Nurse task management system capable of dynamically adjusting working hours

Abstract

The invention relates to the technical field of scheduling optimization scheduling, in particular to a nurse task management system for dynamic man-hour adjustment, which comprises a man-hour extension identification module, a task time period matching module, a task transboundary splitting module, a scheduling structure reconstruction module and a management execution coverage module. In the task allocation process, newly added available time periods are identified based on actual off-duty time, adaptive tasks are accurately screened and dynamically inserted in combination with task duration and starting and ending time, cross-period tasks are proportionally split and redistributed in combination with task time boundaries and handover buffer limits, and a site-by-site disassembly and reconstruction mechanism is introduced. The dispatching granularity and response flexibility are effectively improved, nursing task specifications and time continuity are considered in the task and time period matching process, task undertaking reasonability and scheduling connection accuracy are achieved, integrated linkage is achieved, the dispatching bearing capacity of sudden overtime situations is enhanced, and the nurse resource configuration efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of shift optimization and scheduling, and in particular to a nurse task management system with dynamic working hour adjustment. Background Art

[0002] The field of scheduling optimization and dispatching technology involves the orderly and efficient time allocation and task configuration of human resources, with the aim of maximizing resource utilization and balancing workloads while meeting service needs. It is widely used in medical, manufacturing, service and other industries, and is particularly important in environments with tight resources and frequently changing work paces. Among them, the nurse task management system refers to a management method for manually or semi-automatically allocating nursing staff tasks in medical care scenarios. It targets overlapping tasks or uneven resource utilization caused by temporary changes in tasks or dynamic adjustments in working hours for nurses in different shifts. Scheduling is usually based on manual adjustments of fixed schedules or a spreadsheet program with preset rules, and relies on manual adjustments of task allocation based on nursing levels and patient needs to cope with dynamic changes.

[0003] The existing nurse task management process mainly relies on fixed schedules and manual adjustment strategies to handle changes in nursing task scheduling. In the event of sudden overtime or task delays, it lacks the ability to automatically identify newly available time periods, resulting in the inability to adjust the original task list in real time, which in turn causes task conflicts or resource vacancies. In terms of shift handovers and task splitting, it only makes manual judgments based on nursing levels, and lacks systematic analysis and quantitative processing of task time structures, resulting in unreasonable cross-shift task arrangements or incomplete splitting. In the process of task insertion, a matching mechanism for continuous time points is not established, and task adaptation is prone to unclear boundaries or time period misalignment, which affects the overall scheduling continuity and nursing process stability. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a nurse task management system with dynamic working hours adjustment.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: A nurse task management system with dynamic working hours adjustment includes: The work time extension recognition module obtains the actual off-duty time in the nurse's sign-in record, combines it with the corresponding planned off-duty time in the schedule, calculates the difference, and filters the data items with a difference greater than zero. It then splits the data into a set of continuous time points to obtain the number of newly added work time slots. The task period matching module reads the planned start time and standard nursing duration of the task to be scheduled based on the number of time period sites in each of the newly added work period sites, filters out tasks whose start and end times fall into any of the newly added time period sites, and statistically generates the newly added time period task adaptation data; The task cross-boundary splitting module retrieves the start and end time of the current nurse's scheduled task based on the nurse handover buffer time. If the task end time exceeds the handover buffer time, the task is split into a retained segment and a split segment based on the standard nursing duration, and the split segment tasks are extracted as tasks to be scheduled, generating the total number of new tasks after cross-boundary splitting; The scheduling structure reconstruction module calculates whether the interval segment can accommodate the corresponding task based on the newly added time period task adaptation data and the total amount of new tasks after cross-border splitting according to the standard nursing time, synchronously updates the nurse's corresponding task start and end schedule, and generates a dynamic scheduling matching record.

[0006] As a further solution of the present invention, the number of newly added work time period sites includes a time interval set, a continuous time point set, and a site number distribution; the newly added time period task adaptation data includes a task matching number, a task adaptation ratio, and a matching time period; the total amount of new tasks after the cross-border split includes the number of retained segment tasks, the number of split segment tasks, and the split task identifier; the scheduling matching record after dynamic plug-in includes the task rescheduling order, plug-in time period, and nurse task update table.

[0007] As a further solution of the present invention, the working time extension identification module includes: The off-duty time extraction submodule obtains the off-duty time of each sign-in record based on the actual off-duty time in the nurse sign-in record, associates it with the corresponding planned off-duty time in the schedule, extracts the minute-level difference data between the two time information, and generates the off-duty time difference interval; The time difference screening submodule performs conditional judgment on the time difference based on the off-duty time difference interval, retains the time difference data items that are greater than zero, and generates a start and end time set of the extended segment in combination with the corresponding planned off-duty time to obtain a new working time interval set; The time period decomposition submodule divides each group of time intervals into fixed granularity based on the newly added working time interval set, extracts all continuous time points covered in each interval, summarizes the number of continuous time points generated by each segment, and obtains the number of newly added working time period points.

