Employee task dynamic scheduling control method and system based on virtual digital identity

Abstract

The application discloses a virtual digital identity-based employee task dynamic scheduling control method and system, relates to the technical field of digital employee task scheduling, and comprises the following steps: collecting the task amount of each time period of different dates and the completion condition of each task; extracting a task density dynamic threshold value, performing weight distribution processing, and obtaining time cost weight data; obtaining the time weight and cost weight of the current date based on the time cost weight data, simultaneously scheduling multiple tasks based on an auction algorithm mechanism, and obtaining all selectable scheduling schemes; and selecting the best scheduling scheme from all selectable scheduling schemes to perform task scheduling; the application is used for solving the problem that the existing digital employee task scheduling technology cannot simultaneously auction and allocate multiple tasks according to historical date task allocation data and balance task processing time and cost when scheduling the work tasks of digital employees based on the auction algorithm mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital employee task scheduling, specifically to an employee task dynamic scheduling control method and system based on virtual digital identity. BACKGROUND

[0002] Digital employee task scheduling technology refers to a technical system that uses intelligent algorithms and system architecture to plan, allocate, monitor and optimize the work tasks of digital employees. The core goal is to ensure that digital employees complete tasks efficiently and orderly in complex business scenarios, maximize resource utilization and reduce operating costs.

[0003] The existing digital employee task scheduling technology often uses a task-by-task auction allocation mechanism when scheduling the work tasks of digital employees based on an auction algorithm mechanism. Each time, only a single task auction process is handled. If the total number of tasks to be allocated is too large, the communication and calculation overhead will increase linearly with the number of tasks, and the efficiency of task scheduling will be low. The task-by-task auction results in scattered allocation of digital employee resources, which cannot take advantage of economies of scale. For example, patent application CN119378940A discloses a multi-robot multi-task scheduling method and application based on a dynamic auction algorithm. This scheme uses a task-by-task auction allocation mechanism when scheduling the work tasks of robots based on an auction algorithm mechanism, which is not efficient in task scheduling. Moreover, the allocation rules of existing auction algorithms often use fixed rules, such as always allocating tasks to the digital employee with the lowest cost or the shortest time, which cannot be adjusted dynamically according to real-time task density and system load. For example, during peak business hours, the fixed rule still prioritizes cost, which can result in a large number of tasks being allocated to low-capacity resources and exceeding the time limit. During off-peak hours, the fixed rule still prioritizes time, which can waste computing resources and increase costs. Therefore, the existing digital employee task scheduling technology cannot simultaneously auction multiple tasks on the current date based on historical task allocation data, balance task processing time and cost, and schedule the work tasks of digital employees based on an auction algorithm mechanism. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the prior art, by collecting the task amount of each time period of different dates and the completion of each task; and extracting the task density dynamic threshold, then performing weight allocation processing to obtain time cost weight data; and obtaining the time weight and cost weight of the current date, and simultaneously scheduling multiple tasks based on the auction algorithm mechanism to obtain all selectable scheduling schemes; selecting the best scheduling scheme from all selectable scheduling schemes according to the time weight and cost weight of the current date to perform task scheduling; to solve the problem that the existing digital employee task scheduling technology cannot simultaneously auction and allocate multiple tasks according to the historical date task allocation data in the current date based on the auction algorithm mechanism for scheduling the work tasks of digital employees, while balancing task processing time and cost.

[0005] To achieve the above-mentioned purpose, in a first aspect, the present application provides a virtual digital identity-based employee task dynamic scheduling control method, comprising the following steps:

[0006] Collecting the task amount of each time period of different dates and the completion of each task to obtain historical task reference data;

[0007] Extracting the task density dynamic threshold based on the historical task reference data, and performing weight allocation processing according to the task density dynamic threshold to obtain time cost weight data;

[0008] Obtaining the time weight and cost weight of the current date based on the time cost weight data, and simultaneously scheduling multiple tasks based on the auction algorithm mechanism to obtain all selectable scheduling schemes;

[0009] Selecting the best scheduling scheme from all selectable scheduling schemes according to the time weight and cost weight of the current date to perform task scheduling.

