Field staff management system

Abstract

The invention relates to an outworking management technology, and discloses an outworking personnel management system, which improves the operation convenience in the aspects of task allocation, personnel action management, work timeliness, outworking data extraction and the like and the accuracy of data acquisition during outworking personnel management. The system comprises a task allocation module used for automatically allocating tasks to outworkers meeting conditions by adopting a task allocation algorithm according to a preset task allocation rule by using an intelligent contract technology; the progress monitoring module is used for verifying and storing work progress information of field staff through an intelligent contract, generating a progress report and automatically triggering an early warning mechanism when the current task progress lags behind the expectation; the attendance management module is used for verifying and storing attendance data of the outworkers through an intelligent contract, generating an attendance report, and automatically triggering abnormal attendance reminding when the attendance data is abnormal; and the incentive mechanism module is used for automatically calculating and distributing corresponding point rewards to the outworkers according to a preset reward rule based on the work performance data of the outworkers.

Description

Technical Field

[0001] The present invention relates to field operation management technology, and particularly to a field staff management system. Background Art

[0002] Field staff are the key executors of an enterprise's market development, customer maintenance, and sales activities. Their core responsibilities include information collection, customer relationship management, market promotion, and the execution of sales tasks. Specifically, field staff need to organize and execute market research activities within a designated area according to the company's market development plan and sales targets, collect market information related to the company's products, such as competitor situations and changes in market demand, in order to support the company in formulating precise market strategies. In addition, they need to regularly report the market research situation to the company, promptly feedback market demand and customer feedback, and are also responsible for maintaining old customers, developing new customers, and establishing and maintaining good customer relationships. In terms of market promotion and sales, field staff need to formulate and execute sales plans, implement marketing activities, and complete sales tasks based on the market research results and the company's sales targets.

[0003] Traditional management methods for field staff mainly rely on manual operations and simple on-site or off-site management means. For example: on-site management records the work status by means of field staff signing in and manually filling out forms, but this method not only has low management efficiency but also is prone to inaccurate data; off-site management conducts work assignments through remote operations such as order sending and material sending, and it is difficult for managers to obtain the work status and location information of field staff in a timely manner, resulting in insufficient real-time and effectiveness of management.

[0004] In recent years, with the development of information technology, existing technical solutions have introduced location service technology and other information technology means on the basis of traditional management methods to improve the management efficiency and work quality of field staff. For example: using positioning technologies such as GPS to achieve real-time positioning of field staff, recording the movement trajectories of field staff through activity trajectory playback, using electronic fence technology to set specific areas to ensure that field staff work within the designated areas, solving the problem of difficult attendance for field staff in traditional attendance methods through mobile attendance, and realizing the rapid transmission of information and the real-time update of work data through message push and data upload functions.

[0005] Although these technical means have improved management efficiency to a certain extent, there are still many problems:

[0006] (1) In terms of task assignment, existing technologies require superiors to establish tasks one by one and issue them level by level, and finally field staff obtain the assigned tasks. The whole process is cumbersome and inefficient.

[0007] (2) In terms of personnel movement management, the existing technologies mainly achieve it through personnel positioning, clock-in or location submission, business trip and other data. However, these data are greatly affected by personnel subjectivity, and it is difficult to ensure their authenticity and reliability.

[0008] (3) In terms of work efficiency management, the existing technologies mainly judge work efficiency through personnel's active clock-in or information submission. There is also a problem that the authenticity of the data is difficult to guarantee.

[0009] (4) In terms of field work data extraction, for enterprises with a large number of field workers, the amount of field work data generated on a single day or during a certain period is huge. It is difficult for the existing technologies to achieve precise management and efficient extraction of field work data.

[0010] In addition, another important problem of the existing technologies is that the various data are not reasonably integrated, and it is difficult to associate the data. For example, task assignment data, work progress data, attendance data, performance data, etc. are scattered in different systems or modules, and it is difficult to conduct comprehensive analysis and collaborative management. This data island phenomenon makes it difficult for enterprises to conduct reasonable and effective management of field workers as a whole, thus affecting the refined management of market business and the scientific nature of decision-making.

