Intelligent task allocation platform for service industry

Abstract

The invention provides an intelligent task allocation platform for the service industry. The intelligent task allocation platform comprises a task receiving module, a task allocation module and a task allocation module, the task analysis module is used for analyzing the task request and extracting task key elements; the task allocation module is used for automatically allocating tasks according to the task key elements and the current resource condition; the task monitoring module is used for monitoring the task execution state in real time and ensuring that the task is completed on time; the data storage module is used for storing task historical data and task allocation strategies; and the user interface module is used for providing an interactive interface between the user and the platform. Compared with the prior art, the task allocation platform has the following beneficial effects that the task allocation platform realizes efficient task allocation through the intelligent algorithm unit. Task requests and resource data are collected and analyzed, and a machine learning model is used for optimizing a task allocation strategy, so that timely completion of tasks and reasonable utilization of resources are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of task allocation, and in particular relates to an intelligent task allocation platform for the service industry. Background Art

[0002] Task allocation in the service industry mainly relies on manual scheduling, which has problems such as low efficiency, high error rate, and uneven task allocation. With the development of artificial intelligence and Internet of Things technologies, intelligent and automated task allocation platforms are gaining more and more attention and demand.

[0003] The main problem with conventional task allocation platforms is the inefficiency of task allocation, because such platforms often rely on manual or rule-driven allocation methods and cannot make optimization decisions in real time based on the actual needs of tasks and the dynamic changes of resources. Non-intelligent methods of task allocation, usually based on static rules or manual judgment, may lead to uneven task allocation, idle or overloaded resources. For example, some high-priority tasks may be assigned to personnel with lower processing capabilities due to manual misjudgment, delaying the completion of tasks. Task scheduling has poor flexibility and adaptability because manual or rule-driven allocation methods cannot dynamically adjust task priorities and resource allocation, resulting in low task processing efficiency.

[0004] Conventional approaches include using rule-based scheduling systems or manual intervention, but these methods have the disadvantage of being difficult to handle complex task environments and changing requirements, lacking flexibility and real-time performance. Rule-based systems are easily restricted by preset conditions and cannot adaptively adjust task allocation strategies, while manual intervention is time-consuming and labor-intensive, easily affected by subjective factors, and difficult to handle large-scale or complex task scenarios.

[0005] These traditional methods cannot effectively cope with the dynamic changes and complex demands in actual work, resulting in low efficiency in task allocation and insufficient resource utilization. Therefore, we hope to provide a new intelligent task allocation platform for the service industry to improve the accuracy of task allocation and service quality. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention aims to provide an intelligent task allocation platform for the service industry to solve the problems raised in the above-mentioned background technology.

[0007] The present invention is implemented through the following technical solutions: A service industry intelligent task allocation platform, comprising:

[0008] Task receiving module: used to receive task requests from customers;

[0009] Task analysis module: used to analyze task requests and extract key elements of tasks;

[0010] Task allocation module: used to automatically allocate tasks based on key elements of the task and current resource conditions;

[0011] Task monitoring module: used to monitor the task execution status in real time to ensure that the task is completed on time;

[0012] Data storage module: used to store task history data and task allocation strategies;

[0013] User interface module: used to provide an interactive interface between users and the platform.

[0014] As a preferred implementation, the task allocation module includes an intelligent algorithm unit for optimizing the task allocation strategy using a machine learning algorithm based on historical data and current resource conditions.

[0015] As a preferred implementation, the intelligent algorithm unit includes a data collection module, a data preprocessing module, a feature extraction module, a model training module, a task allocation algorithm module, a decision support module, and a feedback mechanism module.

[0016] As a preferred implementation, the output end of the task receiving module is connected to the input end of the task analysis module, the output end of the task analysis module is connected to the input end of the task assignment module, the output end of the task assignment module is connected to the input end of the task monitoring module, and the output end of the task monitoring module is connected to the input end of the data storage module and the user interface module.