[0008] As a further solution of the present invention, the task period matching module includes: The task information reading submodule obtains the data of each time period location in the number of newly added working time period locations, reads the information of the tasks to be scheduled in the nurse task pool, extracts the planned start time and standard nursing duration of the tasks, calculates the start and end time intervals of each task, and generates a set of time intervals of tasks to be matched; The task time comparison submodule matches the start time and end time of each task based on the set of time intervals of the tasks to be matched, combined with the time period data of each location in the newly added work period location number, and calculates the matching score of the newly added task. If it is less than the matching success benchmark value, it is marked as a matching task, and the matching task set is obtained by statistics; The adaptation result statistics submodule performs task volume statistics by nurse dimension and date dimension based on the task set marked as successfully matched in the matching task set, extracts the statistical total of tasks corresponding to each newly added time period, and integrates the task adaptation data for the newly added time period.

[0009] As a further solution of the present invention, the cross-border task splitting module includes: The task time judgment submodule reads the start and end times of all tasks recorded in the current nurse's daily work schedule based on the nurse's shift handover buffer time, determines whether the end time of each task is later than the nurse's actual off-duty time minus the shift handover buffer time, records it as a split task, and obtains a list of task markers that need to be split; The task time splitting submodule determines the planned start time, end time and nursing duration of each task based on the list of task tags to be split, compares the time period within the task interval with the nurse's dispatchable time period, confirms the split point position, demarcates the first half of the task interval as the retained segment and the second half as the split segment, and obtains the task split segment time data set; The split task extraction submodule generates a new task code by uniformly numbering the split segment tasks according to the split start time and split end time information of each record in the task split segment time data set, combined with the corresponding nurse number and the original task code, and records the corresponding task start and end information, task responsible nurse and task content to obtain the total amount of new tasks after cross-border splitting.

[0010] As a further solution of the present invention, the shift structure reconstruction module includes: The idle segment identification submodule extracts the task end time and the start time of the next task of each nurse in chronological order based on the newly added time period task adaptation data and the total amount of new tasks after the cross-border split. It judges the interval between the two time nodes to confirm whether there is a time period with no assigned tasks, constructs data structure entries in a unified format, and establishes a nurse idle time period dataset; The task insertion judgment submodule is based on the nurse idle time period dataset and the information of the tasks to be scheduled in the total amount of new tasks after the cross-border splitting, and determines whether it can be completely embedded in the idle time period of any nurse, confirms the combination relationship between the successfully matched tasks and the idle segments, and obtains the task insertion matching degree data table; The task start and end synchronization submodule processes the corresponding task information one by one according to the matching success relationship items recorded in the task matching data table, obtains the start and end time of the idle time period to be inserted into the task, inserts it into the corresponding nurse task list, and updates the task sequence number and time node in the scheduling record to obtain the scheduling matching record after dynamic matching.

[0011] As a further embodiment of the present invention, the system further comprises: The management execution coverage module reads the nurse number, task number, task start and end time in the scheduling record and the actual check-in time in the check-in record based on the dynamic matching schedule record, writes the task allocation content into the electronic nursing record, summarizes the daily nurse task dynamic change data entries, and establishes a dynamic management record of nurse tasks; The nurse task dynamic management record includes task allocation log, allocation execution information, and daily dynamic entries.

[0012] As a further solution of the present invention, the management execution coverage module includes: The task allocation and extraction submodule reads the task information in sequence based on the dynamic matching post-shift matching record, associates and matches the task number with the nurse's check-in number and check-in time, constructs a data table including the shift information and check-in information structure, and obtains the task check-in matching data table; The record synchronization writing submodule processes each data entry based on all task data entries in the task check-in matching data table, synchronously records the time difference between the task scheduling time and the check-in time, forms a nursing data record with structured entries and task execution status parameters, and establishes a task execution status identification data set; The dynamic data collection submodule classifies and organizes the task execution status of each nurse according to the status information of all task records in the task execution status identification data set, establishes a data mapping of the time series dimension after statistics, and obtains the dynamic management record of nurse tasks.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, in the process of task allocation, newly added available time periods are identified based on the actual off-duty time, and adapted tasks are accurately screened and dynamically inserted in combination with the task duration and start and end time. The cross-time period tasks are proportionally split and redistributed in combination with the task time boundary and the handover buffer limit. A site-based disassembly and reconstruction mechanism is introduced to effectively improve the allocation granularity and response flexibility. In the process of matching tasks and time periods, nursing task specifications and time continuity are considered to achieve task acceptance rationality and scheduling connection accuracy. In the multi-task allocation and scheduling reconstruction links, task time records and execution result data are synchronously updated to achieve integrated linkage of task adaptation, cross-segment disassembly, task reconstruction, and data landing, thereby enhancing the scheduling carrying capacity for sudden overtime situations and improving the allocation efficiency of nurse resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the working time extension identification module of the present invention; Figure 3 This is a flow chart of the task period matching module of the present invention; Figure 4 This is a flowchart of the cross-border splitting module of the present invention; Figure 5 This is a flow chart of the scheduling structure reconstruction module of the present invention; Figure 6 The flow chart of the module covering the management execution of the present invention is shown. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0017] See also Figure 1 , a nurse task management system with dynamic working hours adjustment includes: The work time extension recognition module obtains the actual off-duty time from the nurse's sign-in record, combines it with the corresponding planned off-duty time in the schedule, calculates the difference between the two in minutes, filters out data items with a difference greater than zero, and converts the difference into a time interval. The module then combines the planned off-duty time to form a start and end segment, which is then split into a set of continuous time points at a granularity of 15 minutes to obtain the number of newly added work time slots. The task time period matching module reads the planned start time and standard nursing duration (in accordance with the Nursing Operation Standards) of the tasks to be scheduled in the nurse task pool based on the number of time period locations in the newly added work time period locations. It compares the start and end times with the newly added time period locations, filters out tasks whose start and end times fall within any of the newly added time period locations, counts the total number of successfully matched tasks, and generates the newly added time period task adaptation data; The task cross-boundary splitting module retrieves the start and end times of the current nurse's scheduled task based on the handover buffer time recorded in advance of the nurse's actual off-duty time (set at 15 minutes according to the "Nursing Handover Specifications"), and determines whether the task end time exceeds the handover buffer time. If so, the task is divided into two segments based on the time ratio according to the standard nursing duration. The number of tasks in the retained segment and the split segment are calculated respectively, and the split segment tasks are extracted as the tasks to be scheduled, generating the total number of new tasks after cross-boundary splitting; The scheduling structure reconstruction module reads the task end time and next task start time of all nurses in the current schedule according to the newly added time period task adaptation data and the total number of new tasks after cross-border splitting. It calculates whether the interval segment can accommodate the corresponding task according to the standard nursing duration. If it can, it binds the task to the idle segment and synchronously updates the start and end timetable of the nurse's corresponding task to generate a scheduling matching record after dynamic insertion. The management execution coverage module is based on the dynamic scheduling matching record, reads the nurse number, task number, task start and end time in the scheduling record and the actual check-in time in the check-in record, and writes the task allocation content into the electronic nursing record (implementing the "Medical Software Quality Requirements"), summarizes the daily dynamic change data entries of nurse tasks, and establishes dynamic management records of nurse tasks.

