Work order automatic distribution monitoring method and system

Abstract

The invention relates to the technical field of work order management, and discloses a work order automatic distribution monitoring method and system, and the system comprises a data collection module and an intelligent management module. According to the work order automatic distribution monitoring method and system, management data of all pre-sales work orders, after-sales work orders and workers are acquired through a data acquisition module and are classified to form a data set, an intelligent management module analyzes the purchase intention of each pre-sales work order customer and the emergency degree of each after-sales work order, a corresponding intention coefficient and an emergency coefficient are generated, and the corresponding intention coefficient and the emergency coefficient are stored in a database; the intelligent management module analyzes the work order processing capability of each employee, generates a corresponding matching coefficient, distinguishes pre-sales or after-sales qualification, and multi-dimensionally evaluates more balanced load, the intelligent management module evaluates the processing levels and priorities of the pre-sales work order and the after-sales work order, and then arranges and generates a pre-sales matching list; the optimal processing personnel is automatically matched, the time cost of manual allocation is reduced, and the dynamic allocation efficiency is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of work order management, and in particular to a method and system for automatically allocating and monitoring work orders. Background Art

[0002] Work orders are a tool for recording, processing, assigning, and tracking the completion of work tasks. These systems offer numerous advantages. They improve productivity by clearly assigning tasks to specific team members, preventing missed or duplicated tasks. They optimize resource utilization, effectively managing and scheduling resources, ensuring appropriate task allocation and reducing resource waste and duplication. To clarify responsibilities, each work order should have a designated responsible individual and a deadline, ensuring the responsible individual clearly understands the task content and completion date. Work order monitoring allows managers to monitor task progress and status in real time, identifying issues and taking action to ensure timely completion. Monitoring also allows for timely detection and correction of errors, improving work accuracy and quality. Furthermore, work order systems collect extensive data, such as processing time, results, and customer satisfaction, to support management decision-making, optimize processes, enhance service quality, and reduce costs. Work orders, their assignment, and monitoring are crucial in the modern workplace. Enterprises should prioritize their development and application to ensure they can effectively respond to customer needs, improve the efficiency and quality of problem resolution, and ultimately enhance customer satisfaction and loyalty.

[0003] Currently, the traditional automatic work order allocation and monitoring method has the problem of a single allocation rule. Under high load conditions, when too many work orders are received, some employees will be overloaded with tasks while other employees will be relatively idle. In addition, this method lacks the ability to analyze changes in pre-sales intentions and post-sales urgency in real time, and cannot adjust the allocation strategy in a timely manner. This unbalanced resource utilization will affect overall processing efficiency and user satisfaction. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a method and system for automatic work order allocation and monitoring, which has the advantages of more balanced multi-dimensional evaluation load and higher dynamic allocation efficiency. It solves the problem that the traditional work order automatic allocation and monitoring method has a single allocation rule and cannot adjust the allocation strategy in time.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solutions: a method and system for automatically allocating and monitoring work orders, comprising a data acquisition module and an intelligent management module;

[0006] The data acquisition module is composed of an integrated data unit, a terminal data unit, and an employee data unit. The integrated data unit collects a pre-sales data set via a network connection to a database. The pre-sales data set includes management data of all pre-sales work orders. The terminal data unit collects a post-sales data set via a network connection to a terminal device. The post-sales data set includes management data of all after-sales work orders. The employee data unit collects an employee data set via a network connection to a database. The employee data set includes management data of all staff members.

[0007] The intelligent management module consists of a pre-sales evaluation unit, a post-sales evaluation unit, a matching analysis unit and an allocation monitoring unit. The pre-sales evaluation unit analyzes the purchase intention of each pre-sales work order customer based on the pre-sales data set and generates a corresponding intention coefficient Yxx. The post-sales evaluation unit analyzes the urgency of each after-sales work order based on the after-sales data set and generates a corresponding urgency coefficient Jix. The matching analysis unit analyzes the work order processing capability of each employee based on the employee data set and generates a corresponding matching coefficient Pix. The allocation monitoring unit sets an intention threshold range YXY and an urgency threshold range JIY, and then evaluates the processing level and priority of pre-sales work orders and after-sales work orders based on the intention coefficient Yxx and the urgency coefficient Jix. The processing priority of first-level pre-sales work orders and first-level after-sales work orders is higher than that of second-level pre-sales work orders and second-level after-sales work orders, and the processing priority of second-level pre-sales work orders and second-level after-sales work orders is higher than that of third-level pre-sales work orders and third-level after-sales work orders. The allocation monitoring unit arranges and generates a matching list based on the employee data set and the matching coefficient Pix, and performs corresponding work order allocation.