[0017] As a preferred implementation, in the intelligent algorithm unit:

[0018] The data acquisition module is used to obtain relevant data from the task receiving module and the task monitoring module, including detailed information of the task request, current resource status, and historical task data;

[0019] The data preprocessing module is used to clean, transform and standardize the collected data to ensure the quality and consistency of the data;

[0020] The feature extraction module is used to extract useful features from the preprocessed data for subsequent algorithm modeling;

[0021] The model training module uses machine learning or optimization algorithms to train the task allocation model to improve the accuracy of the allocation strategy;

[0022] The task allocation algorithm module makes task allocation decisions based on the trained model and real-time data;

[0023] The decision support module generates specific task allocation suggestions based on the output of the task allocation algorithm module and feeds the results back to the task allocation module;

[0024] The feedback mechanism module collects feedback data during task execution for continuous optimization and adjustment of the model.

[0025] As a preferred implementation, the task analysis module includes:

[0026] Task parsing unit: parses the customer's task request and converts the request information into structured data for subsequent processing;

[0027] Natural Language Processing (NLP) unit: performs semantic analysis on customers’ natural language requests to extract the main requirements, categories, and priorities of tasks;

[0028] Keyword extraction unit: extracts key elements and keywords from task requests to quickly locate the core requirements of the task;

[0029] Task classification unit: Assign tasks to predefined categories based on extracted keywords and task features;

[0030] Priority assessment unit: Combines the task content and system resource status to assess the importance and urgency of the task to determine the priority of the task;

[0031] Associated resource identification unit: Analyzes the resource status in the current system and identifies the resources related to the task request as input conditions for task allocation.

[0032] After adopting the above technical solution, the beneficial effect of the present invention is that the task allocation platform realizes efficient task allocation through the intelligent algorithm unit. By collecting and analyzing task requests and resource data, the task allocation strategy is optimized using a machine learning model, ensuring the timely completion of tasks and the rational use of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0034] Figure 1 This is a system principle block diagram of a service industry intelligent task allocation platform of the present invention.

[0035] In the figure, 1. Task receiving module;

[0036] 2. Task analysis module;

[0037] 3. Task allocation module;

[0038] 4. Task monitoring module;

[0039] 5. Data storage module;

[0040] 6. User interface module. DETAILED DESCRIPTION

[0041] 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.

[0042] See also Figure 1 The present invention provides a technical solution: a service industry intelligent task allocation platform, comprising:

[0043] Task receiving module: used to receive task requests from customers;

[0044] Task analysis module: used to analyze task requests and extract key elements of tasks;

[0045] Task allocation module: used to automatically allocate tasks based on key elements of the task and current resource conditions;

[0046] Task monitoring module: used to monitor the task execution status in real time to ensure that the task is completed on time;

[0047] Data storage module: used to store task history data and task allocation strategies;

[0048] User interface module: used to provide an interactive interface between users and the platform.

[0049] The task allocation module includes an intelligent algorithm unit for optimizing the task allocation strategy using a machine learning algorithm based on historical data and current resource conditions.

[0050] The intelligent algorithm unit includes a data collection module, a data preprocessing module, a feature extraction module, a model training module, a task allocation algorithm module, a decision support module, and a feedback mechanism module.

[0051] The output end of the task receiving module is connected to the input end of the task analysis module, the output end of the task analysis module is connected to the input end of the task allocation module, the output end of the task allocation module is connected to the input end of the task monitoring module, and the output end of the task monitoring module is connected to the input end of the data storage module and the user interface module.

[0052] In the intelligent algorithm unit:

[0053] The data acquisition module is used to obtain relevant data from the task receiving module and the task monitoring module, including detailed information of the task request, current resource status, and historical task data;

[0054] The data preprocessing module is used to clean, transform and standardize the collected data to ensure the quality and consistency of the data;

[0055] The feature extraction module is used to extract useful features from the preprocessed data for subsequent algorithm modeling;

[0056] The model training module uses machine learning or optimization algorithms to train the task allocation model to improve the accuracy of the allocation strategy;

[0057] The task allocation algorithm module makes task allocation decisions based on the trained model and real-time data;

[0058] The decision support module generates specific task allocation suggestions based on the output of the task allocation algorithm module and feeds the results back to the task allocation module;

[0059] The feedback mechanism module collects feedback data during task execution for continuous optimization and adjustment of the model.