[0018] The number of newly added work time period sites includes the time interval set, the continuous time point set, and the site number distribution. The newly added time period task adaptation data includes the task matching number, the task adaptation ratio, and the matching time period. The total number of new tasks after cross-border splitting includes the number of retained segment tasks, the number of split segment tasks, and the split task identification. The scheduling matching records after dynamic plug-in include the task rescheduling order, plug-in time period, and nurse task update table. The dynamic management records of nurse tasks include task allocation logs, allocation execution information, and daily dynamic entries.

[0019] See also Figure 2 , the working time extension recognition module includes: The off-duty time extraction submodule obtains the off-duty time of each sign-in record based on the actual off-duty time in the nurse sign-in record, associates it with the corresponding planned off-duty time in the schedule, extracts the minute-level difference data between the two time information, and generates the off-duty time difference interval; Based on the correspondence between the actual off-duty time in the nurse's sign-in record and the planned off-duty time in the schedule, we first need to extract all the sign-in details of each nurse on a certain day from the sign-in system, and obtain the last sign-out time of that day. For example, the last sign-out time of nurse A on July 12, 2024 was 18:47, which is converted into minutes as 18×60+47=1127 minutes. This value is used as her actual off-duty time; then extract the corresponding schedule information of the nurse on that day. For example, if the schedule time is 08:00-18:00, the corresponding planned off-duty time is 18:00, which is converted to 18×60=1080 minutes. The two data are subtracted to obtain the actual off-duty time relative to the planned off-duty time. The time offset is 1127-1080 = 47 minutes. To ensure data accuracy and validity, a maximum allowable offset threshold is set to exclude invalid data caused by abnormalities or misoperations. This threshold is based on historical statistics. After analyzing 872 records from Hospital A over a three-month period, it was found that only 6.3% of the records had offsets exceeding 180 minutes, while 90% were within 180 minutes. Therefore, 180 minutes is set as the upper limit of the tolerable offset. For example, if a nurse checks in at 18:00 but last checks out at 23:45 (i.e., 1425 minutes), the offset is 345 minutes, exceeding the 180-minute threshold, so the nurse is excluded from this statistics. The following is an example of actual offset data: Table 1 Sample table of nurse shift offset data

[0020] As shown in Table 1, records with an offset of more than 180 minutes or an offset less than or equal to zero are considered invalid and not included in the statistics. Finally, the difference interval of the off-duty time is generated for subsequent processing.

[0021] The time difference screening submodule performs conditional judgment on the time difference based on the difference interval of the off-duty time, retains the time difference data items that are greater than zero, and generates the start and end time set of the extended segment in combination with the corresponding planned off-duty time to obtain the newly added working time interval set; After calling the data set of the difference interval of off-duty time, the system filters the offset minutes in each record and only retains valid data items with offset values ​​greater than zero and not exceeding 180 minutes. For example, the offset of nurse A001 is 47 minutes and that of A002 is 92 minutes, both of which are valid records. The system combines the planned off-duty time with the offset minutes to construct the corresponding time period. The start time of the segment is the planned off-duty time and the end time is the actual off-duty time. The time period is stored in minutes. For example, the time period corresponding to A001 is 1080 to 1127 minutes and that of A002 is 1080 to 1172 minutes. If the actual off-duty time minute value is less than the start time minute value, such as a data starting value of 1080 and ending value of 975 minutes, the system will determine it as cross-day abnormal data and directly remove it. To ensure that such abnormal situations are not mistakenly included in the statistics, the system is designed to automatically determine it as invalid when the end time value is less than the start time value, and record the entry index of this situation through the data log for subsequent verification and processing. The data conversion results are summarized as follows: Table 2 Sample table of valid new time period data

[0022] As shown in Table 2, only A001 and A002 meet the validity requirements. A005 is excluded because it spans days (end time < start time). Finally, the newly added working time interval set is constructed.