[0008] Preferably, the expression of the pre-sale data set is {Q1 s 、Q2 s 、Q3 s ,...,Qn s}, Q1 s To Qn s Represents the management data of the first to nth pre-sales work orders. The pre-sales work order management data includes the customer's historical purchase quantity, purchase budget, number of consultations, and browsing time. s represents the time when the pre-sales work order was generated.

[0009] Preferably, the expression of the after-sales data set is {H1 j 、H2 j 、H3 j 、...、Hm j}, H1 j To Hm j Represents the management data of the first to the,th after-sales work orders. The after-sales work order management data includes the product's service type, purchase time, and historical after-sales service times. j represents the time when the after-sales work order was generated.

[0010] Preferably, the expression of the employee data set is {R1 l 、R2 l 、R3 l 、...、Ry l}, R1 l To Ry l Represents the management data of the first to y-th staff members. Staff management data includes the current work order load, response time, number of historical work orders processed, work qualifications, number of orders completed, and number of customer complaints. l represents the staff member's length of service.

[0011] Preferably, the calculation process of the intention coefficient Yxx is as follows:

[0012] Based on the pre-sales data set, extract the management data of the i-th pre-sales work order and mark the historical purchase quantity of the i-th pre-sales work order as ls i , mark the purchase budget of the i-th pre-sales work order as ys i , mark the number of consultations for the i-th pre-sales work order as zc i , mark the browsing time of the i-th pre-sales work order as lc i , mark the generation time of the i-th pre-sales work order as i s ;

[0013]

[0014] In the formula, α1 represents the weight for the number of historical purchases, KD represents the average customer spending at the current time point, α2 represents the weight for the ratio of purchase budget to average customer spending, α3 represents the weight for the ratio of consultation times to browsing time, DS represents the current time point, and DS-i s represents the waiting time of the customer for the i-th pre-sales work order, BSQ represents the standard value used to measure the waiting time of the customer for the pre-sales work order, α4 represents the weight of the ratio of the standard value to the waiting time of the customer for the pre-sales work order, α1, α2, α3 and α4 are all constants, and α1+α2+α3+α4=1, Indicates that the intention coefficient Yxx of the i-th pre-sales work order customer is calculated according to the weights α1, α2, α3 and α4 i .

[0015] Preferably, the calculation process of the emergency coefficient Jix is as follows:

[0016] According to the after-sales data set, the management data of the kth after-sales work order is extracted, and the purchase time of the kth after-sales work order is marked as gs k , mark the historical after-sales service times of the kth after-sales work order as fw k , mark the generation time of the kth after-sales work order as k j ;

[0017]

[0018] In the formula, lx k represents the service type score of the kth after-sales work order. If the service type of the kth after-sales work order is product maintenance, the service type score is lx k The default value is 1. If the service type of the kth after-sales work order is product maintenance, the service type is lx k The default value is 2, β1 represents the weight of the service type, BXQ represents the warranty period, k j -gs k represents the time difference between the time when the after-sales work order is generated and the time when the work order is purchased, and β2 represents the weight of the ratio of the warranty period to the time difference. represents the frequency of product after-sales service corresponding to the kth after-sales work order, β3 represents the weight for the frequency of product after-sales service, DS represents the current time point, DS-k j represents the waiting time of the customer for the kth after-sales work order, β4 represents the weight of the waiting time for the customer for the after-sales work order, β1, β2, β3 and β4 are all constants, and β1+β2+β3+β4=1, Indicates that the urgency coefficient Jix of the kth after-sales work order is calculated according to the weights β1, β2, β3 and β4 k .

[0019] Preferably, the matching coefficient Pix calculation process is as follows:

[0020] According to the employee dataset, extract the management data of the e-th worker and mark the current work order load quantity of the e-th worker as df e , mark the response time of the e-th worker as xy e , mark the number of historical work orders processed by the e-th worker as cl e , mark the number of orders completed by the e-th staff member as ds e , mark the number of customer complaints of the e-th staff member as ks e , mark the length of service of the e-th staff member as e l ;

[0021] If the e-th staff member's job qualification is only able to handle pre-sales work orders,

[0022]