[0010] Further, collecting the task amount of each time period of different dates and the completion of each task to obtain historical task reference data comprises the following sub-steps:

[0011] Dividing the date type into weekdays and weekends; for any one historical date, denoted as the first date; obtaining the date type of the first date;

[0012] And dividing the first date into multiple uniform time periods with a first time interval within the first date, and denoting any one time period as the first time period, wherein the first time interval is t1;

[0013] Obtaining the number of tasks received by the scheduling platform in the first time period, denoted as AF; and obtaining the limited duration time length of each task, denoted as the limited completion duration, and obtaining the actual time length used by each task to complete, denoted as the actual completion duration, to obtain the initial task-related data of the first time period after completion.

[0014] Further, the task quantity of each time period and the completion of each task on different dates are collected to obtain historical task reference data, which further includes the following sub-steps:

[0015] According to the initial task-related data of the first time period, the task density MP corresponding to the first time period is calculated, MP = AF / t1;

[0016] And the number of tasks with an actual completion duration not greater than the limited completion duration among all tasks received by the scheduling platform in the first time period is obtained, denoted as BF; and the task completion rate RE corresponding to the first time period is calculated, RE = BF / AF;

[0017] The task density MP and the task completion rate RE corresponding to the first time period are recorded as the first task-related data of the first time period.

[0018] The first task-related data of all time periods in the first date is repeatedly obtained to obtain the first task reference data of the first date; and the first task reference data of each date is continuously repeatedly obtained, denoted as historical task reference data.

[0019] Further, based on the historical task reference data, a task density dynamic threshold is extracted, and a weight distribution process is performed according to the task density dynamic threshold to obtain time cost weight data, which includes the following sub-steps:

[0020] According to the date type of the first date, the k1 historical dates before the first date and having the same date type as the first date are obtained, denoted as reference dates of the first date, and any one reference date is denoted as a first reference date, wherein k1 is a set number.

[0021] According to the first task reference data of the first reference date, the average value of the task density of all time periods of the first reference date is calculated, denoted as the average task density of the first reference date.

[0022] The average task density of all reference dates of the first date is repeatedly obtained, and is sorted from far to near according to the corresponding date order, denoted as an average task density sequence; and the reference task density CP0 of the first date is calculated according to the first formula, the first formula is as follows: Wherein TPi represents the i-th average task density in the average task density sequence.

[0023] Further, based on the historical task reference data, a task density dynamic threshold is extracted, and a weight distribution process is performed according to the task density dynamic threshold to obtain time cost weight data, which further includes the following sub-steps:

[0024] A time period in which the starting working time of a working day is located is recorded as a starting time period; according to the first task reference data of the first date, the task density MP and the task completion rate RE of all time periods in the first date are respectively sorted in time sequence starting from the starting time period, and a first task density sequence and a first task completion rate sequence are respectively obtained in sequence;

[0025] For the starting time period of the first date, the completion rate change trend corresponding to the starting time period of the first date is calculated according to a second formula, and the second formula is as follows: , wherein AH represents the completion rate change trend; AS is a set task density sensitivity coefficient, 0<AS<1; MPi represents the i-th task density in the first task density sequence; CP(i-1), if i-1=0, then CP(i-1) is the reference task density CP0, if i-1>0, then CP(i-1) represents the task density bearing value of the i-1-th time period of the first date;

[0026] Then, the task density bearing value corresponding to the starting time period of the first date is calculated according to a third formula, and the third formula is as follows: , wherein AQ is a set density change amplitude coefficient, 0<AQ<1;

[0027] The task density bearing values of all time periods in the first date are repeatedly calculated in sequence, and are arranged in the order of the corresponding time periods, and are recorded as a first density bearing value sequence; weight coefficients are set for all time periods in the first date, and the sum of the weight coefficients of all time periods in the first date is ensured to be equal to 1; and the task density dynamic threshold YP of the first date is calculated according to a fourth formula, and the fourth formula is as follows: , wherein qi is the weight coefficient of the i-th time period; n is the total number of time periods in the first date;

[0028] According to the historical task reference data, the task density dynamic threshold of all historical dates is repeatedly obtained.