[0023] The time period decomposition submodule divides each time period into fixed granularity based on the newly added working time interval set, extracts all continuous time points covered by each interval, and summarizes the number of continuous time points generated by each segment to obtain the number of newly added working time period points; Based on the newly added working time interval set, each segment data is split into time points. Each group of intervals is divided into fixed 15-minute intervals. For example, the nurse A001 segment is from 1080 to 1127 minutes, and the interval length is 47 minutes. It can be divided into three 15-minute segments (1080 to 1095, 1095 to 1110, and 1110 to 1125). The starting points of each segment are set to 1080, 1095, and 1127 minutes respectively. 110, thus generating three new time points. Similarly, the 92 minutes corresponding to A002 can be divided into six time periods (1080 to 1095, 1095 to 1110, 1110 to 1125, 1125 to 1140, 1140 to 1155, and 1155 to 1170), with the starting points being 1080, 1095, 1110, 1125, 1140, and 1155, generating six time points. By merging all recorded time point sets, we can obtain the overall distribution of new time points, which can be used to quantify the changes in manual workload at the time point level. The number of time points after statistics is as follows: Table 3 Statistics of newly added time points

[0024] As shown in Table 3, a total of 9 new time period sites were generated. The system finally aggregated all the splittable time point data to obtain the number of new working period sites.

[0025] See also Figure 3 ,The task period matching module includes: The task information reading submodule obtains the data of each time period location in the number of newly added working time period locations, reads the information of the tasks to be scheduled in the nurse task pool, extracts the planned start time and standard nursing duration of the tasks, calculates the start and end time intervals of each task, and generates a set of time intervals for tasks to be matched; To obtain the data of each time period location in the number of newly added working time period locations, the system first identifies the newly added available time period of each nurse based on the time point record generated by the front module, and then accesses the task data list marked as "to be scheduled" in the nurse task pool database, extracts the "planned start time" and "standard nursing duration" fields in each task record, and then calls the task type field to determine its corresponding nursing standard. According to the national "Nursing Operation Standards" specifications, the nursing level and the corresponding standard duration can be found in the task template table. The system realizes the unique positioning of the task through task coding, and adds the obtained planned start time to the nursing standard duration to calculate the task end time, and then adds the start time to the standard duration. The end time is converted into minutes starting from midnight to form minute-level time intervals. For example, the planned start time of nurse A001's task is 08:45, the nursing level is level one nursing, and the standard nursing duration is 45 minutes. The corresponding end time is 09:30. The start time is 525 minutes and the end time is 570 minutes. The task record is encapsulated as the interval (525, 570); if a task has no nursing duration field or the field value is 0, the system determines that the data is missing and does not participate in the time period matching process; all valid tasks are calculated in sequence to establish a task time interval set. Each record in the set carries necessary fields such as task code and start and end minutes. See the following table for an example: Table 4 Time interval data table of tasks to be scheduled

[0026] As shown in Table 4, A003 was marked as invalid due to the lack of nursing time, and the rest were converted into minute-level task intervals, ultimately generating a set of task time intervals to be matched.

[0027] The task time comparison submodule matches the start time and end time of each task based on the set of task time intervals to be matched and the time period data of each location in the newly added working period location number. The formula is: ; The matching score of the newly added task is obtained by calculation. If it is less than the matching success benchmark value, it is marked as a matching task. The matching task set is obtained by statistics, where: Indicates the The matching score of each nurse task, in minutes, Indicates the The scheduled start time of each task (in minutes), Indicates the The scheduled end time of each task (minutes), Indicates the scheduling density level of the time period in which the task is located, in units of task items / minute. Indicates the The standard nursing duration of each task, in minutes, Indicates the The number of similar tasks in the time period. Indicates the The total number of tasks in the nurse scheduling task set (integer); Based on the set of task time intervals to be matched, the minute-level start and end time periods of each task are read in sequence, and the continuous time period records generated by the number of newly added work time period locations are called. For example, the newly added time periods for nurses are (540, 600), (615, 675), etc. The system determines whether each task time period is completely nested in any newly added time period interval. If nested, it is included in the next matching scoring process. Assume that nurse Z003 has 3 tasks, and her data is as follows: Task T01: start and end minutes are 540~585, , , ; Task T02: start and end minutes are 590~630, , , ; Task T03: start and end minutes are 640~680, , , ; Substitute the formula and calculate item by item: ; The matching benchmark threshold is set to 350 minutes. This value is derived from the 90th percentile statistical value of the scores of nearly 1,000 scheduling matching records on the scheduling platform. The match is considered successful. The current score of nurse Z003 is 375.69, which does not meet the conditions and is not marked as a match. See the table below: Table 5 Matching calculation and judgment results

[0028] As shown in Table 5, only Z006 is matched successfully, and the system finally obtains a set of matching tasks.

[0029] The new task matching score is a comprehensive assessment of the degree of fit between each nurse's current set of matching tasks and the newly added work slots within their available time slot. This score quantifies multiple factors, including task start and end time spans, scheduling density, task duration, and number of tasks, to reflect the cumulative time and workload associated with the nurse's workload during the newly added work slots. A lower score indicates that tasks are more likely to fit neatly within the newly added time slots and that the workload is more evenly distributed, leading the scheduling system to more easily determine a "successful match." Conversely, a higher score indicates that tasks are either too dense or poorly distributed, reducing the likelihood of a successful match. Therefore, the new task matching score serves as a key criterion in the system scheduling process, used to filter, screen, and confirm whether each nurse can effectively undertake the scheduled tasks within the newly added time slots.