[0011] In summary, the existing field worker management solutions have obvious deficiencies in aspects such as task assignment, personnel movement management, work efficiency monitoring, and field work data extraction, and it is difficult to meet the needs of enterprises for efficient and precise management of field workers. Summary of the Invention

[0012] The technical problem to be solved by the present invention is: to propose a field worker management system to improve the operational convenience and data acquisition accuracy in aspects such as task assignment, personnel movement management, work efficiency, and field work data extraction when managing field workers.

[0013] The technical solution adopted by the present invention to solve the above technical problems is:

[0014] A field worker management system, comprising:

[0015] A task assignment module, configured to use smart contract technology to automatically assign tasks to eligible field workers according to preset task assignment rules and using a task assignment algorithm;

[0016] A progress monitoring module, configured to verify and store the work progress information uploaded by field workers to the blockchain network through smart contracts, generate a progress report, and automatically trigger an early warning mechanism when the current task progress lags behind the expectation;

[0017] The attendance management module is used to verify and store the attendance data uploaded by field staff to the blockchain network through a smart contract, generate an attendance report, and automatically trigger an abnormal attendance reminder when the attendance data is abnormal;

[0018] The incentive mechanism module is used to automatically calculate and allocate corresponding integral rewards to field staff based on the task completion situation, work progress, attendance, customer satisfaction evaluation data, and team collaboration performance evaluation data of field staff according to preset reward rules, and provide an integral exchange mall for field staff to conduct integral exchanges.

[0019] Furthermore, the task assignment module is also used to record the historical data of task assignment, and through machine learning algorithms, learn from the historical data of task assignment to optimize the task assignment strategy.

[0020] Furthermore, the task assignment module automatically assigns tasks to eligible field staff according to preset task assignment rules, including: assigning tasks to matching field staff based on the nature of the task, the skill level, geographical location, workload, and historical performance factors of the field staff.

[0021] Furthermore, the task assignment module is also used to update and optimize the tasks of field staff, including:

[0022] Obtain the current location information and movement path of the field staff;

[0023] Predict the network stability trend of the field staff on the movement path according to the current location information and movement path;

[0024] Determine the priority of task assignment according to the network stability trend and the working time efficiency of the field staff;

[0025] Update and push the task information according to the priority;

[0026] Real-time monitor the work progress and network stability of field staff, and dynamically adjust the assignment strategy.

[0027] Furthermore, predicting the network stability trend of the field staff on the movement path according to the current location information and movement path includes: collecting historical network signal data on each path, including network signal strength, disconnection rate, and delay time in each time period; based on the collected historical network data, establishing a network stability prediction model using machine learning algorithms; based on the network stability prediction model, inputting the current location and time information of the field staff, and outputting the network stability prediction values of each time period of the field staff on the movement path.

[0028] Further, based on the network stability trend and the work efficiency of field staff, determine the priority of task allocation, including: calculating the average approval time and approval efficiency of field staff according to the approval time limit and approval quantity parameters of field staff; evaluating the work processing rate of field staff according to the approval time limit and approval efficiency; combining the work processing rate and the evaluation result of network stability impact to determine the priority of task allocation, and giving priority to processing the task information update requests of field staff who are about to enter the network unstable area.

[0029] Further, the system monitors the work progress and network stability of field staff in real time and dynamically adjusts the allocation strategy, including: if the existing work progress of field staff lags behind or the network stability is poor, the system automatically reallocates the tasks to be assigned to them to other field staff with better work progress and better network conditions.

[0030] Further, the incentive mechanism module also provides an integral leaderboard function to display field staff with top integral rankings.

[0031] The beneficial effects of the present invention are as follows:

[0032] (1) Through blockchain technology, the present invention decentralizes functions such as task allocation, attendance record, and progress monitoring, and utilizes the distributed ledger feature of blockchain to ensure the transparency and immutability of all data.

[0033] (2) The present invention introduces smart contracts to automatically match tasks for field staff according to preset rules, and at the same time uses machine learning to analyze historical data and continuously optimize the allocation strategy. This intelligent management method not only improves the efficiency of task allocation, but also dynamically adjusts the task priority according to the actual situation to ensure more reasonable work arrangements for field staff.

[0034] (3) Utilizing the traceability and immutability of blockchain, the present invention records and monitors the work progress, attendance, etc. uploaded by field staff in real time, thereby ensuring the authenticity and integrity of data. At the same time, the system also has functions of progress warning and abnormal attendance detection, which can timely discover problems and notify managers, thus improving the refined management level.

