel is obtained, including: The priority index of each customer corresponding to each type of travel order in each channel is compared with the priority index range corresponding to each preset priority level. If the priority index of a customer corresponding to a certain type of travel order in a certain channel belongs to the priority index range corresponding to a preset priority level, then the priority index of the customer corresponding to this type of travel order in the channel belongs to the priority level, and the priority level of each customer corresponding to each type of travel order in each channel is obtained.

6. The method and device for intelligently processing concurrent travel orders according to claim 1, characterized in that: The processing proportion and processing efficiency of each processing personnel in processing various types of travel orders include: B1. Based on the number of travel orders of each type processed by each processing personnel, the total number of travel orders processed by each processing personnel is obtained; B2. Divide the number of travel orders processed by each processing staff by the total number of travel orders processed by the corresponding processing staff to obtain the processing ratio of each travel order processed by each processing staff, which is recorded as Where d represents the number corresponding to the dth processing personnel, d = 1, 2, 3...D; B3. Based on the processing time of each processing staff for each type of travel order, the total processing time of each processing staff for each type of travel order is obtained. The processing time of each processing staff for each type of travel order is divided by the total processing time of the travel order to obtain the processing efficiency of each processing staff for each type of travel order, which is recorded as 7. The method and device for intelligently processing concurrent travel orders according to claim 6, characterized in that: The analysis of the superiority index of various types of travel orders of each processing personnel Among them, Propor′ and effic′ l They respectively represent the reference processing proportion and reference processing efficiency of travel orders processed by the processing personnel, β1 and β2 respectively represent the weights corresponding to the processing proportion deviation value and processing efficiency deviation value of travel orders processed by the processing personnel, and β1+β2=1.

8. The method and device for intelligently processing concurrent travel orders according to claim 1, characterized in that: The analysis of the matching index between various travel orders of various channels and various processing personnel includes: Summarize the number of various types of travel orders from each channel to obtain the total number of travel orders from each channel; Divide the number of each type of travel orders in each channel by the total number of travel orders to obtain the ratio of each type of travel orders in each channel; The advantage index of each type of travel order of each processing personnel is multiplied by the ratio of each type of travel orders of each channel to obtain the matching index of each type of travel orders of each channel and each processing personnel.

9. The method and device for intelligently processing concurrent travel orders according to claim 1, characterized in that: The specific process of allocating various travel orders from various channels to various processing personnel is as follows: The travel orders and processing personnel corresponding to the largest matching degree index are screened out from the matching degree indexes of various types of travel orders and processing personnel in various channels, and the travel orders of this type in the channel are assigned to the corresponding processing personnel, and then various types of travel orders in various channels are assigned to corresponding processing personnel.

10. An intelligent processing device for concurrent travel orders, characterized in that: include: Order receiving and integration module: used to collect travel orders from various channels in real time, classify them based on travel modes, and obtain various travel orders from various channels; Dynamic load balancing module: used to filter out the number of errors in each order processing node in each type of travel order in each channel based on preset error rules and extract the response time data of each order processing node in each type of travel order in each channel from the database, analyze the processing efficiency defect degree index of each order processing node in each type of travel order in each channel, determine whether the processing efficiency of the order processing node in each type of travel order in each channel is qualified, and then make adjustments; Priority allocation module: used to extract the consumption amount and consumption frequency of each customer corresponding to each type of travel order of each channel from the database, analyze the priority index of each customer corresponding to each type of travel order of each channel, obtain the priority level of each customer corresponding to each type of travel order of each channel, and then assign the order; Travel order allocation module: used to analyze the processing proportion and processing efficiency of each processing personnel for each type of travel order based on the number and processing time of each type of travel order processed by each processing personnel extracted from the database, and then obtain the dominant travel order type of each processing personnel, and then analyze the matching degree index of each type of travel order of each channel and each processing personnel based on the number of each type of travel order collected from each channel, and then allocate each type of travel order of each channel to each processing personnel; Database: used to store the total number of operations of each order processing node in each type of travel orders from each channel, store the response time data of each order processing node in each type of travel orders from each channel, store the consumption amount and consumption frequency of each customer corresponding to each type of travel orders from each channel, and store the number and processing time of each type of travel orders processed by each processing personnel.

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