[0030] Formula by nurse first The time span, scheduling load, standard nursing time and task concentration of each task in the task set are comprehensively calculated to evaluate the degree of fit between the task time and the newly added schedulable time. represents the duration of the task in minutes, and the denominator in is the scheduling density (task items / minute) for that period. 1 is added to avoid division errors when the density is zero, while retaining the impact of task density on time allocation, forming the task load sharing value per unit time. Subsequently, this time "pressure value" is compared with the standard nursing time of the task itself. and the number of tasks in that period Add the products of This item reflects the superposition intensity of the nurse's task concentration and time consumption during this period. The two parts are added together and the absolute value is taken as the whole to ensure that the score is positively integrable, which is convenient for matching and evaluating with the threshold. It reflects the comprehensive intensity of time, pressure and density required for the nurse to complete all tasks in the current scheduling round, and is used to determine whether the nurse is suitable for the corresponding newly added scheduling period.

[0031] The adaptation result statistics submodule counts the task volume by nurse and date dimensions based on the task sets marked as successfully matched in the matching task set, extracts the total statistical amount of tasks corresponding to each newly added time period, and integrates them to obtain the task adaptation data for the newly added time period; Based on all task records marked as "matched successfully" in the matching task set, the system performs quantitative statistics using "nurse number × time period number" as the two-dimensional statistical dimension. For each newly added working time period, the system extracts the number of tasks included in the scheduling match within that period, constructs a two-dimensional mapping data matrix, and classifies and integrates it into a time period task statistics table. For example, in a certain department, the newly added time periods for nurse A005 on July 20, 2024 are (540, 600) and (615, 675), with 2 and 0 successfully matched tasks respectively. Nurse A009 has 1 successfully matched task in the time period (570, 630). The overall summary table is as follows: Table 6 Statistics of task adaptation in new time periods

[0032] As shown in Table 6, each newly added time period corresponds to a task adaptation amount. The system summarizes the fields in the table to obtain the task adaptation data for the newly added time period.

[0033] See also Figure 4 ,The task cross-border splitting module includes: The task time judgment submodule is based on the nurse handover buffer time. It reads the start and end times of all tasks recorded in the current nurse's daily work schedule, determines whether the end time of each task is later than the nurse's actual off-duty time minus the handover buffer time, and records it as a split task. It then obtains a list of task markers that need to be split. Based on the handover buffer time in the nurses' actual off-duty time records, the system first extracts each nurse's actual off-duty time information from the nursing staff attendance data. This data comes from the daily clock-in module of the scheduling system, and the time accuracy is controlled in minutes. For example, nurse A027's actual off-duty time on July 18 is 17:30. The system defaults to a handover buffer time of 15 minutes, and the matching judgment time point is 17:15. Then, the scheduling task record table in the task management subsystem is called to filter the task list under the current date. The planned end time field of each task item is read and compared and judged one by one. Tasks with an end time earlier than the judgment time are directly skipped and do not enter the judgment process. Tasks with an end time later than the judgment time enter the marking process. For example, the end time of task B105 is 17:40. After comparing it with the judgment time of 17:15, the judgment result is exceeded. The system sets a split flag for this task item in Boolean form and stores the marking result in the task tag list. To verify the data distribution, some task items for nurses A027 to A032 on July 18 are selected and listed as follows: Table 7 Task termination time judgment record table

[0034] As shown in Table 7, the system accurately determines in minutes whether the termination time of each task is later than the set decision point and generates a list of task markers that need to be split.

[0035] The task time splitting submodule determines the planned start time, end time, and nursing duration of each task based on the task tag list to be split. It compares the time period within the task interval with the nurse's dispatchable time period, confirms the split point location, and demarcates the first half of the task interval as the retained segment and the second half as the split segment, thereby obtaining the task split segment time dataset. According to the task mark list to be split, the system calls the three parameters of the marked task: the planned start time, planned end time, and standard nursing duration. After reading the task time information, it first locates the complete period of the task within the scheduling interval, and then uses the actual off-duty time minus the handover buffer time as the split point to divide the task time period into two continuous segments. The division standard is whether the split point is in the middle of the task time period. For example, the start time of task B105 is 16:50 and the end time is 17:40. The off-duty time of nurse A027 is 17:30, and the split point is 17:15. , the first segment of the split is from 16:50 to 17:15, and the second segment is from 17:15 to 17:40. The two task intervals correspond to different data structure records, and the corresponding task codes are expanded to B105-1 and B105-2. The first segment is the retained segment of the original task, and the second segment is the split task. The start and end time of the split segment are generated from the split point backward. The system stores all the split task segments in the task split segment time record set. The task record field must be accompanied by a split type field, indicating that the task is a segment generated by the decomposition of the original task, and the task split segment time data set is obtained.