[0035] (4) The present invention sets up an incentive mechanism to give integral rewards based on the completion of work tasks, work progress, attendance, customer satisfaction evaluation data, team collaboration performance evaluation data, etc. of field staff. Field staff can exchange physical prizes, coupons or other benefits in the integral mall. This incentive mechanism can effectively improve the work enthusiasm of employees.

[0036] (5) The management system in the present invention adopts a modular design, splitting core functions such as task assignment, progress monitoring, attendance management, and incentive mechanisms into independent modules. Data interaction between modules is carried out through a blockchain network. This design not only reduces the complexity of the system but also improves the scalability and maintainability of the system. Description of the Drawings

[0037] Figure 1 It is a structural block diagram of the field staff management system in an embodiment of the present invention. Detailed Embodiment

[0038] The present invention aims to provide a field staff management system to improve the operational convenience and data acquisition accuracy in aspects such as task assignment, personnel movement management, work timeliness, and field data extraction during the management of field staff. Its core idea is: to achieve decentralization, intelligence, and data trustworthiness in field staff management through blockchain technology, thereby improving the efficiency and accuracy of task assignment, personnel movement management, work timeliness monitoring, and field data extraction. More specifically, it includes:

[0039] (1) A task assignment module is designed. Using smart contract technology, according to preset task assignment rules and conditions, tasks are automatically assigned to eligible field staff without manual intervention, improving efficiency and fairness. At the same time, machine learning is used to analyze the historical task data of the assignment, continuously optimizing the assignment strategy. In addition, the present invention also continuously updates and optimizes the tasks to be assigned according to network conditions, task completion status, etc. Implementing this dynamic adjustment mechanism can effectively handle emergencies such as network instability and lagging work progress, ensuring that tasks can proceed smoothly.

[0040] (2) A progress monitoring module is designed. Using the traceability and immutability of the blockchain, the work progress of field staff is recorded in real time. Smart contracts are used to verify and store this information, and at the same time, a progress report is generated for managers to view. When the task progress lags behind the expectation, an early warning mechanism is automatically triggered. This helps to ensure the authenticity and transparency of the work progress and also facilitates the enterprise to discover problems in a timely manner and take corresponding measures.

[0041] (3) An attendance management module is designed. Through blockchain technology, the attendance management of field staff is realized. Smart contracts are used to verify and store attendance data, and an attendance report is generated for enterprise use. When abnormal attendance data occurs, an anomaly detection mechanism is automatically triggered. Due to the immutability of the blockchain, the authenticity and accuracy of attendance data are ensured. Enterprises can conduct attendance statistics and analysis based on these data, providing a basis for salary payment, performance appraisal, etc.

[0042] (4) An incentive mechanism module is designed. Through the blockchain integral reward system, integral rewards are given according to factors such as the completion of field staff's work tasks, work progress, and attendance. The integral can be exchanged within the enterprise or used for other incentive measures, thus stimulating the work enthusiasm of field staff.

[0043] Based on the above modular design method of the field staff management system, each module is relatively independent and interrelated. This design method helps to reduce the complexity of the system, improve the scalability and maintainability of the system. At the same time, the data exchange and communication between each module are carried out through the blockchain network, ensuring the authenticity and integrity of the data.

[0044] Embodiment

[0045] The architecture of the field staff management system provided in this embodiment is shown in Figure 1 , which includes several parts such as a task assignment module, a progress monitoring module, an attendance management module, and an incentive mechanism module. Each module will be specifically described below.

[0046] I. Task Assignment Module

[0047] Based on blockchain technology, this module realizes the decentralized assignment of work tasks. Through smart contract technology, an enterprise can set task assignment rules and conditions. When specific conditions are met, the smart contract automatically assigns tasks to eligible field staff. This process requires no manual intervention, reducing management costs and improving the efficiency and fairness of task assignment at the same time.

[0048] A task assignment algorithm is embedded in the smart contract. This algorithm automatically matches and assigns tasks according to factors such as the nature of the task, the skills of field staff, and geographical location. At the same time, the smart contract can also record the historical data of task assignment for subsequent analysis and optimization. In addition, by introducing machine learning algorithms, the historical data of task assignment is learned to optimize the task assignment strategy and improve the efficiency and fairness of task assignment.

