Customer service work order automatic monitoring and dispatching method and system

Abstract

The invention discloses a customer service work order automatic monitoring and dispatching method and system, and relates to the technical field of intelligent customer service systems, and the method comprises the steps: sensing a new work order, carrying out the automatic refreshing, and carrying out the notification; automatically classifying the work orders, and carrying out first division on priorities; and performing first analysis on the work order execution, and distributing the work order. According to the customer service work order automatic monitoring and dispatching method provided by the invention, through setting work order monitoring and real-time perception of a new work order, stable work order monitoring in all time periods is realized, delay caused by manual errors or fatigue is avoided, the timeliness and accuracy of work order response are improved, work orders are automatically classified and subjected to emergency degree division, and the work order automatic monitoring and dispatching efficiency is improved. Scientific sorting and hierarchical management of work order priorities are achieved, emergency tasks are responded preferentially, the customer experience is improved, a reasonable order dispatching strategy is made according to the working condition of the current operator on duty, the reasonability and efficiency of task execution are improved, and better effects are achieved in the aspects of timeliness, accuracy and efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent customer service systems, and specifically to a method for automatically monitoring and dispatching customer service work orders. Background Art

[0002] With the advent of the information age, various industries have an increasing demand for automation and intelligence of service processes. Especially in power supply companies, customer service work order systems have gradually become an important support tool for responding to user needs. The traditional work order processing model mainly relies on manual operations and distributes tasks through telephone, instant messaging tools, etc. Although this model has achieved electronic management of work orders in the early stages, there are obvious bottlenecks in efficiency and response speed. In recent years, with the rapid development of natural language processing (NLP), artificial intelligence (AI) and automation technology, a new generation of work order systems have begun to try to introduce automation technology to achieve task monitoring and distribution. Some studies have initially realized automatic refresh and classification processing of work order interfaces. However, these technologies still need to be improved in terms of accuracy, timeliness and intelligence, which provides potential for further optimizing user experience and enterprise management efficiency.

[0003] Although existing technologies have achieved automation in some aspects of work order processing, there are still significant limitations. First, most work order monitoring systems require on-duty personnel to manually refresh the work order interface through 24-hour shift operations, which not only wastes a lot of human resources, but also easily leads to response delays due to human negligence. Secondly, the existing work order distribution method lacks intelligent means. Most systems are unable to conduct a comprehensive analysis of factors such as the urgency of the work order, the workload of the processing personnel, and the geographical location, resulting in unreasonable allocation, which affects processing efficiency and personnel safety. In addition, some technologies have realized a simple automatic dispatching function, but have failed to fully consider the work order processing priority and real-time status, which is prone to task allocation errors. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: the existing service work order processing method has the problems of lack of scientificity and intelligence in task allocation, unstable response time, low efficiency, and how to achieve optimization problems of automatic monitoring and scientific dispatching.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for automatically monitoring and dispatching customer service work orders, including sensing new work orders, automatically refreshing and notifying; automatically classifying work orders and making a first division of priorities; performing a first analysis of work order execution and distributing work orders.

[0007] As a preferred solution of the method for automatically monitoring and dispatching customer service work orders described in the present invention, the sensing of new work orders includes discovering new work orders by monitoring.

[0008] As a preferred solution of the method for automatically monitoring and dispatching customer service work orders described in the present invention, the automatic refresh and notification includes automatically refreshing the work order interface and notifying the on-duty personnel.

[0009] As a preferred solution of the method for automatically monitoring and dispatching customer service work orders described in the present invention, the automatic classification of work orders includes automatically classifying new work orders that are monitored.

[0010] As a preferred solution of the method for automatically monitoring and dispatching customer service work orders described in the present invention, the first classification of priorities includes classifying the processing urgency of new work orders.

[0011] As a preferred solution of the method for automatically monitoring and dispatching customer service work orders described in the present invention, the first analysis of the work order execution includes analyzing the work order execution status and reasonably distributing the work orders.

[0012] As a preferred solution of the method for automatically monitoring and dispatching customer service work orders described in the present invention, the work order dispatching includes generating a pre-distribution list for confirmation by the on-duty personnel.

[0013] Another object of the present invention is to provide a customer service work order automatic monitoring and dispatching system, which can automatically classify new work orders through a classification module and perform a first-level classification of work order priorities, thereby solving the current problem of lack of accuracy and automation in work order classification.

[0014] As a preferred solution of the customer service work order automatic monitoring and dispatching system described in the present invention, it includes: a monitoring notification module, a classification module, and an analysis and distribution module; the monitoring notification module is used to sense new work orders, automatically refresh and notify; the classification module is used to automatically classify work orders and perform a first division of priorities; the analysis and distribution module is used to perform a first analysis of work order execution and distribute work orders.