[0029] Further, based on the historical task reference data, the task density dynamic threshold is extracted, and the weight allocation processing is performed according to the task density dynamic threshold, and the time cost weight data obtained further includes the following sub-steps:

[0030] For the first time period of the first date, according to the task density dynamic threshold YP of the first date and the task density MP corresponding to the first time period; if MP≥YP, then the time weight TE of the first time period is set to 1, and the cost weight CE is set to 0; if MP<YP, then the time weight TE of the first time period is set to MP / YP, and the cost weight CE is set to 1-MP / YP;

[0031] The time weight and the cost weight of each time period in the first date are repeatedly obtained to obtain the time cost weight information of the first date, and the time cost weight information of all historical dates is repeatedly obtained to obtain the time cost weight data.

[0032] Further, the time weight and the cost weight of the current date are obtained based on the time cost weight data, and a plurality of tasks are simultaneously scheduled based on an auction algorithm mechanism to obtain all selectable scheduling schemes, including the following sub-steps:

[0033] For the current date to be regulated, denoted as the first current date; any one time period in the first current date is denoted as the first current time period;

[0034] According to the date type of the first current date, the k2 historical dates before the first current date and the same as the date type of the first current date are obtained, denoted as the weight reference date of the first current date, wherein k2 is the number of settings;

[0035] According to the time cost weight information of the weight reference date, the time weight and the cost weight of the time period in all weight reference dates are obtained, and the time weight reference sequence and the cost weight reference sequence are sorted according to the corresponding date order, respectively;

[0036] According to the time distance of each weight reference date from the first current date, a weight coefficient is set for each weight reference date, and the sum of the weight coefficients of all weight reference dates is ensured to be equal to 1;

[0037] According to the weight coefficient of each weight reference date, the weighted average value of the time weight reference sequence and the cost weight reference sequence is calculated, respectively, to obtain the average time weight and the average cost weight, respectively, denoted as the current time weight and the current cost weight of the first current time period;

[0038] The current time weight and the current cost weight of all time periods in the first current date are repeatedly obtained.

[0039] Further, the time weight and the cost weight of the current date are obtained based on the time cost weight data, and a plurality of tasks are simultaneously scheduled based on an auction algorithm mechanism to obtain all selectable scheduling schemes, including the following sub-steps:

[0040] For the first current time period, the number of currently idle virtual digital employees is obtained, denoted as LA; and any one idle virtual digital employee is denoted as the first idle employee; and the number of tasks to be scheduled is obtained, denoted as LB;

[0041] Based on the auction algorithm mechanism, and according to LA and LB, LC tasks to be scheduled are auctioned at the same time; wherein, if LA>LB, LC=LB, otherwise LC=LA; the estimated processing time and the estimated processing cost given by the first idle employee for each auctioned task are obtained, and are recorded as processing time and processing cost respectively, to obtain the task processing data of the first idle employee; the task processing data of all idle virtual digital employees are repeatedly obtained, and are recorded as task processing cost data;

[0042] LC tasks to be scheduled are allocated to LA idle virtual digital employees, and all allocation schemes are obtained, and are recorded as optional scheduling schemes;

[0043] For any optional scheduling scheme, recorded as the first scheduling scheme, the sum of the estimated processing costs of all tasks in the first scheduling scheme is calculated, recorded as the processing cost of the first scheduling scheme, and the estimated processing time of all tasks in the first scheduling scheme is sorted in ascending order, and the k3th percentile is obtained, recorded as the processing time of the first scheduling scheme; wherein k3 is a set percentile; the processing time and the processing cost of each optional scheduling scheme are repeatedly obtained.