[0049] In an exemplary implementation scheme, the task assignment algorithm can adopt algorithms such as ant colony algorithm or genetic algorithm. By comprehensively evaluating task requirements and the characteristics of field staff, it realizes the precise matching of tasks and personnel. Among them, task requirements include the type of task (such as market research, customer visit, sales promotion, etc.), urgency, required skills and knowledge level, the specific location or area range of the task, and the time window for task execution. The characteristics of field staff include: skill level: the professional skills, experience level, and qualifications of field staff; geographical location: the current location of field staff; workload: the number of currently assigned tasks and work saturation; historical performance: the task completion rate, task completion quality, customer feedback, etc. of field staff.

[0050] When setting the matching rules, the following factors need to be considered: match the skill levels of field staff according to the nature of the tasks to ensure that the field staff have the capabilities required to complete the tasks; preferentially assign tasks based on the geographical location of the tasks to field staff who are closer, reducing commuting time and costs; consider the workload of the field staff to avoid over-assigning tasks and ensure the fairness of task allocation; refer to the historical performance of the field staff and preferentially assign tasks to those with excellent performance to improve the quality of task completion and customer satisfaction.

[0051] Accordingly, for each field staff, the matching degree score between the task requirements and the staff characteristics can be calculated through an algorithm. This matching degree score is the score obtained by weighted summation of the matching degrees between the characteristics of the field staff and the task requirements. Based on the sorting of the scores of all field staff, the field staff with the highest score is selected for the allocation of corresponding tasks.

[0052] During the process of task allocation, there are still some problems, such as the problem that field staff cannot obtain task information in a timely manner and the system has deficiencies in task information update and push. Specifically, it may be due to the fact that the area where the field staff are located has an unstable network environment, resulting in the inability to receive task information in a timely manner. Or network latency or interruption may also cause the failure or delay of task information push, resulting in untimely information update and affecting the work efficiency of field staff. Especially when dealing with a large number of task information update requests, the frequency of this problem is particularly prominent.

[0053] Therefore, the task allocation module in this embodiment is also used to update and optimize the tasks of field staff, specifically including:

[0054] (1) Obtain the current location information and moving path of the field staff;

[0055] Specifically, the real-time location information of the field staff can be obtained through the positioning function of the mobile device (such as GPS), and combined with historical task data and planned paths, to predict their future moving paths.

[0056] (2) According to the current location information and moving path, predict the network stability trend of the field staff on the moving path;

[0057] Specifically, the historical network signal data on each path can be collected, including the network signal strength, disconnection rate, and delay time in each time period; based on the collected historical network data, a machine learning algorithm is used to establish a network stability prediction model; based on the network stability prediction model, input the current location and time information of the field staff, and output the network stability prediction values of the field staff at each time period on the moving path.

[0058] (3) Determine the priority of task allocation according to the network stability trend and the work efficiency of the field staff;

[0059] Specifically, according to the approval time limit and approval quantity parameters of field staff, the average approval time limit and approval efficiency of field staff can be calculated; according to the said approval time limit and approval efficiency, the work processing rate of field staff can be evaluated; combining the said work processing rate and the evaluation result of network stability impact, the priority of task allocation can be determined, and the task information update request of field staff who are about to enter the network unstable area can be processed preferentially.

[0060] (4) Update and push the task information according to the said priority;

[0061] Specifically, the task information can be pushed to the mobile terminal of field staff. For high-priority tasks, ensure preferential push and remind field staff to process them in time.

[0062] (5) Monitor the work progress and network stability of field staff in real time, and dynamically adjust the allocation strategy;

[0063] For example: if the existing work progress of field staff lags behind or the network stability is poor, the system automatically reallocates the tasks to be assigned to them to other field staff with better work progress and better network conditions, so as to ensure that the tasks can be smoothly received and completed.

[0064] II. Progress Monitoring Module

[0065] This module utilizes the traceability and immutability of blockchain technology to record the work progress of field staff in real time. Field staff can upload the work progress information to the blockchain network through mobile devices or other means. The smart contract verifies and stores this information, and at the same time generates a progress report for managers to view. At the same time, this module provides a progress warning function. When the task progress lags behind the expectation, the smart contract automatically triggers the warning mechanism to remind managers and field staff to take measures in time.