[0060] Example 1, as an example of a task allocation module of the present invention: Assume that a large cleaning service company uses an intelligent task allocation platform to manage the task allocation of its service team. The services provided by the company include office cleaning, carpet cleaning, window cleaning, etc., and the service areas are distributed in different parts of the city. In order to improve service efficiency and customer satisfaction, the company hopes to optimize the task allocation process through the intelligent task allocation platform.

[0061] Task receiving module: receives cleaning task requests submitted by customers through the application or website.

[0062] Task analysis module: Analyzes task requests and extracts key information.

[0063] Task allocation module: Use intelligent algorithm unit to allocate tasks.

[0064] Task monitoring module: monitor task execution status in real time.

[0065] Data storage module: stores historical data and task allocation strategies.

[0066] User interface module: the interface through which customers and service personnel interact with the platform.

[0067] 1. Task Receiving

[0068] The customer submitted the following task request in the application:

[0069] Task 1: Office cleaning, Location: Building A, City Center, Estimated time: 2 hours, Priority: High

[0070] Task 2: Carpet cleaning, Location: Office Building B in the city center, Estimated time: 1.5 hours, Priority: Medium

[0071] Task 3: Window cleaning, Location: District C, Estimated time: 1 hour, Priority: Low

[0072] 2. Task Analysis

[0073] The task analysis module receives these task requests and extracts the following key information:

[0074] Task 1: Cleaning type: Office, Location: Building A, City Center, Estimated time: 2 hours, Priority: High

[0075] Task 2: Cleaning type: Carpet, Location: Office Building B in the city center, Estimated time: 1.5 hours, Priority: Medium

[0076] Task 3: Cleaning type: Windows, Location: District C, Estimated time: 1 hour, Priority: Low

[0077] 3. Data Collection

[0078] Assume that the company has the following resources (service personnel) available:

[0079] Service Personnel A: Location: City Center A, Skill: Office Cleaning, Current Load: 0 Tasks Service Personnel B: Location: City Center B, Skill: Carpet Cleaning, Current Load: 1 Task

[0080] Service Personnel C: Location: Urban Area C, Skill: Window Washing, Current Load: 0 Tasks

[0081] 4. Data Preprocessing

[0082] The data preprocessing module cleans and standardizes the collected data:

[0083] Task data: Make sure each task has a clear location, duration, and priority.

[0084] Resource Data: Standardize service personnel’s skills and location data.

[0085] 5. Feature extraction

[0086] Extract features from preprocessed data:

[0087] Task 1: Priority: High, Location: City Center A, Estimated Time: 2 hours

[0088] Task 2: Priority: Medium, Location: City Center B, Estimated Time: 1.5 hours

[0089] Task 3: Priority: Low, Location: Urban Area C, Estimated Time: 1 hour

[0090] 6. Model Training

[0091] Based on historical task data and service personnel performance data, the task allocation model is trained. Assume that regression analysis is used to predict task completion time and classification algorithms are used to prioritize tasks.

[0092] 7. Task Assignment

[0093] The task allocation algorithm module makes allocation decisions based on the output of the model:

[0094] Task 1 (high priority): Assigned to service worker A because he is near Building A in the city center, has matching skills, and has a current load of 0.

[0095] Task 2 (medium priority): Since service worker B already has a task and his skills match the carpet cleaning task, it is assigned to service worker B.

[0096] Task 3 (low priority): Assigned to service worker C, who is near community C in the urban area, has skills matching the window washing task, and has a current load of 0.