[0044] Further, selecting the best scheduling scheme from all optional scheduling schemes for task scheduling according to the time weight and the cost weight of the current date includes the following sub-steps:

[0045] The processing time and the processing cost of all optional scheduling schemes are sorted in descending order respectively, to obtain a processing time sequence and a processing cost sequence;

[0046] For the first scheduling scheme, the positions of the processing time and the processing cost of the first scheduling scheme in the corresponding processing time sequence and processing cost sequence are obtained, and are recorded as AG1 and BG1 in order respectively; and the processing time weight QG1 and the processing cost weight QG2 of the first scheduling scheme are calculated, wherein QG1=(AG1 / W0) / [(AG1 / W0)+(BG1 / W0)], QG2=(BG1 / W0) / [(AG1 / W0)+(BG1 / W0)], the processing time weight and the processing cost weight of all first scheduling schemes are repeatedly obtained, and are sorted in size respectively, and are recorded as a time weight sequence and a cost weight sequence respectively;

[0047] If the current time weight TE=1 and the current cost weight CE=0 in the first current period, the optional scheduling scheme with the largest processing time weight is marked as the best scheduling scheme;

[0048] Otherwise, VE and NE are obtained, VE = MAX (TE, CE), NE = MIN (TE, CE), and the time length weight sequence or the cost weight sequence is recorded as the first sequence according to the weight type of VE, and the other is recorded as the second sequence;

[0049] An optional scheduling scheme in the first sequence sequence located at [k4*VE, k5*VE] is obtained, recorded as a to-be-selected scheduling scheme; for any one to-be-selected scheduling scheme, the first to-be-selected scheme is recorded as a first to-be-selected scheme, the processing time length weight corresponding to VE is obtained as VG, and the other is recorded as NG; and the weight difference degree XG of the first to-be-selected scheme is calculated, XG = (|VE-VG|\VE) + (|NE-NG|\NE); the weight difference degrees of all to-be-selected scheduling schemes are repeatedly obtained, and the to-be-selected scheduling scheme with the minimum weight difference degree is recorded as the best scheduling scheme;

[0050] The task allocation scheduling is performed according to the best scheduling scheme.

[0051] In a second aspect, the present application provides an employee task dynamic scheduling control system based on a virtual digital identity, comprising a data acquisition module, a threshold weight module, a scheduling preset module and a task scheduling module;

[0052] The data acquisition module is used to acquire the task quantity of each time period of different dates and the completion condition of each task, and obtain historical task reference data;

[0053] The threshold weight module comprises a threshold extraction unit and a weight allocation unit, the threshold extraction unit extracts a task density dynamic threshold based on the historical task reference data, and the weight allocation unit performs weight allocation processing according to the task density dynamic threshold to obtain time cost weight data;

[0054] The scheduling preset module obtains time weight and cost weight of the current date based on the time cost weight data, and simultaneously schedules multiple tasks based on an auction algorithm mechanism to obtain all optional scheduling schemes;

[0055] The task scheduling module selects the best scheduling scheme from all optional scheduling schemes according to the time weight and cost weight of the current date to perform task scheduling.

[0056] The beneficial effects of the present application are as follows: the present application collects the task amount of each time period on different dates and the completion of each task to obtain historical task reference data; based on the historical task reference data, a task density dynamic threshold is extracted, and weight distribution processing is performed according to the task density dynamic threshold to obtain time cost weight data; based on the time cost weight data, the time weight and cost weight of the current date are obtained, and multiple tasks are simultaneously scheduled based on the auction algorithm mechanism to obtain all selectable scheduling schemes; the best scheduling scheme is selected from all selectable scheduling schemes according to the time weight and cost weight of the current date to perform task scheduling; when the work tasks of digital employees are scheduled based on the auction algorithm mechanism, the task allocation data of the historical date can be used to simultaneously auction and allocate multiple tasks on the current date, and the task processing time and cost are balanced at the same time;

[0057] Based on the sequence change of the task density and the completion rate, the carrying capacity of each period is calculated through the completion rate trend and the density carrying value, the real-time load change is adapted, the obtained threshold is more in line with the actual situation, and the final dynamic threshold is generated through weighted average, the influence of the work period on the overall threshold is highlighted, and the interference of the data of the rest period on the threshold is avoided; according to the task density dynamic threshold, the time weight and cost weight of each period are distributed, so that the weight distribution can not only adapt to the business periodic fluctuation, but also can respond to the task quantity change in real time, and the task processing time and cost can be balanced in real time during task scheduling; meanwhile, multiple tasks are auctioned and allocated, and the best scheduling scheme is selected, which can improve the efficiency of task scheduling and ensure that the task scheduling strategy conforms to the current actual situation. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 It is a principle block diagram of the system of the present application;