[0023] In the formula, BQD represents the standard value used to measure the current number of pre-sales work order loads, and ω1 represents the weight of the ratio of the standard value to the current number of pre-sales work order loads. represents the average response time of the e-th staff member, ω2 represents the weight for the average response time, ω3 represents the weight for the ratio of the number of completed orders to the length of service, and ω4 represents the weight for the ratio of the number of customer complaints to the number of completed orders. ω1, ω2, ω3, and ω4 are all constants, and ω1+ω2+ω3+ω4=1. Indicates that the matching coefficient Pix when the e-th staff member handles the pre-sales work order is calculated according to the weights of ω1, ω2, ω3 and ω4 e ;

[0024] If the e-th staff member's job qualification is only able to handle after-sales work orders,

[0025]

[0026] In the formula, BHD represents the standard value used to measure the current after-sales work order load quantity. Indicates the weight of the ratio of the standard value to the current after-sales work order load quantity. Represents the weight for the average response time, Indicates the weight of the ratio of the number of customer complaints to length of service. and are constants, and Indicates that and Weight, calculate the matching coefficient Pix when the e-th staff member handles the after-sales work order e .

[0027] Preferably, when the intention coefficient Yxx is lower than the intention threshold range YXY, it indicates that the customer's purchase intention is low, and the processing level of the pre-sales work order is level three; when the intention coefficient Yxx is included in the intention threshold range YXY, it indicates that the customer's purchase intention is medium, and the processing level of the pre-sales work order is level two; when the intention coefficient Yxx exceeds the intention threshold range YXY, it indicates that the customer's purchase intention is high, and the processing level of the pre-sales work order is level one.

[0028] Preferably, when the emergency coefficient Jix is lower than the emergency threshold range JIY, it indicates that the urgency of the product after-sales service is low, and the processing level of the after-sales work order is level three. When the emergency coefficient Jix is included in the emergency threshold range JIY, it indicates that the urgency of the product after-sales service is medium, and the processing level of the after-sales work order is level two. When the emergency coefficient Jix exceeds the emergency threshold range JIY, it indicates that the urgency of the product after-sales service is high, and the processing level of the after-sales work order is level one.

[0029] Preferably, the allocation monitoring unit classifies the staff into pre-sales service staff and after-sales service staff according to their work qualifications, and then generates a pre-sales matching list based on the matching coefficients Pix of the pre-sales service staff, arranged from high to low, and generates an after-sales matching list based on the matching coefficients Pix of the after-sales service staff, arranged from high to low. The work order with the highest priority is preferentially assigned to the staff with the highest matching coefficient Pix in the matching list. If the staff's response time exceeds two minutes, it will be reallocated to the next ranked staff member according to the matching list.

[0030] Compared with the prior art, the present invention provides a method and system for automatically allocating and monitoring work orders, which has the following beneficial effects:

[0031] 1. The present invention connects the database and terminal equipment through the data acquisition module network to obtain the management data of all pre-sales work orders, after-sales work orders and staff, and classifies them into pre-sales data sets, after-sales data sets and employee data sets. The intelligent management module analyzes the purchase intention of each pre-sales work order customer based on the pre-sales data set and generates a corresponding intention coefficient Yxx. Then, based on the after-sales data set, it analyzes the urgency of each after-sales work order and generates a corresponding urgency coefficient Jix. The weight coefficient supports dynamic adjustment. Enterprises can flexibly optimize the algorithm model according to actual business priorities to avoid misjudgment caused by a single indicator, identify work orders within the warranty period or with high-frequency service needs, and give priority to resolving urgent after-sales problems, reducing customer waiting time and improving service experience. The intelligent management module analyzes the work order processing capabilities of each employee based on the employee data set and generates a corresponding matching coefficient Pix to distinguish pre-sales or after-sales qualifications, avoid inefficient processing due to skill mismatch, give priority to employees with low customer complaint rates, reduce service risks, and make multi-dimensional evaluation load more balanced.