[0097] 8. Decision support

[0098] The decision support module generates a task allocation plan:

[0099] Task 1: Service Personnel A (Building A, Downtown)

[0100] Task 2: Service Personnel B (Office Building B in the City Center)

[0101] Task 3: Service Personnel C (C District, Urban Area)

[0102] 9. Task Monitoring

[0103] The task monitoring module tracks the execution of tasks in real time. After the service personnel complete the task, the platform will record the actual completion time, task quality and other information.

[0104] 10. Feedback Mechanism

[0105] After the task is completed, customers and service personnel provide feedback through the user interface module. The feedback mechanism module passes this feedback data to the intelligent algorithm unit for continuous optimization and adjustment of the model.

[0106] In embodiment 2, the task analysis module includes:

[0107] Task parsing unit: parses the customer's task request and converts the request information into structured data for subsequent processing;

[0108] Natural Language Processing (NLP) unit: performs semantic analysis on customers’ natural language requests to extract the main requirements, categories, and priorities of tasks;

[0109] Keyword extraction unit: extracts key elements and keywords from task requests to quickly locate the core requirements of the task;

[0110] Task classification unit: Assign tasks to predefined categories based on extracted keywords and task features;

[0111] Priority assessment unit: Combines the task content and system resource status to assess the importance and urgency of the task to determine the priority of the task;

[0112] Associated resource identification unit: Analyzes the resource status in the current system and identifies the resources related to the task request as input conditions for task allocation.

[0113] As an embodiment of a task analysis module of the present invention: when the task receiving module receives a task request from a client, the request will be passed to the task analysis module, and the various units of the module work together to extract the key elements of the task according to the following steps:

[0114] Task parsing: The task parsing unit performs preliminary parsing of the text or data content of the request to ensure the structuring of the request content and remove noise or irrelevant information.

[0115] Semantic analysis: The NLP unit is used to perform semantic understanding on the natural language text of the task request, such as identifying the service type and target of the task.

[0116] Keyword extraction: The keyword extraction unit extracts key elements from task requests and core vocabulary in specific fields to facilitate subsequent classification and allocation.

[0117] Task classification: The extracted keywords are input into the task classification unit, and the tasks are classified into the specified categories based on machine learning or rule classification models.

[0118] Priority assessment: The priority assessment unit assesses the urgency and importance of the task, and generates task priority information based on the current system resource situation.

[0119] Associated resource identification: The associated resource identification unit scans the current resource status in the system, identifies the resources that can be used to perform this task, and provides a reference for the task allocation module.

[0120] Example: Customer Request: "Please arrange emergency repair service for VIP customers."

[0121] Task parsing unit: parses the request into text data and extracts the keywords "VIP customer", "urgent", and "maintenance service".

[0122] NLP unit: Identifies that the task request belongs to the “maintenance service” category and is marked as an “urgent” task.

[0123] Keyword extraction unit: extract "VIP customers", "urgent", and "maintenance services" from the text.

[0124] Task classification unit: Based on keywords, the tasks are classified into “maintenance category”.

[0125] Priority assessment unit: Combine the keywords "VIP customer" and "urgent" to set the task priority to high.

[0126] Associated resource identification unit: Searches the system for maintenance personnel and tools that are currently idle and suitable for performing the task, for decision-making by the task allocation module.

[0127] Finally, the task analysis module passes the extracted key elements of the task (task type: maintenance service, priority: high, customer type: VIP) to the task allocation module, which performs reasonable allocation based on the current resource situation.

[0128] Example 3, as an example of a service industry intelligent task allocation platform of the present invention:

[0129] Suppose that on a typical working day, the company receives a task request from a VIP customer for "air conditioning maintenance" service, which is required to be completed within two hours. The platform system will automatically complete the receipt, analysis, and allocation of task requests, and provide a visual task execution status on the user interface.

[0130] Detailed working process of each module of the system

[0131] Task receiving module

[0132] Task request reception: The customer submits a task request through the online platform or by phone. The task request content is "VIP customer urgently needs air conditioning repair service" and comes with a time limit description, requiring it to be completed within two hours.