[0059] Figure 2 It is a step flow chart of the method of the present application;

[0060] Figure 3 It is a task scheduling strategy flow chart of the present application;

[0061] Figure 4 It is a structural schematic diagram of the electronic device of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0063] Embodiment 1, please refer toFigure 1 As shown, the present application provides a virtual digital identity-based employee task dynamic scheduling control system, comprising a data acquisition module, a threshold weight module, a scheduling preset module, and a task scheduling module;

[0064] The data acquisition module is used to acquire the task amount of each time period of different dates and the completion of each task, and obtain historical task reference data;

[0065] The data acquisition module is configured with a data acquisition strategy, which includes: dividing the date type into weekdays and rest days; for example, Monday to Friday is a weekday, and holidays and weekends are rest days; for any historical date, denoted as the first date; obtain the date type of the first date;

[0066] And divide the first date into multiple uniform time periods with a first time interval within the first date, and denote any one time period as the first time period, wherein the first time interval is t1; the single-day task amount fluctuation is decomposed into changes in smaller time units, avoiding coarse-grained data in units of "days" to mask the intra-day fluctuation rule; the first time interval can be set according to the actual application scenario, and in this embodiment, t1=1 hour;

[0067] Obtain the number of tasks received by the scheduling platform in the first time period, denoted as AF; and obtain the time length of the limited duration of each task, denoted as the limited completion duration, and obtain the actual time length of each task, denoted as the actual completion duration, and obtain the initial task-related data of the first time period after completion; the limited completion duration is the deadline constraint required by the task; the actual completion duration is the actual execution time of the task; the deviation from the limited completion duration directly reflects the scheduling efficiency, providing data support for subsequent evaluation of period carrying capacity;

[0068] According to the initial task-related data of the first time period, calculate the task density MP corresponding to the first time period, MP=AF / t1; quantify the task influx in the unit time period, which is used to measure the load pressure of the period, and is convenient for load comparison of different periods and different dates;

[0069] And obtain the number of tasks with an actual completion duration not greater than the limited completion duration among all tasks received by the scheduling platform in the first time period, denoted as BF; and calculate the task completion rate RE corresponding to the first time period, RE=BF / AF; quantify the proportion of tasks delivered on time in the period, which directly reflects the matching degree of system processing capacity and task pressure; for example, when MP=40 and RE=70% in a period, it can be determined that the MP has exceeded the system carrying capacity;

[0070] The task density MP and the task completion rate RE corresponding to the first time period are recorded as the first task-related data of the first time period;

[0071] repeatedly obtaining the first task-related data of all time periods in the first date to obtain the first task reference data of the first date, and repeatedly obtaining the first task reference data of each date in succession to obtain the historical task reference data;

[0072] In the specific implementation process, the date type is divided into weekdays and rest days, so as to lay a reference basis for subsequent threshold calculation and ensure that the subsequent threshold calculation conforms to the business cycle law. For example, the threshold calculation of weekdays only depends on the historical weekday data, avoiding the misjudgment of the threshold of the peak scene of weekdays caused by the low load data of rest days. Taking the average task density of rest days as the threshold of weekdays will lead to insufficient scheduling resources of weekdays.

[0073] The threshold weight module includes a threshold extraction unit and a weight allocation unit. The threshold extraction unit extracts the task density dynamic threshold based on the historical task reference data, and the weight allocation unit performs weight allocation processing according to the task density dynamic threshold to obtain the time cost weight data.

[0074] The threshold extraction unit is configured with a threshold extraction strategy, which includes: according to the date type of the first date, obtaining k1 historical dates before the first date and having the same date type as the first date, denoted as the reference date of the first date, and any one reference date is denoted as the first reference date, wherein k1 is the number of settings; in this embodiment, k1=7 days; using historical data of the same type of date, such as reference weekdays for weekdays and reference rest days for rest days, to avoid confusion of business characteristics of different date types, such as interference of low load data of rest days to threshold calculation of weekdays, so as to ensure that the subsequent threshold calculation conforms to the business periodicity law;

[0075] According to the first task reference data of the first reference date, the average value of the task density of all time periods of the first reference date is calculated, denoted as the average task density of the first reference date.