[0032] 2. The present invention sets the intention threshold range YXY and the emergency threshold range JIY through the intelligent management module, and then combines the intention coefficient Yxx and the emergency coefficient Jix to evaluate the processing level and priority of pre-sales work orders and after-sales work orders. The processing priority of the first-level pre-sales work order and the first-level after-sales work order is higher than that of the second-level pre-sales work order and the second-level after-sales work order, and the processing priority of the second-level pre-sales work order and the second-level after-sales work order is higher than that of the third-level pre-sales work order and the third-level after-sales work order. It supports differentiated processing of pre-sales work orders and after-sales work orders, adapts to the diversified business needs of enterprises, ensures priority allocation of high-priority work orders, avoids delays caused by human negligence, and intelligently The management module can classify staff into pre-sales service staff and after-sales service staff according to their work qualifications, and then generate a pre-sales matching list based on the matching coefficient Pix of the pre-sales service staff, arranged from high to low. Based on the matching coefficient Pix of the after-sales service staff, a post-sales matching list is generated based on the matching coefficient Pix of the after-sales service staff, arranged from high to low. The highest priority work order is assigned first to the staff with the highest matching coefficient Pix in the matching list. If the staff's response time exceeds two minutes, it will be reallocated to the next ranked staff according to the matching list, automatically matching the best processing personnel, reducing the time cost of manual allocation, and making dynamic allocation more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of the system of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0035] The traditional automatic work order allocation and monitoring method has the problem of a single allocation rule. Under high load conditions, when too many work orders are received, some employees will be overloaded with tasks, while other employees will be relatively idle. In addition, this method lacks the ability to analyze changes in pre-sales intentions and post-sales urgency in real time, and cannot adjust the allocation strategy in a timely manner. This unbalanced resource utilization will affect overall processing efficiency and user satisfaction. Therefore, a method and system for automatic work order allocation and monitoring is provided. Please refer to Figure 1 , a method and system for automatically allocating and monitoring work orders, including a data acquisition module and an intelligent management module;

[0036] The data acquisition module consists of an integrated data unit, a terminal data unit, and an employee data unit. The integrated data unit collects pre-sales data sets through a network connection to the database. The pre-sales data sets include the management data of all pre-sales work orders. The expression of the pre-sales data set is {Q1s 、Q2 s 、Q3 s ,...,Qn s}, Q1 s To Qn s Represents the management data of pre-sales work orders from the first to the nth. Pre-sales work order management data includes the customer's historical purchase quantity, purchase budget, number of consultations, and browsing time. s represents the time when the pre-sales work order was generated.

[0037] The terminal data unit collects the after-sales data set through the network connection terminal device. The after-sales data set includes the management data of all after-sales work orders. The expression of the after-sales data set is {H1 j 、H2 j 、H3 j 、...、Hm j}, H1 j To Hm j Represents the management data of the first to mth after-sales work orders. The after-sales work order management data includes the product's service type, purchase time, and historical after-sales service times. j represents the time when the after-sales work order was generated.

[0038] The employee data unit collects employee data sets through the network connection database. The employee data sets include the management data of all staff members. The expression of the employee data set is {R1 l 、R2 l 、R3 l 、...、Ry l}, R1 l To Ry l Represents the management data of the first to yth staff members. Staff management data includes the current work order load, response time, number of historical work orders processed, work qualifications, number of completed orders, and number of customer complaints. l represents the staff member's length of service.

[0039] The intelligent management module consists of a pre-sales evaluation unit, a post-sales evaluation unit, a matching analysis unit, and an allocation monitoring unit. The pre-sales evaluation unit analyzes the purchase intention of each pre-sales work order customer based on the pre-sales data set and generates a corresponding intention coefficient Yxx. The calculation process is as follows:

[0040] Based on the pre-sales data set, extract the management data of the i-th pre-sales work order and mark the historical purchase quantity of the i-th pre-sales work order as ls i , mark the purchase budget of the i-th pre-sales work order as ys i , mark the number of consultations for the i-th pre-sales work order as zc i , mark the browsing time of the i-th pre-sales work order as lc i , mark the generation time of the i-th pre-sales work order as i s ;

[0041]

[0042] In the formula, α1 represents the weight for the historical purchase quantity. If the historical purchase quantity of the customer corresponding to the i-th pre-sales work order is 0, then the historical purchase quantity ls i The default value is 1, KD represents the store average customer spending at the current time point, α2 represents the weight for the ratio of purchase budget to average customer spending, α3 represents the weight for the ratio of consultation times to browsing time, DS represents the current time point, DS-i s represents the waiting time of the customer for the i-th pre-sales work order, BSQ represents the standard value used to measure the waiting time of the customer for the pre-sales work order, α4 represents the weight of the ratio of the standard value to the waiting time of the customer for the pre-sales work order, α1, α2, α3 and α4 are all constants, and α1+α2+α3+α4=1, Indicates that the intention coefficient Yxx of the i-th pre-sales work order customer is calculated according to the weights α1, α2, α3 and α4 i The weight coefficient supports dynamic adjustment, and enterprises can flexibly optimize the algorithm model according to their actual business priorities to avoid misjudgments caused by a single indicator;