[0133] Data entry: The task receiving module receives and structures the task request, converts it into system-readable data information, marks it as "air conditioning maintenance", and records the VIP customer identity and emergency time limit requirements.

[0134] Task transfer: The task receiving module passes the processed task data to the task analysis module to ensure that the request can be accurately parsed and subsequent allocation decisions can be made.

[0135] Task Analysis Module

[0136] Task parsing unit: After receiving the task request, the task parsing unit extracts the task content "air conditioning maintenance" and "urgent time limit" and preliminarily structures it. The parsing process removes irrelevant information to ensure that only the core requirements of the task are retained.

[0137] Natural Language Processing (NLP) Unit: The NLP unit performs semantic analysis on the request text for "air conditioning repair" and identifies its core intent, which is the need for urgent repair of the air conditioning equipment. The system marks the task as "high priority".

[0138] Keyword extraction unit: extracts keywords such as "air conditioning", "VIP", "maintenance", and "urgent", and further tags the task with "urgent" to facilitate subsequent task classification and priority assessment.

[0139] Task classification unit: The classification algorithm identifies the task as a "maintenance" task and specifically classifies it in the urgent task list. The classification unit divides the task into the "air conditioning maintenance" sub-category, making it easier for the system to find engineers with relevant skills.

[0140] Priority Assessment Unit: Based on the client status (VIP) and time limit requirement (within two hours) of the task, the task priority is set to high.

[0141] Associated resource identification unit: The system scans currently available resources in real time. By analyzing the current status of engineers, it identifies that Engineer A and Engineer C are idle and suitable for performing air conditioning maintenance tasks.

[0142] The final results of the analysis are: Task type: air conditioning maintenance; Priority: high; Customer type: VIP; Suitable resources: Engineers A and C. This key information is transmitted to the task allocation module.

[0143] Task Assignment Module

[0144] Intelligent Algorithm Unit: The intelligent algorithm unit combines historical data and current resource status to make allocation decisions through machine learning algorithms. The system queries historical data and finds that Engineer A performs well in air conditioning maintenance, with a completion rate of 98% in the past 50 related tasks and an average time of 1 hour and 45 minutes.

[0145] Task allocation algorithm module: Based on the above information, the system will prioritize the task to Engineer A to ensure that the task can be completed within two hours while meeting the high standards of VIP customers.

[0146] Decision support module: The decision support module generates a task assignment suggestion of "assign to engineer A" and feeds the decision result back to the task assignment module.

[0147] Feedback mechanism module: After the task starts, the system will continue to receive feedback data during the task execution for subsequent model optimization. After the task allocation is completed, the task is pushed to the task monitoring module.

[0148] Task monitoring module

[0149] Task tracking: The monitoring module tracks the task status in real time, records information about each step of the task (such as the time when Engineer A arrives at the customer's location, the progress of the repair process, etc.), and confirms that the engineer is performing the task as planned.

[0150] Early warning system: The monitoring module is equipped with an early warning system. If Engineer A encounters delays or obstacles during the repair task, the system will issue an early warning prompt. The early warning information will be pushed to the management interface so that timely intervention measures can be taken.

[0151] Real-time data collection: After each stage is completed, the monitoring module will send data to the data storage module to provide a reliable historical reference for the system. After the task is completed, the monitoring module will generate a task summary report.

[0152] Data storage module

[0153] Data collection and storage: All data generated during the task execution, including reception time, allocation strategy, resource usage, completion time, etc., are stored in the data storage module. The data storage module will automatically record the entire process of the task and form a historical record.

[0154] Data is used for training: The stored historical task data will be used as training data for the intelligent algorithm unit to optimize the algorithm for future task allocation to improve allocation accuracy and response efficiency.

[0155] Data backup: The data storage module is also backed up regularly to ensure the security and traceability of all task data.