[0076] The average task density of all reference dates of the first date is repeatedly obtained, and is sorted from far to near according to the corresponding date order, denoted as the average task density sequence. The reference task density CP0 of the first date is calculated according to the first formula, and the first formula is as follows: Wherein TPi represents the i-th average task density in the average task density sequence. Through dynamic weighting of k1 recent dates, the weight is higher for the closer date, so that CP0 can reflect the recent business trend and avoid threshold deviation caused by outdated historical data. CP0 is the initial task density bearing value for subsequent calculation.

[0077] A starting time period of a starting working time of a working day is recorded as a starting time period; according to first task reference data of the first date, task densities MP and task completion rates RE of all time periods in the first date are respectively sorted in time sequence starting from the starting time period, and a first task density sequence and a first task completion rate sequence are respectively obtained in sequence;

[0078] For the starting time period of the first date, a completion rate change trend corresponding to the starting time period of the first date is calculated according to a second formula, and the second formula is as follows: , wherein AH represents the completion rate change trend; AS is a set task density sensitivity coefficient, 0<AS<1; MPi represents the i th task density in the first task density sequence; CP(i-1), if i-1=0, then CP(i-1) is a reference task density CP0, and if i-1>0, then CP(i-1) represents a task density bearing value of the i-1 th time period of the first date;

[0079] The second formula is used to quantify the change trend of the completion rate with the task density, and when a certain time period MPi exceeds a bearing value CP(i-1) of a previous time period, AH is negative, which drives CPi to decrease, thereby realizing linkage adjustment of “time period-density-completion rate”, for example, automatically reducing a threshold value of a subsequent time period after overload of a peak time period; AS represents the sensitivity of the task completion rate to the change of the task density, and the greater AS is, the greater the influence of the task density on the task completion rate is. AS can be set according to an actual application scene, and in this embodiment, AS=0.6

[0080] The third formula is used to calculate the task density bearing value corresponding to the starting time period of the first date, and the third formula is as follows: , wherein AQ is a set density change amplitude coefficient, 0<AQ<1; CPi not only considers the current MPi, but also is associated with a bearing state of a previous time period, thereby forming dynamic calibration in time sequence, and avoiding the problem that a static threshold value cannot adapt to real-time load fluctuation; AQ is used to adjust the dynamic change amplitude of the task density bearing value, and the greater AQ is, the more intense the change of the threshold task density bearing value is. AQ can be set according to an actual application scene, and in this embodiment, AQ=0.4;

[0081] The task density bearing values of all time periods in the first date are repeatedly calculated in sequence, and are arranged in the order of corresponding time periods, and are recorded as a first density bearing value sequence; weight coefficients are set for all time periods in the first date, and the sum of the weight coefficients of all time periods in the first date is ensured to be equal to 1; and a task density dynamic threshold YP of the first date is calculated according to a fourth formula, and the fourth formula is as follows: , wherein qi is a weight coefficient of the i th time period; n is the total number of time periods in the first date;

[0082] The weighted average mechanism enables the YP to reflect the overall level of the full period load and focus on the service peak pressure, for example, the working hours are 10 hours, the weight of each hour period is set to 0.6 / 10, the working hours are 14 hours, and the weight of each hour period is set to 0.4 / 14; and ensure that the YP is more suitable for the actual demand of the high load scene;

[0083] According to the historical task reference data, the task density dynamic threshold of all historical dates is repeatedly obtained;

[0084] The weight allocation unit is configured with a weight allocation strategy, and the weight allocation strategy comprises: for a first time period of a first date, according to the task density dynamic threshold YP of the first date and the task density MP corresponding to the first time period; if MP≥YP, the time weight TE of the first time period is set to 1, and the cost weight CE is set to 0; if MP<YP, the time weight TE of the first time period is set to MP / YP, and the cost weight CE is set to 1-MP / YP; that is, high load is given priority to guarantee task timeliness, and sudden drop of completion rate is avoided; low load is proportionally allocated weight, and cost optimization is considered;

[0085] The time weight and the cost weight of all time periods in the first date are repeatedly obtained to obtain the time cost weight information of the first date; and the time cost weight information of all historical dates is repeatedly obtained to obtain the time cost weight data;

[0086] In the specific implementation process, YP highlights the dominant role of the key period on the overall threshold through weight aggregation, avoids the low density data of the low peak period to lower the threshold, and causes the high peak period to be insufficient in scheduling resources; and YP as an "efficiency-cost" balance point enables the weight allocation to automatically adjust with real-time load, for example, MP of a certain period increases from 30 to 40, YP=35, TE increases from 0.86 to 1, triggering the time priority strategy to prevent task backlog.