[0066] III. Attendance Management Module

[0067] This module realizes the attendance management of field staff through blockchain technology. Field staff can record their attendance through mobile devices or other means and upload this information to the blockchain network. The smart contract verifies and stores the attendance data, and at the same time generates an attendance report for the enterprise to use. Due to the immutability of blockchain technology, this information cannot be changed once uploaded, thus ensuring the authenticity and accuracy of the attendance data. The enterprise can conduct attendance statistics and analysis based on these data to provide a basis for salary payment, performance appraisal, etc. In addition, this module also introduces an abnormal attendance detection function. When abnormal attendance data appears (such as frequent lateness, early leaving, etc.), the abnormal detection mechanism is automatically triggered to remind managers to verify and handle.

[0068] IV. Incentive Mechanism Module

[0069] This module conducts corresponding point rewards based on the completion of field staff's work tasks, work progress, attendance, customer satisfaction evaluation data, team collaboration performance evaluation data, etc. through a blockchain point reward system.

[0070] Completion of work tasks: task completion rate, task completion quality, etc.

[0071] Work progress: whether the task is completed on time, whether the progress meets expectations, etc.

[0072] Attendance: attendance rate, late arrival and early departure situations, etc.

[0073] Customer satisfaction: satisfaction scores or evaluations feedback by customers

[0074] Team collaboration and communication skills: records of team collaboration tools, evaluations by colleagues and superiors, etc.

[0075] These metrics are quantified and evaluated through the rules preset by the smart contract, and finally the point reward results are generated.

[0076] Points can be used to exchange for physical prizes in the enterprise's point redemption mall, or exchange for benefits such as coupons, or used for consumption payment and other scenarios to stimulate the work enthusiasm and creativity of field staff. At the same time, this module can introduce a point ranking function to display field staff with top points rankings to further motivate their work performance.

[0077] Based on the above architecture of the field management system, the field management process is as follows:

[0078] 1. Initialize the blockchain network: Set up the blockchain network and configure the corresponding smart contracts and rules to ensure the stable operation of the blockchain network and the security of data.

[0079] 2. Release work tasks: Managers release work tasks on the blockchain and set the corresponding task requirements and reward rules. The smart contract automatically assigns tasks to suitable field staff according to the preset rules and conditions.

[0080] 3. Progress monitoring and evaluation: During the process of field staff executing tasks, they upload progress information to the blockchain in real time. The smart contract automatically conducts progress tracking and evaluation based on the uploaded progress information and feeds back the evaluation results to managers.

[0081] 4. Attendance record and report generation: Record the attendance of field staff through blockchain technology. The smart contract automatically calculates the attendance results and generates an attendance report according to the preset attendance rules. The attendance report can be publicly viewed on the blockchain, ensuring the transparency and fairness of the attendance results.

[0082] 5. Performance Appraisal Index Setting

[0083] 5.1 Completion of Work Tasks: According to the work task assignment and completion of field staff, corresponding assessment indicators are set, such as task completion rate, task completion quality, etc.

[0084] 5.2 Work Progress: The work progress of field staff is recorded in real time through the progress monitoring module and used as one of the important indicators for performance appraisal. For cases of lagging progress, corresponding points deductions or penalties should be given.

[0085] 5.3 Attendance: Based on the data in the attendance management module, the attendance rate, number of late arrivals and early departures of field staff are assessed. For abnormal attendance situations, in-depth investigations should be carried out and corresponding handling measures should be taken.

[0086] 5.4 Customer Satisfaction: For positions where field staff have more contact with customers, customer satisfaction assessment indicators can be set, and the work performance of field staff is evaluated through customer feedback.

[0087] 5.5 Teamwork and Communication Skills: Evaluate the performance of field staff in teamwork, including communication skills and teamwork spirit with colleagues, superiors and customers.

[0088] 6. Set the Appraisal Cycle: According to the actual situation of the enterprise, set a reasonable performance appraisal cycle, such as monthly, quarterly or annual appraisal.

[0089] 7. Develop an Appraisal Plan: Before the start of each appraisal cycle, develop a detailed appraisal plan to clarify the appraisal purpose, indicators, methods and time nodes.