[0043] The after-sales evaluation unit analyzes the urgency of each after-sales work order based on the after-sales data set and generates a corresponding urgency coefficient Jix. The calculation process is as follows:

[0044] According to the after-sales data set, the management data of the kth after-sales work order is extracted, and the purchase time of the kth after-sales work order is marked as gs k , mark the historical after-sales service times of the kth after-sales work order as fw k , mark the generation time of the kth after-sales work order as k j ;

[0045]

[0046] In the formula, lx k represents the service type score of the kth after-sales work order. If the service type of the kth after-sales work order is product maintenance, the service type score is lx k The default value is 1. If the service type of the kth after-sales work order is product maintenance, the service type is lx k The default value is 2, β1 represents the weight of the service type, BXQ represents the warranty period, k j -gs k represents the time difference between the time when the after-sales work order is generated and the time when the work order is purchased, and β2 represents the weight of the ratio of the warranty period to the time difference. represents the frequency of product after-sales service corresponding to the kth after-sales work order, β3 represents the weight for the frequency of product after-sales service, DS represents the current time point, DS-k j represents the waiting time of the customer for the kth after-sales work order, β4 represents the weight of the waiting time for the customer for the after-sales work order, β1, β2, β3 and β4 are all constants, and β1+β2+β3+β4=1, Indicates that the urgency coefficient Jix of the kth after-sales work order is calculated according to the weights β1, β2, β3 and β4 k , identify work orders within the warranty period or with high-frequency service needs, prioritize solving urgent after-sales issues, reduce customer waiting time, and improve service experience;

[0047] The matching analysis unit analyzes each employee's work order processing capability based on the employee data set and generates a corresponding matching coefficient Pix. The calculation process is as follows:

[0048] According to the employee dataset, extract the management data of the e-th worker and mark the current work order load quantity of the e-th worker as df e , mark the response time of the e-th worker as xy e , mark the number of historical work orders processed by the e-th worker as cl e , mark the number of orders completed by the e-th staff member as ds e , mark the number of customer complaints of the e-th staff member as ks e , mark the length of service of the e-th staff member as e l ;

[0049] If the e-th staff member's job qualification is only able to handle pre-sales work orders,

[0050]

[0051] In the formula, BQD represents the standard value used to measure the current number of pre-sales work order loads, and ω1 represents the weight of the ratio of the standard value to the current number of pre-sales work order loads. represents the average response time of the e-th staff member, ω2 represents the weight for the average response time, ω3 represents the weight for the ratio of the number of completed orders to the length of service, and ω4 represents the weight for the ratio of the number of customer complaints to the number of completed orders. ω1, ω2, ω3, and ω4 are all constants, and ω1+ω2+ω3+ω4=1. Indicates that the matching coefficient Pix when the e-th staff member handles the pre-sales work order is calculated according to the weights of ω1, ω2, ω3 and ω4 e , distinguish pre-sales or after-sales qualifications to avoid inefficient processing due to skill mismatch;

[0052] If the e-th staff member's job qualification is only able to handle after-sales work orders,

[0053]

[0054] In the formula, BHD represents the standard value used to measure the current after-sales work order load quantity. Indicates the weight of the ratio of the standard value to the current after-sales work order load quantity. Represents the weight for the average response time, Indicates the weight of the ratio of the number of customer complaints to length of service. and are constants, and Indicates that and Weight, calculate the matching coefficient Pix when the e-th staff member handles the after-sales work order e , give priority to employees with low customer complaint rates, reduce service risks, and make multi-dimensional assessment load more balanced;

[0055] The monitoring unit is assigned to set the intention threshold range YXY and the emergency threshold range JIY, and then combined with the intention coefficient Yxx and the emergency coefficient Jix to evaluate the processing level and priority of pre-sales work orders and after-sales work orders. When the intention coefficient Yxx is lower than the intention threshold range YXY, it means that the customer's purchase intention is low, and the processing level of the pre-sales work order is level three. When the intention coefficient Yxx is included in the intention threshold range YXY, it means that the customer's purchase intention is medium, and the processing level of the pre-sales work order is level two. When the intention coefficient Yxx exceeds the intention threshold range YXY, it means that the customer's purchase intention is high, and the processing level of the pre-sales work order is level one. When the emergency coefficient Jix is lower than the emergency threshold range JIY, it means that the urgency of the product after-sales service is low, and the sales The processing level of after-sales work orders is level three. When the urgency coefficient Jix is within the urgency threshold range JIY, it indicates that the urgency of product after-sales service is medium, and the processing level of after-sales work orders is level two. When the urgency coefficient Jix exceeds the urgency threshold range JIY, it indicates that the urgency of product after-sales service is high, and the processing level of after-sales work orders is level one. The processing priority of level one pre-sales work orders and level one after-sales work orders is higher than that of level two pre-sales work orders and level two after-sales work orders, and the processing priority of level two pre-sales work orders and level two after-sales work orders is higher than that of level three pre-sales work orders and level three after-sales work orders. It supports differentiated processing of pre-sales work orders and after-sales work orders to meet the diversified business needs of enterprises, ensures that high-priority work orders are allocated first, and avoids delays caused by human negligence;