[0015] A computer device includes a memory and a processor, wherein the memory stores a computer program, and wherein the processor executes the computer program to implement a method for automatically monitoring and dispatching customer service work orders.

[0016] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method for automatically monitoring and dispatching customer service work orders are implemented.

[0017] Beneficial effects of the present invention: The customer service work order automatic monitoring and dispatching method provided by the present invention realizes stable work order monitoring at all times by setting work order monitoring and real-time perception of new work orders, avoids delays caused by human errors or fatigue, improves the timeliness and accuracy of work order responses, automatically classifies work orders and divides them according to their urgency, realizes scientific sorting and hierarchical management of work order priorities, gives priority to responding to urgent tasks, improves customer experience, formulates reasonable dispatching strategies based on the current work situation of on-duty personnel, and improves the rationality and efficiency of task execution. The present invention achieves better results in terms of timeliness, accuracy and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0019] Figure 1 An overall flow chart of a method for automatically monitoring and dispatching customer service work orders provided for the first and second embodiments of the present invention.

[0020] Figure 2 An overall module diagram of a customer service work order automatic monitoring and dispatching system provided for the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0022] Example 1

[0023] Reference Figure 1 , is an embodiment of the present invention, and provides a method for automatically monitoring and dispatching customer service work orders, comprising:

[0024] S1: Sense new work orders, automatically refresh and notify.

[0025] Furthermore, sensing new work orders includes discovering new work orders through listening.

[0026] It should be noted that the monitoring frequency is an important parameter for work order monitoring. Setting a suitable monitoring frequency can balance system performance and response speed. By comprehensively analyzing the business needs of the power supply enterprise and the resource limitations of the marketing system, the monitoring frequency is set to 5 to 10 seconds to ensure that the system can capture emergency fault work orders in a timely manner and avoid occupying system resources due to too frequent refresh operations. The traditional manual monitoring mode requires on-duty personnel to refresh the system around the clock, which not only consumes a lot of human resources, but also easily leads to monitoring failure due to fatigue or negligence. Monitoring eliminates this bottleneck through automatic refresh, providing a stable foundation for subsequent work order processing;

[0027] Semantic analysis technology is the key to realizing intelligent monitoring of work order monitoring. Based on natural language processing (NLP) technology, the system can deeply analyze the text content of the work order and extract key information such as work order category and name. Data preprocessing is an important step in semantic analysis, including cleaning noise data, word segmentation and building a keyword library. For example, for the work order requirements of power supply companies, the keyword library may cover common terms such as fault reporting and complaints. Through matching rules, the system can accurately identify new work orders and avoid omissions caused by differences in text expression. Compared with the traditional model that relies on manual judgment, the semantic analysis function improves the accuracy of information processing.

[0028] Furthermore, automatic refresh and notification include automatically refreshing the work order interface and notifying the on-duty personnel.

[0029] It should be noted that when the listener of the work order monitoring finds a new work order, it automatically refreshes the work order interface of the marketing system and notifies the on-duty personnel at the same time. After the work order interface is automatically refreshed, the new work order is highlighted in different colors according to its urgency, with urgent dark red, general light blue and lighter light green, and is automatically sorted according to custom rules;

[0030] Notifications to on-duty personnel include system pop-ups, ringtone reminders, SMS notifications, etc. You can choose one or several notification methods at the same time, and customize notification rules according to actual needs. You can set the personnel or groups to be notified by work order type, time period (to distinguish business peak periods), shift personnel status, permission control, etc., to notify the corresponding personnel in a timely manner, while avoiding notification messages flooding the screen and causing interference.

[0031] Example 2

[0032] Reference Figure 1 , is an embodiment of the present invention, and provides a method for automatically monitoring and dispatching customer service work orders, comprising:

[0033] S2: Automatically classify work orders and make the first priority division.

[0034] Furthermore, automatically classifying work orders includes automatically classifying new work orders that are monitored.

[0035] It should be noted that automatic classification makes work order management more systematic and refined. According to the content and nature of the work order, it is divided into categories such as consultation, fault reporting, opinions and suggestions, and complaints. This classification not only facilitates subsequent processing, but also helps the system quickly identify the importance of the work order. For example, fault reporting work orders usually need to be handled first, while consultation work orders can be appropriately postponed. Compared with the traditional method that relies on manual reading and judgment, automatic classification improves efficiency and accuracy.

[0036] Furthermore, the first classification of priorities includes classifying the processing urgency of the new work orders.