[0090] 8. Collect Appraisal Data: Collect the appraisal data of field staff through the attendance management module, progress monitoring module and other relevant data sources.

[0091] 9. Evaluation and Feedback: Based on the collected data, conduct a performance evaluation of field staff and give specific feedback. For employees with excellent performance, commendation and rewards should be given; for employees with poor performance, improvement suggestions should be put forward and help them improve their work performance.

[0092] 10. Point Rewards and Exchanges: According to factors such as the completion of work tasks, progress execution and attendance of field staff, corresponding point rewards are given. Field staff can exchange physical prizes, coupons and other benefits through the blockchain point reward system, or use points for consumption payment and other scenarios.

[0093] Finally, it should be noted that the above embodiments are only preferred embodiments and are not intended to limit the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the spirit and scope of the present invention as defined by the claims, several modifications, equivalent substitutions, improvements, etc. should all be included within the protection scope of the present invention.

Claims

1. A field staff management system, characterized in that, Including: A task assignment module, which uses smart contract technology to automatically assign tasks to eligible field personnel according to preset task assignment rules and a task assignment algorithm; A progress monitoring module, which verifies and stores the work progress information uploaded by field personnel to the blockchain network through a smart contract, generates a progress report, and automatically triggers an early warning mechanism when the current task progress lags behind the expectation; An attendance management module, which verifies and stores the attendance data uploaded by field personnel to the blockchain network through a smart contract, generates an attendance report, and automatically triggers an abnormal attendance reminder when the attendance data is abnormal; An incentive mechanism module, which automatically calculates and assigns corresponding integral rewards to field personnel based on the task completion situation, work progress, attendance situation, customer satisfaction evaluation data, and team collaboration performance evaluation data of field personnel according to preset reward rules, and provides an integral exchange mall for field personnel to conduct integral exchanges.

2. The field personnel management system according to claim 1, wherein The task assignment module is further configured to record task assignment historical data and optimize the task assignment strategy by learning the task assignment historical data through a machine learning algorithm.

3. The field personnel management system according to claim 1, wherein The task assignment module automatically assigns tasks to eligible field personnel according to preset task assignment rules and a task assignment algorithm, including: assigning tasks to matching field personnel according to the nature of the task, the skill level, geographical location, workload, and historical performance factors of the field personnel.

4. The field personnel management system according to claim 1, wherein The task assignment module is further configured to update and optimize the tasks of field personnel, including: Obtaining the current location information and movement path of the field personnel; Predicting the network stability trend of the field personnel on the movement path according to the current location information and movement path; Determining the priority of task assignment according to the network stability trend and the work timeliness of the field personnel; Updating and pushing the task information according to the priority; Real-time monitoring the work progress and network stability of field personnel, and dynamically adjusting the assignment strategy.

5. The field personnel management system according to claim 4, wherein The predicting the network stability trend of the field personnel on the movement path according to the current location information and movement path includes: collecting historical network signal data on each path, including network signal strength, disconnection rate, and delay time in each time period; establishing a network stability prediction model by using a machine learning algorithm based on the collected historical network data; and inputting the current location and time information of the field personnel into the network stability prediction model to output the network stability prediction values of the field personnel at each time period on the movement path.

6. The field personnel management system according to claim 4, wherein Determine the priority of task assignment according to the network stability trend and the work efficiency of field personnel, including: calculate the average approval time limit and approval efficiency of field personnel according to the approval time limit and approval quantity parameters of field personnel; evaluate the work processing rate of field personnel according to the approval time limit and approval efficiency; combine the work processing rate and the evaluation result of network stability impact to determine the priority of task assignment, and preferentially process the task information update requests of field personnel who are about to enter the network unstable area.

7. A field personnel management system according to claim 4, wherein The real-time monitoring of the work progress and network stability of field personnel and the dynamic adjustment of the allocation strategy include: if the existing work progress of the field personnel lags behind or the network stability is poor, the system automatically reallocates the tasks to be assigned to them to other field personnel with better work progress and better network conditions.

8. A field personnel management system according to any one of claims 1-7, wherein The incentive mechanism module also provides an integral ranking list function to display the field personnel with top integral rankings.

Images