[0056] The allocation monitoring unit classifies the staff into pre-sales service personnel and after-sales service personnel according to their work qualifications, and then generates a pre-sales matching list based on the matching coefficient Pix of the pre-sales service personnel, arranged from high to low. The after-sales matching list is generated based on the matching coefficient Pix of the after-sales service personnel, arranged from high to low. The highest priority work order is first assigned to the staff with the highest matching coefficient Pix in the matching list. If the staff member's response time exceeds two minutes, it will be reallocated to the next ranked staff member according to the matching list, automatically matching the best processing personnel, reducing the time cost of manual allocation, and making dynamic allocation more efficient.

[0057] Example 1:

[0058] In this experiment, a customer with a historical purchase volume of 5 items was selected as the experimental subject. According to statistics, in March, this customer's purchase budget was 3,000 yuan, the number of consultations was 8, and the browsing time was 30 minutes. The pre-sales work order was generated on March 30th, and the current time point is April 1st. The calculation process of the intention coefficient Yxx of this pre-sales work order customer is as follows:

[0059]

[0060] In the formula, α1=0.2 represents the weight for the number of historical purchases, KD=500 yuan represents the average customer spending at the current time point, α2=0.3 represents the weight for the ratio of purchase budget to average customer spending, α3=0.25 represents the weight for the ratio of consultation times to browsing time, and DS-i s =1 represents the time difference between the current time point, April 1, and the time point when the work order was generated, March 30. The customer waiting time for this pre-sales work order is 1 day. BSQ=1 represents the standard value used to measure the waiting time of pre-sales work order customers. α4=0.25 represents the weight of the ratio of the standard value to the waiting time of pre-sales work order customers. α1, α2, α3, and α4 are all constants, and 0.2+0.3+0.25+0.25=1. According to the weights of α1, α2, α3, and α4, the intention coefficient Yxx of the pre-sales work order customer is calculated. i It is approximately 3.12. The intention threshold range YXY is set to 3 to 8. After judgment, the intention coefficient Yxx of the pre-sales work order customer i The customer's purchase intention is within the YXY threshold range, indicating that the customer has a medium purchase intention and the pre-sales ticket is handled at level 2.

[0061] Example 2:

[0062] In this experiment, we selected an after-sales work order with the product service type of repair as the experimental object. According to statistics, the product corresponding to this after-sales work order was purchased on June 1st, the warranty period is 24 months, the number of historical after-sales service visits is 3, the after-sales work order was generated on November 1st, and the current time point is November 15th. The calculation process of the urgency coefficient Jix of this after-sales work order is as follows:

[0063]

[0064] In the formula, the service type of the after-sales work order is product maintenance, and the service type is divided into lx k The default value is 2, β1 represents the weight of the service type, BXQ=24 represents the warranty period, k j -gs k =5 means that the time difference between the after-sales work order generation time of November 1 and the purchase time of June 1 is 5 months. β2 = 0.3 represents the weight for the ratio of warranty period to time difference. represents the frequency of after-sales service for the product corresponding to the after-sales work order, β3 = 0.3 represents the weight for the frequency of after-sales service for the product, DS-k j =14 represents the time difference between the current time point, November 15, and the time point when the after-sales work order was generated, November 1, that is, the customer waiting time for this after-sales work order is 14 days. β4 = 0.2 represents the weight for the customer waiting time of the after-sales work order. β1, β2, β3, and β4 are all constants, and 0.2 + 0.3 + 0.3 + 0.2 = 1. According to the weights of β1, β2, β3, and β4, the urgency coefficient Jix of this after-sales work order is calculated. k is 4.82, the emergency threshold range JIY is set to 2 to 4, and after judgment, the emergency coefficient Jix of the after-sales work order k The emergency threshold range JIY has been exceeded, indicating that the urgency of product after-sales service is high and the processing level of the after-sales work order is level one.