[0037] In the embodiment of the present application, the first division is the division of the urgency of the work order. The default priority is set based on the rule base, fault>complaint>opinion and suggestion>consultation, and a multi-level weight mechanism is introduced to dynamically adjust the priority. For example, if a complaint work order involves a public safety issue, its priority may be higher than a general fault repair. The system can also perform secondary subdivision according to the specific content of the work order. For example, consultation issues involving disputes can be handled first, while daily consultations have a lower priority. Through this mechanism, the system can maximize the efficiency of task allocation under limited resources;

[0038] During business peaks or emergencies, the system can dynamically adjust weight parameters to ensure that emergency work orders are processed first. This flexibility makes the present invention more adaptable to complex and changing business environments in practical applications.

[0039] In an optional embodiment, the first division may also be implemented in other ways, such as through an intelligent prediction model based on machine learning. The machine learning model can be trained in combination with multi-dimensional features in historical work order data (such as work order category, customer type, submission time, involved area, etc.) to establish an intelligent model for predicting the urgency of work orders.

[0040] Construct a training dataset for the urgency of work orders. The dataset includes a large number of historical work orders and their processing results, and annotates their urgency levels such as high, medium, and low. During model training, the system extracts key features from the work order text, such as the description content submitted by the customer, keywords, timestamps, regional impact range, and work order processing history. Through natural language processing (NLP) technology, the model can deeply analyze the text content and extract core information related to the urgency level.

[0041] After training, the model can make real-time predictions for newly generated work orders in actual applications. When the system monitors a new work order, the model comprehensively analyzes the feature data of the work order and outputs the corresponding urgency label. This prediction method not only improves the intelligence level of urgency classification, but also adjusts the weight in real time according to different business scenarios, reflecting actual needs more accurately.

[0042] Compared with the division method based on rule base, the division method driven by machine learning can handle complex and changeable scenarios, reducing the workload of manually setting rules. At the same time, by continuously learning new work order data, the model can continuously optimize its own prediction ability to ensure the scientific and real-time division of work order urgency.

[0043] S3: Perform the first analysis on the work order execution and distribute the work order.

[0044] Furthermore, the first analysis of the work order execution includes analyzing the work order execution status and reasonably allocating the work orders.

[0045] In the embodiment of the present application, the first analysis is the work order execution analysis. The distribution of work orders cannot be simply randomly issued to a certain processing personnel. It needs to be reasonable and scientifically allocated. The work execution analysis unit comprehensively analyzes the current processing personnel's duty status, whether they are processing work orders, historical work order processing workload and other factors according to the rule base to achieve reasonable allocation and avoid situations such as no one to assign and excessive workload.

[0046] No orders will be assigned to non-duty personnel; a certain on-duty processing personnel is processing other work orders and is excluded from being assigned orders; a certain processing personnel has a heavy workload recently and his priority is lowered, so he will be assigned to other processing personnel with a smaller workload first. At the same time, regional factors and the principle of proximity are taken into consideration to avoid processing personnel having to travel far and delaying fault handling time.

[0047] In an optional embodiment, the first analysis may also be implemented in other ways, such as a comprehensive evaluation based on multiple indicators of real-time data, by collecting key data of processing personnel in real time through an interface, including the current number of tasks, remaining processing time, historical workload, completion rate, error rate, etc., and external parameters such as geographic location, working time period, and fatigue level may also be added as supplements. By inputting these multi-dimensional data into a weighted evaluation model, a comprehensive score can be generated to reflect the current status and execution ability of each processing personnel;

[0048] Weights can be set for different parameters, such as 0.4 for current task load, 0.3 for historical completion rate, 0.2 for geographic location relevance, and 0.1 for error rate. After comprehensive scoring, those with lower scores will be excluded from the candidate list, while those with higher scores will be recommended as priority candidates.

[0049] When a processing staff completes a task or its status changes (such as when a task is completed or a new task is added), the comprehensive scores of all candidates are automatically recalculated to ensure the timeliness and rationality of the dispatch decision. For emergency work orders under special circumstances, the system also supports manual adjustment of weight parameters, such as increasing the task load weight in an emergency situation and giving priority to selecting efficient personnel to handle it.

[0050] The analysis of work order execution is no longer limited to static rules, but has achieved real-time response to dynamic business scenarios. This analysis method based on multi-indicator evaluation is more comprehensive than the traditional single-factor judgment. It can not only optimize the efficiency and accuracy of work order allocation, but also effectively alleviate the work pressure of processing personnel and improve the fairness and scientificity of overall task management.

[0051] Furthermore, distributing the work order includes generating a pre-distribution list for confirmation by the duty personnel.

[0052] It should be noted that the system generates a pre-dispatch list and allows the on-duty personnel to make manual adjustments, which ensures the intelligence of the system allocation and retains the flexibility of manual intervention. When generating the pre-dispatch list, the system will also provide detailed classification and priority information of the work order for the on-duty personnel to confirm or adjust with one click. For special circumstances (such as a processing personnel taking temporary leave), the on-duty personnel can quickly modify the allocation plan to ensure the smooth execution of the task.