[0036] The split task extraction submodule generates a new task code by uniformly numbering the split tasks according to the split start time and split end time information of each record in the task split time data set, combining the corresponding nurse number and the original task code, and records the corresponding task start and end information, task responsible nurse and task content, and obtains the total amount of new tasks after cross-border splitting; Based on all the split task fragment information recorded in the task split time data set, the system reconstructs the task fields according to the task start and end time, nursing workload and nurse identification information, generates a separate task code for each task split fragment, and recombines the field information such as the split segment start and end time, task nursing workload, and original task number to establish a standardized task data structure record. The distinguishing mark between the split fragment and the original task is set to "cross-border generation", the task nursing content field remains unchanged, and the nursing responsible person field uses the original task setting. If the original task nursing level is level three, the standard duration is 30 minutes, and the task split segment is the last 15 minutes, then the workload field in the task data item is set to 30 minutes × 0.5 = 15 minutes. The system scheduling module calls the task split time data set and the nursing duration record table to bind and complete the fields, and finally generates a complete structure of the split task entry, uniformly includes all split segment tasks in the task scheduling database, and records the split source mapping relationship to obtain the total amount of new tasks after cross-border splitting.

[0037] See also Figure 5 ,The scheduling structure reconstruction module includes: The idle segment identification submodule extracts the end time of each nurse's task and the start time of the next task in chronological order based on the newly added time period task adaptation data and the total number of new tasks after cross-border splitting. It then determines the interval between the two time nodes, confirms whether there is a time period with no assigned tasks, constructs data structure entries in a unified format, and establishes a nurse idle time period dataset. Based on the newly added time period task adaptation data and the total amount of new tasks after cross-border splitting, the system extracts all nurses' task records for the day from the scheduling database, arranges the task start and end time data in chronological order, calculates the interval time period between adjacent tasks, and identifies whether there is a blank segment whose time is not covered by the task. Then, it compares each item according to the task sequence structure. If the start time of the latter task is later than the end time of the previous task, the time difference between the two is the identifiable idle segment. In this process, the system eliminates cases where the time difference is zero or negative. For example, the task record of nurse number C017 is: Task T001 starts at 09:00 and ends at 09:30, and Task T002 starts at 10:00. The system recognizes that the nurse has a 30-minute idle segment between 09:30 and 10:00. This segment record is a structure entry containing the fields of nurse number, idle segment start time, idle segment end time, date, and original task number. Multiple entries can construct an idle time segment dataset, as shown in the table below: Table 8 Sample table of nurses’ free time periods

[0038] As shown in Table 8, the system completes the extraction process of all nurse idle segments in sequence and generates a nurse idle time segment dataset.

[0039] The task insertion judgment submodule is based on the nurses' idle time period dataset and the information of the tasks to be scheduled in the total amount of new tasks after cross-border splitting. It determines whether it can be completely embedded in the idle time period of any nurse, confirms the combination relationship between the successfully matched tasks and the idle period, and obtains the task insertion matching degree data table; After obtaining the nurses' idle time period data set, the system reads the list of tasks to be scheduled from the total number of newly added tasks, and extracts the task code, nursing time, task priority, scheduling restriction type, task shift and whether it crosses time periods for each task record. The system then compares the task nursing time with the length of each nurse's idle period to determine whether the task can be fully covered in the idle period, thereby forming a matching relationship. During the judgment process, if the task nursing time is longer than the length of the idle period, the idle period is eliminated and the next cycle is entered. If the matching requirements are met, the system continues to read the task priority field. This field has five levels from 1 to 5. The smaller the value, the higher the priority. The system sets the task priority screening benchmark to level ≤ 2. As a strong insertion condition, this type of task is preferentially adapted to the time period, and the time difference in the idle segment is close to the nursing duration as the primary judgment criterion. If the nursing duration of task T034 is 25 minutes, the priority is 1, and it is successfully inserted in the idle segment from 10:15 to 10:45 (30 minutes), the matching relationship is written into the task insertion result table as a structure record item, completing the comparison and judgment between all tasks and the idle segment, and generating a task insertion matching degree data table.

[0040] The task start and end synchronization submodule processes the corresponding task information one by one according to the matching success relationship items recorded in the task matching data table, obtains the start and end time of the idle time period to be inserted into the task, inserts it into the corresponding nurse task list, and updates the task sequence number and time node in the scheduling record to obtain the scheduling matching record after dynamic matching; According to the task matching data table, the system updates the post-matching fields of the successfully matched tasks, extracts the start time of the task matching idle segment as the new start time of the task, and the nursing duration field remains unchanged. The new end time is obtained by postponing the start time. The task is then inserted into the corresponding nurse task list and inserted into the remaining task timelines in sequence. The task sorting order is adjusted according to the task numbers before and after the task insertion, and the idle segment data between the tasks in the task list is recalculated. The updated task records are written back to the scheduling main table, and new scheduling snapshot data records are generated synchronously. Data mapping is established in the task + time dimension. Finally, the system outputs the scheduling data records after all newly added tasks are matched and adjusted, and obtains the scheduling matching records after dynamic matching.