[0065] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method and system for automatically allocating and monitoring work orders, characterized by: Including data acquisition module and intelligent management module; The data acquisition module is composed of an integrated data unit, a terminal data unit, and an employee data unit. The integrated data unit collects a pre-sales data set via a network connection to a database. The pre-sales data set includes management data of all pre-sales work orders. The terminal data unit collects a post-sales data set via a network connection to a terminal device. The post-sales data set includes management data of all after-sales work orders. The employee data unit collects an employee data set via a network connection to a database. The employee data set includes management data of all staff members. The intelligent management module consists of a pre-sales evaluation unit, a post-sales evaluation unit, a matching analysis unit and an allocation monitoring unit. The pre-sales evaluation unit analyzes the purchase intention of each pre-sales work order customer based on the pre-sales data set and generates a corresponding intention coefficient Yxx. The post-sales evaluation unit analyzes the urgency of each after-sales work order based on the after-sales data set and generates a corresponding urgency coefficient Jix. The matching analysis unit analyzes the work order processing capability of each employee based on the employee data set and generates a corresponding matching coefficient Pix. The allocation monitoring unit sets an intention threshold range YXY and an urgency threshold range JIY, and then evaluates the processing level and priority of pre-sales work orders and after-sales work orders based on the intention coefficient Yxx and the urgency coefficient jix. The processing priority of first-level pre-sales work orders and first-level after-sales work orders is higher than that of second-level pre-sales work orders and second-level after-sales work orders, and the processing priority of second-level pre-sales work orders and second-level after-sales work orders is higher than that of third-level pre-sales work orders and third-level after-sales work orders. The allocation monitoring unit arranges and generates a matching list based on the employee data set and the matching coefficient Pix, and performs corresponding work order allocation.

2. A method and system for automatically allocating and monitoring work orders according to claim 1, characterized in that: The expression of the pre-sale data set is {Q1 s 、Q2 s 、Q3 s ,...,Qn s }, Q1 s To Qn s Represents the management data of the first to nth pre-sales work orders. The pre-sales work order management data includes the customer's historical purchase quantity, purchase budget, number of consultations, and browsing time. s represents the time when the pre-sales work order was generated.

3. A method and system for automatically allocating and monitoring work orders according to claim 2, characterized in that: The expression of the after-sales data set is {H1 j 、H2 j 、H3 j 、...、Hm j }, H1 j To Hm j Represents the management data of the first to mth after-sales work orders. The after-sales work order management data includes the product's service type, purchase time, and historical after-sales service times. j represents the time when the after-sales work order was generated.

4. A method and system for automatically allocating and monitoring work orders according to claim 3, characterized in that: The expression of the employee data set is {R1 l 、R2 l 、R3 l 、...、Ry l }, R1 l To Ry l Represents the management data of the first to y-th staff members. Staff management data includes the current work order load, response time, number of historical work orders processed, work qualifications, number of orders completed, and number of customer complaints. l represents the staff member's length of service.

5. A method and system for automatically allocating and monitoring work orders according to claim 4, characterized in that: The calculation process of the intention coefficient Yxx is as follows: Based on the pre-sales data set, extract the management data of the i-th pre-sales work order and mark the historical purchase quantity of the i-th pre-sales work order as ls i , mark the purchase budget of the i-th pre-sales work order as ys i , mark the number of consultations for the i-th pre-sales work order as zc i , mark the browsing time of the i-th pre-sales work order as lc i , mark the generation time of the i-th pre-sales work order as i s ; In the formula, α1 represents the weight for the number of historical purchases, KD represents the average customer spending at the current time point, α2 represents the weight for the ratio of purchase budget to average customer spending, α3 represents the weight for the ratio of consultation times to browsing time, DS represents the current time point, and DS-i s represents the waiting time of the customer for the i-th pre-sales work order, BSQ represents the standard value used to measure the waiting time of the customer for the pre-sales work order, α4 represents the weight of the ratio of the standard value to the waiting time of the customer for the pre-sales work order, α1, α2, α3 and α4 are all constants, and α1+α2+α3+α4=1, Indicates that the intention coefficient Yxx of the i-th pre-sales work order customer is calculated according to the weights α1, α2, α3 and α4 i .