[0156] User interface module

[0157] Visualization of task progress: The user interface module displays the real-time status of task execution, showing Engineer A’s task acceptance, estimated arrival time, current task progress, and completion status, ensuring that both customers and managers can understand the real-time progress of the task.

[0158] Historical data query: The user interface provides historical task data query function, based on which managers can analyze resource usage and allocation effects to optimize resource allocation.

[0159] Customer feedback function: After the task is completed, the user interface module supports customers to fill in task feedback. The customer's evaluation will become the basis for system optimization allocation and adjustment of task allocation algorithm.

[0160] Data flow example

[0161] The task request data received includes: customer identity (VIP), task content (air conditioning maintenance), priority (high), and time limit requirement (within two hours).

[0162] Analyze the extracted data: task type (air conditioning maintenance), task label (urgent), available resources (engineers A and C).

[0163] Task assignment data: The task assignment module assigns the task to experienced engineer A to increase the possibility of task completion.

[0164] Monitoring data records: task start time, engineer A's task progress, and task completion time.

[0165] Data storage: The system records all execution information of tasks, including allocation decisions, resource usage, task status and completion.

[0166] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A service industry intelligent task allocation platform, characterized in that: include: Task receiving module: used to receive task requests from customers; Task analysis module: used to analyze task requests and extract key elements of tasks; Task allocation module: used to automatically allocate tasks based on key elements of the task and current resource conditions; Task monitoring module: used to monitor the task execution status in real time to ensure that the task is completed on time; Data storage module: used to store task history data and task allocation strategies; User interface module: used to provide an interactive interface between users and the platform.

2. The intelligent task allocation platform for service industry as claimed in claim 1, characterized in that: The task allocation module includes an intelligent algorithm unit for optimizing the task allocation strategy using a machine learning algorithm based on historical data and current resource conditions.

3. The intelligent task allocation platform for service industry as claimed in claim 2, characterized in that: The intelligent algorithm unit includes a data collection module, a data preprocessing module, a feature extraction module, a model training module, a task allocation algorithm module, a decision support module, and a feedback mechanism module.

4. The intelligent task allocation platform for service industry as claimed in claim 3, characterized in that: The output end of the task receiving module is connected to the input end of the task analysis module, the output end of the task analysis module is connected to the input end of the task allocation module, the output end of the task allocation module is connected to the input end of the task monitoring module, and the output end of the task monitoring module is connected to the input end of the data storage module and the user interface module.

5. The intelligent task allocation platform for service industry as claimed in claim 3, characterized in that: In the intelligent algorithm unit: The data acquisition module is used to obtain relevant data from the task receiving module and the task monitoring module, including detailed information of the task request, current resource status, and historical task data; The data preprocessing module is used to clean, transform and standardize the collected data to ensure the quality and consistency of the data; The feature extraction module is used to extract useful features from the preprocessed data for subsequent algorithm modeling; The model training module uses machine learning or optimization algorithms to train the task allocation model to improve the accuracy of the allocation strategy; The task allocation algorithm module makes task allocation decisions based on the trained model and real-time data; The decision support module generates specific task allocation suggestions based on the output of the task allocation algorithm module and feeds the results back to the task allocation module; The feedback mechanism module collects feedback data during task execution for continuous optimization and adjustment of the model.

6. A service industry intelligent task allocation platform as claimed in claim 1 or 5, characterized in that: The task analysis module includes: Task parsing unit: parses the customer's task request and converts the request information into structured data for subsequent processing; Natural Language Processing (NLP) unit: performs semantic analysis on customers’ natural language requests to extract the main requirements, categories, and priorities of tasks; Keyword extraction unit: extracts key elements and keywords from task requests to quickly locate the core requirements of the task; Task classification unit: Assign tasks to predefined categories based on extracted keywords and task features; Priority assessment unit: Combines the task content and system resource status to assess the importance and urgency of the task to determine the priority of the task; Associated resource identification unit: Analyzes the resource status in the current system and identifies the resources related to the task request as input conditions for task allocation.

Images