[0041] See also Figure 6 , the management execution coverage modules include: The task allocation and extraction submodule reads the task information in sequence based on the dynamic matching schedule and matches the task number with the nurse's check-in number and check-in time, constructs a data table including the schedule information and check-in information structure, and obtains the task check-in matching data table; Based on the dynamic matching schedule matching records, the system retrieves daily task data from the schedule database, sorts by time field, and reads each nurse's task number and its corresponding start and end time in turn. Then, the system synchronously calls the sign-in system database to extract the sign-in table, selects the entries with complete fields in the daily sign-in data, and performs an equivalent connection operation on the nurse number field and the nurse number field in the schedule data. For the sign-in time with the same nurse number, it compares the task start time to determine whether the nurse has completed the sign-in before the task starts. For example, nurse A013's task number is T057, the start time is 08:30, and the sign-in time is 08:22. The early sign-in condition is met. The task is merged with the sign-in data to construct a preliminary record of task execution. If the sign-in record is missing or the sign-in time is more than ten minutes later than the task start time, it is marked as a delayed task and the missing reason field is recorded. The system sets a field validation mechanism in the preliminary merging stage. If the task number field contains a non-standard prefix or appears repeatedly, such as "TASK-123" and "TASK_123", they are considered inconsistent numbers and will be eliminated. Subsequently, the nurse number, task number, start time, end time, and sign-in time are written to each valid matching record. A cross-table data mapping structure is established, and the task number is used as the primary key to complete the data sorting work. The final output data structure is as follows: Table 9 Nurse task check-in matching sample table

[0042] As shown in Table 9, this table is based on the joint extraction of scheduling and sign-in data. It is used to identify and filter out task-sign-in matching entries that meet the scheduling relationship. During the field screening and comparison process, the system standardizes the data structure through steps such as unified processing of time fields and elimination of abnormal records, and finally generates a task sign-in matching data table.

[0043] The record synchronization writing submodule processes all task data entries in the task sign-in matching data table based on the task sign-in time, and synchronously records the time difference between the task scheduling time and the sign-in time to form a nursing data record with structured entries and task execution status parameters, and establish a task execution status identification data set; After obtaining the task check-in matching data table, the system reads the task number and check-in time one by one, converts each data structure into a data format that meets the electronic nursing record standard, and sets the task execution status label according to the offset between the task scheduling time and the check-in time. First, the system extracts the task plan start time field and the check-in time field, calculates the time difference between the two, and determines whether there is an advance or delay. If the time difference is between -5 minutes and +5 minutes, it is classified as a normal execution state. If it exceeds ±5 minutes, an offset mark is added. For example, if the check-in time of nurse A014 is 09:45 and the task start time is 09:30, the task is marked as "delayed execution" and an additional record is made. The offset time is recorded as 15 minutes. Next, the system writes the above structured data into the electronic nursing record table. The fields include: nurse number, task number, planned start time, actual check-in time, offset status, and offset duration. When writing, the system sorts by timestamp and numbers the daily task records of the same nurse in sequence. If it is found that the same task number corresponds to multiple check-in times, such as nurse A015 has two check-in records of 08:55 and 09:01 for task T062, the system retains the one closest to the task start time and eliminates the rest. The system does not perform any calculation processing during the writing stage, but only performs formatting specifications and structural supplements. Through the above process, a task execution status identification dataset is established.

[0044] The dynamic data collection submodule classifies and organizes the task execution status of each nurse according to the status information of all task records in the task execution status identification data set, establishes a data mapping of the time series dimension after statistics, and obtains the dynamic management records of nurse tasks; Based on the task execution status identification dataset, the system uses the daily date field as the aggregation basis, groups all records by nurse number, and performs statistical aggregation on each group of task entries. The system extracts the total number of tasks for each nurse that day, the number of tasks with a "delayed" status, and the number of tasks with a "missing" status. The normal task percentage field and the average offset time value are calculated as evaluation indicators. During the statistical process, the system reads the offset status field of each record and aggregates all offset duration field values ​​to calculate the average offset rate indicator for the nurse that day. For example, if nurse A014 performs three tasks, two of which are delayed and one is normal, the delay rate is 66.7%. If the offset durations of the two delayed tasks are 12 minutes and 8 minutes, respectively, the nurse's average offset rate for the day is 10 minutes. The system then structures these statistical indicators into a daily snapshot structure with fields including nurse number, date, number of tasks, number of delays, number of missing tasks, normal rate, and average offset duration. Time series record data is constructed using date as the time axis, and the output is a daily dynamic task data stream. After integrating all records, the system forms a sequence dataset for scheduling monitoring, obtaining dynamic management records of nurse tasks.

[0045] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A nurse task management system with dynamic working hours adjustment, characterized in that: The system comprises: The work time extension recognition module obtains the actual off-duty time in the nurse's sign-in record, combines it with the corresponding planned off-duty time in the schedule, calculates the difference, and filters the data items with a difference greater than zero. It then splits the data into a set of continuous time points to obtain the number of newly added work time slots. The task period matching module reads the planned start time and standard nursing duration of the task to be scheduled based on the number of time period sites in each of the newly added work period sites, filters out tasks whose start and end times fall into any of the newly added time period sites, and statistically generates the newly added time period task adaptation data; The task cross-boundary splitting module retrieves the start and end time of the current nurse's scheduled task based on the nurse handover buffer time. If the task end time exceeds the handover buffer time, the task is split into a retained segment and a split segment based on the standard nursing duration, and the split segment tasks are extracted as tasks to be scheduled, generating the total number of new tasks after cross-boundary splitting; The scheduling structure reconstruction module calculates whether the interval segment can accommodate the corresponding task based on the newly added time period task adaptation data and the total amount of new tasks after cross-border splitting according to the standard nursing time, synchronously updates the nurse's corresponding task start and end schedule, and generates a dynamic scheduling matching record.