6. A method and system for automatically allocating and monitoring work orders according to claim 5, characterized in that: The calculation process of the emergency coefficient Jix is as follows: According to the after-sales data set, the management data of the kth after-sales work order is extracted, and the purchase time of the kth after-sales work order is marked as gs k , mark the historical after-sales service times of the kth after-sales work order as fw k , mark the generation time of the kth after-sales work order as k j ; In the formula, lx k represents the service type score of the kth after-sales work order. If the service type of the kth after-sales work order is product maintenance, the service type score is lx k The default value is 1. If the service type of the kth after-sales work order is product maintenance, the service type is lx k The default value is 2, β1 represents the weight of the service type, BXQ represents the warranty period, k j -gs k represents the time difference between the time when the after-sales work order is generated and the time when the work order is purchased, and β2 represents the weight of the ratio of the warranty period to the time difference. represents the frequency of product after-sales service corresponding to the kth after-sales work order, β3 represents the weight for the frequency of product after-sales service, DS represents the current time point, DS-k j represents the waiting time of the customer for the kth after-sales work order, β4 represents the weight of the waiting time for the customer for the after-sales work order, β1, β2, β3 and β4 are all constants, and β1+β2+β3+β4=1, Indicates that the urgency coefficient Jix of the kth after-sales work order is calculated according to the weights β1, β2, β3 and β4 k .

7. A method and system for automatically allocating and monitoring work orders according to claim 6, characterized in that: The matching coefficient Pix calculation process is as follows: According to the employee dataset, extract the management data of the e-th worker and mark the current work order load quantity of the e-th worker as df e , mark the response time of the e-th worker as xy e , mark the number of historical work orders processed by the e-th worker as cl e , mark the number of orders completed by the e-th staff member as ds e , mark the number of customer complaints of the e-th staff member as ks e , mark the length of service of the e-th staff member as e l ; If the e-th staff member's job qualification is only able to handle pre-sales work orders, In the formula, BQD represents the standard value used to measure the current number of pre-sales work order loads, and ω1 represents the weight of the ratio of the standard value to the current number of pre-sales work order loads. represents the average response time of the e-th staff member, ω2 represents the weight for the average response time, ω3 represents the weight for the ratio of the number of completed orders to the length of service, and ω4 represents the weight for the ratio of the number of customer complaints to the number of completed orders. ω1, ω2, ω3, and ω4 are all constants, and ω1+ω2+ω3+ω4=1. Indicates that the matching coefficient Pix when the e-th staff member handles the pre-sales work order is calculated according to the weights of ω1, ω2, ω3 and ω4 e ; If the e-th staff member's job qualification is only able to handle after-sales work orders, In the formula, BHD represents the standard value used to measure the current after-sales work order load quantity. Indicates the weight of the ratio of the standard value to the current after-sales work order load quantity. Represents the weight for the average response time, Indicates the weight of the ratio of the number of customer complaints to length of service. and are constants, and Indicates that and Weight, calculate the matching coefficient Pix when the e-th staff member handles the after-sales work order e .

8. A method and system for automatically allocating and monitoring work orders according to claim 7, characterized in that: When the intention coefficient Yxx is lower than the intention threshold range YXY, it indicates that the customer's purchase intention is low, and the processing level of the pre-sales work order is level three. When the intention coefficient Yxx is included in the intention threshold range YXY, it indicates that the customer's purchase intention is medium, and the processing level of the pre-sales work order is level two. When the intention coefficient Yxx exceeds the intention threshold range YXY, it indicates that the customer's purchase intention is high, and the processing level of the pre-sales work order is level one.

9. A method and system for automatically allocating and monitoring work orders according to claim 8, characterized in that: When the emergency coefficient Jix is lower than the emergency threshold range JIY, it indicates that the urgency of the product after-sales service is low, and the processing level of the after-sales work order is level three. When the emergency coefficient Jix is included in the emergency threshold range JIY, it indicates that the urgency of the product after-sales service is medium, and the processing level of the after-sales work order is level two. When the emergency coefficient Jix exceeds the emergency threshold range JIY, it indicates that the urgency of the product after-sales service is high, and the processing level of the after-sales work order is level one.

10. A method and system for automatically allocating and monitoring work orders according to claim 9, characterized in that: The allocation monitoring unit classifies the staff into pre-sales service staff and after-sales service staff according to their work qualifications, and then generates a pre-sales matching list based on the matching coefficient Pix of the pre-sales service staff, arranged from high to low, and generates an after-sales matching list based on the matching coefficient Pix of the after-sales service staff, arranged from high to low. The work order with the highest priority is preferentially assigned to the staff with the highest matching coefficient Pix in the matching list. If the staff's response time exceeds two minutes, it will be reallocated to the next ranked staff member according to the matching list.

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