[0053] Example 3

[0054] An embodiment of the present invention provides a method for automatically monitoring and dispatching customer service work orders. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0055] The test environment was set as a simulated power supply enterprise customer service work order processing scenario. The experiment was divided into two groups, namely the prior art (traditional manual order dispatching system) and the method of the present invention (automatic order dispatching system). The experimental data were recorded and counted through the processing process of different batches of customer work orders for three consecutive days. Each group processed the same number of work orders, and the participants had the same work experience.

[0056] The existing technology uses manual monitoring of the work order status, with on-duty personnel taking turns on duty and manually assigning tasks through instant messaging tools. The on-duty personnel need to manually determine the priority of the work order and manually select the appropriate processing personnel for assignment.

[0057] The method of the present invention adopts the automatic dispatching system of the present invention to monitor the generation of new work orders in real time, automatically classify the work order types based on semantic analysis, sort them according to priority, and automatically dispatch tasks in combination with the real-time work status, historical workload and geographical location of the processing personnel. Before dispatching the order, the system will generate a task list for the on-duty personnel to confirm to ensure the rationality and accuracy of the dispatch results.

[0058] As shown in Table 1, the present invention reduces the average response time from 15-20 minutes to 2-3 minutes, and the optimization range reaches more than 85%. This is because the method of the present invention can realize real-time monitoring and automatically refresh the work order interface without manual intervention, thereby improving the response speed. The work order error rate of the method of the present invention is only 0.5%-1%, which is lower than 12%-15% of the prior art. Thanks to the system classifying and prioritizing work orders through semantic analysis technology, the subjective error caused by manual judgment is avoided. The processing efficiency of the method of the present invention reaches 25-30 work orders per hour, while the traditional system is only 8-10 work orders. The high efficiency is achieved from automatic classification and intelligent dispatching, which saves a lot of manual judgment and communication time. The customer satisfaction score shows that the method of the present invention better meets customer needs through accurate and efficient dispatching process and timely response. The task allocation balance of the method of the present invention reaches 90%-94%, while the traditional system is only 55%-60%. Reasonable task allocation reduces the workload of employees and reduces the risk of task completion. The resource saving rate of the method of the present invention reaches 38%-40%, which shows that the method of the present invention is more efficient in personnel scheduling and equipment utilization.

[0059] Table 1 Experimental data table

[0060]

[0061]

[0062] Example 4

[0063] Reference Figure 2 , which is an embodiment of the present invention, provides a customer service work order automatic monitoring and dispatching system, including: a monitoring notification module, a classification module, and an analysis and distribution module.

[0064] The monitoring and notification module is used to sense new work orders, automatically refresh and notify; the classification module is used to automatically classify work orders and make a first division of priorities; the analysis and distribution module is used to make a first analysis of work order execution and distribute work orders.

[0065] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0066] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0067] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0068] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0069] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for automatically monitoring and dispatching customer service work orders, characterized in that: include: Sense new work orders, automatically refresh and notify; Automatically classify work orders and prioritize them; Perform the first analysis on the work order execution and distribute the work order.

2. The method for automatically monitoring and dispatching customer service work orders according to claim 1, characterized in that: The sensing of the new work order includes discovering the new work order by monitoring.

3. The method for automatically monitoring and dispatching customer service work orders according to claim 2, characterized in that: The automatic refreshing and notification includes automatically refreshing the work order interface and notifying the on-duty personnel.

4. The method for automatically monitoring and dispatching customer service work orders according to claim 3, characterized in that: The automatic classification of work orders includes automatically classifying new work orders that are monitored.

5. The method for automatically monitoring and dispatching customer service work orders according to claim 4, characterized in that: The first classification of priorities includes classifying the new work orders according to their processing urgency.

6. The method for automatically monitoring and dispatching customer service work orders according to claim 5, characterized in that: The first analysis of the work order execution includes analyzing the work order execution status and reasonably allocating the work orders.

7. The method for automatically monitoring and dispatching customer service work orders according to claim 6, characterized in that: The distribution of the work order includes generating a pre-distribution list for confirmation by the on-duty personnel.

8. A system using the method for automatically monitoring and dispatching customer service work orders as claimed in any one of claims 1 to 7, characterized in that: It includes monitoring notification module, classification module, and analysis and distribution module; The monitoring notification module is used to sense new work orders, automatically refresh and notify; The classification module is used to automatically classify work orders and perform a first classification of priorities; The analysis and distribution module is used to perform a first analysis on the work order execution and distribute the work order.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for automatically monitoring and dispatching customer service work orders described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for automatically monitoring and dispatching customer service work orders described in any one of claims 1 to 7 are implemented.

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