2. The nurse task management system with dynamic working hours adjustment according to claim 1 is characterized in that: The number of newly added work time period sites includes a time interval set, a continuous time point set, and a site number distribution; the newly added time period task adaptation data includes a task matching number, a task adaptation ratio, and a matching time period; the total amount of new tasks after the cross-border split includes the number of retained segment tasks, the number of split segment tasks, and the split task identifier; the scheduling matching record after dynamic plug-in includes the task rescheduling order, plug-in time period, and nurse task update table.

3. The nurse task management system with dynamic working hours adjustment according to claim 1 is characterized in that: The working hours extension identification module includes: The off-duty time extraction submodule obtains the off-duty time of each sign-in record based on the actual off-duty time in the nurse sign-in record, associates it with the corresponding planned off-duty time in the schedule, extracts the minute-level difference data between the two time information, and generates the off-duty time difference interval; The time difference screening submodule performs conditional judgment on the time difference based on the off-duty time difference interval, retains the time difference data items that are greater than zero, and generates a start and end time set of the extended segment in combination with the corresponding planned off-duty time to obtain a new working time interval set; The time period decomposition submodule divides each group of time intervals into fixed granularity based on the newly added working time interval set, extracts all continuous time points covered in each interval, summarizes the number of continuous time points generated by each segment, and obtains the number of newly added working time period points.

4. The nurse task management system with dynamic working hours adjustment according to claim 1 is characterized in that: The task period matching module includes: The task information reading submodule obtains the data of each time period location in the number of newly added working time period locations, reads the information of the tasks to be scheduled in the nurse task pool, extracts the planned start time and standard nursing duration of the tasks, calculates the start and end time intervals of each task, and generates a set of time intervals of tasks to be matched; The task time comparison submodule matches the start time and end time of each task based on the set of time intervals of the tasks to be matched, combined with the time period data of each location in the newly added work period location number, and calculates the matching score of the newly added task. If it is less than the matching success benchmark value, it is marked as a matching task, and the matching task set is obtained by statistics; The adaptation result statistics submodule performs task volume statistics by nurse dimension and date dimension based on the task set marked as successfully matched in the matching task set, extracts the statistical total of tasks corresponding to each newly added time period, and integrates the task adaptation data for the newly added time period.

5. The nurse task management system with dynamic working hours adjustment according to claim 1 is characterized in that: The task cross-border splitting module includes: The task time judgment submodule reads the start and end times of all tasks recorded in the current nurse's daily work schedule based on the nurse's shift handover buffer time, determines whether the end time of each task is later than the nurse's actual off-duty time minus the shift handover buffer time, records it as a split task, and obtains a list of task markers that need to be split; The task time splitting submodule determines the planned start time, end time and nursing duration of each task based on the list of task tags to be split, compares the time period within the task interval with the nurse's dispatchable time period, confirms the split point position, demarcates the first half of the task interval as the retained segment and the second half as the split segment, and obtains the task split segment time data set; The split task extraction submodule generates a new task code by uniformly numbering the split segment tasks according to the split start time and split end time information of each record in the task split segment time data set, combined with the corresponding nurse number and the original task code, and records the corresponding task start and end information, task responsible nurse and task content to obtain the total amount of new tasks after cross-border splitting.

6. The nurse task management system with dynamic working hours adjustment according to claim 1 is characterized in that: The scheduling structure reconstruction module includes: The idle segment identification submodule extracts the task end time and the start time of the next task of each nurse in chronological order based on the newly added time period task adaptation data and the total amount of new tasks after the cross-border split. It judges the interval between the two time nodes to confirm whether there is a time period with no assigned tasks, constructs data structure entries in a unified format, and establishes a nurse idle time period dataset; The task insertion judgment submodule is based on the nurse idle time period dataset and the information of the tasks to be scheduled in the total amount of new tasks after the cross-border splitting, and determines whether it can be completely embedded in the idle time period of any nurse, confirms the combination relationship between the successfully matched tasks and the idle segments, and obtains the task insertion matching degree data table; The task start and end synchronization submodule processes the corresponding task information one by one according to the matching success relationship items recorded in the task matching data table, obtains the start and end time of the idle time period to be inserted into the task, inserts it into the corresponding nurse task list, and updates the task sequence number and time node in the scheduling record to obtain the scheduling matching record after dynamic matching.

7. The nurse task management system with dynamic working hours adjustment according to claim 1 is characterized in that: The system further comprises: The management execution coverage module reads the nurse number, task number, task start and end time in the scheduling record and the actual check-in time in the check-in record based on the dynamic matching schedule record, writes the task allocation content into the electronic nursing record, summarizes the daily nurse task dynamic change data entries, and establishes a dynamic management record of nurse tasks; The nurse task dynamic management record includes task allocation log, allocation execution information, and daily dynamic entries.

8. The nurse task management system with dynamic working hours adjustment according to claim 7 is characterized in that: The management execution coverage module includes: The task allocation and extraction submodule reads the task information in sequence based on the dynamic matching post-shift matching record, associates and matches the task number with the nurse's check-in number and check-in time, constructs a data table including the shift information and check-in information structure, and obtains the task check-in matching data table; The record synchronization writing submodule processes each data entry based on all task data entries in the task check-in matching data table, synchronously records the time difference between the task scheduling time and the check-in time, forms a nursing data record with structured entries and task execution status parameters, and establishes a task execution status identification data set; The dynamic data collection submodule classifies and organizes the task execution status of each nurse according to the status information of all task records in the task execution status identification data set, establishes a data mapping of the time series dimension after statistics, and obtains the dynamic management record of nurse tasks.

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