[0087] The scheduling preset module obtains the time weight and the cost weight of the current date based on the time cost weight data, and simultaneously schedules multiple tasks based on the auction algorithm mechanism to obtain all selectable scheduling schemes;

[0088] The scheduling preset module is configured with a scheduling preset strategy, and the scheduling preset strategy comprises: for a current date to be controlled, denoted as a first current date; any time period in the first current date is denoted as a first current time period;

[0089] According to the date type of the first current date, the k2 historical dates before the first current date and the same as the date type of the first current date are obtained, denoted as the weight reference date of the first current date, wherein k2 is the number set; in this embodiment, k2=7;

[0090] According to the time cost weight information of the weight reference date, the time weight and the cost weight of the same time period as the first current period in all weight reference dates are obtained, and are sorted in corresponding date order respectively, and are marked as time weight reference sequence and cost weight reference sequence respectively; The precise data matching of "same time period, same type date" is realized, for example, the weight distribution of the first current date 9 o'clock is analyzed, and the weight distribution of the weight reference date 9 is obtained; Avoid the interference of cross-period data on the current period weight;

[0091] According to the time distance of each weight reference date from the first current date, a weight coefficient is set for each weight reference date, and the sum of the weight coefficients of all weight reference dates is ensured to be equal to 1; The closer the time distance, the higher the weight, giving higher weight to recent data to make the current period weight closer to the latest business state;

[0092] According to the weight coefficient of each weight reference date, the weighted average value of the time weight reference sequence and the cost weight reference sequence is calculated respectively, and the average time weight and the average cost weight are obtained in sequence, and are marked as the current time weight and the current cost weight of the first current period respectively; Through the weighted aggregation of historical weights of the same type and the same time period, the current weight conforming to the business rule is generated, providing accurate reference for subsequent task scheduling;

[0093] Repeat to obtain the current time weight and the current cost weight of all time periods in the first current date;

[0094] For the first current period, the number of currently idle virtual digital employees is obtained, marked as LA; And any idle virtual digital employee is marked as the first idle employee; And the number of tasks to be scheduled is obtained, marked as LB;

[0095] Based on the auction algorithm mechanism, and according to LA and LB, LC tasks to be scheduled are auctioned; If LA> LB, then LC= LB, otherwise LC= LA; Avoid the number of allocated tasks exceeding the number of idle employees; The estimated processing time and the estimated processing cost given by the first idle employee for each auctioned task are obtained, marked as processing time and processing cost respectively, and the task processing data of the first idle employee is obtained; Repeat to obtain the task processing data of all idle virtual digital employees, marked as task processing cost data; For example, there are three tasks, and the first idle employee gives the time and cost required to process task 1, task 2 and task 3, marked as the task processing data of the first idle employee;

[0096] LC tasks to be scheduled are allocated to LA idle virtual digital employees, and all allocation schemes are obtained, marked as optional scheduling scheme;

[0097] For any one optional scheduling scheme, denoted as a first scheduling scheme, the sum of the estimated processing cost of all tasks in the first scheduling scheme is calculated, denoted as the processing cost of the first scheduling scheme, and the estimated processing time of all tasks in the first scheduling scheme is sorted in ascending order, and the k3th percentile is obtained, denoted as the processing time of the first scheduling scheme; wherein k3 is a set percentile; the processing time and the processing cost of each optional scheduling scheme are repeatedly obtained; in this embodiment, k3 = 90;

[0098] In the specific implementation process, the processing cost of the scheme, that is, the sum of the processing costs of all tasks, measures the overall economy; and the k3th percentile, denoted as the processing time of the first scheduling scheme, is selected to prevent some few tasks from being overdue, and is more representative of the time cost of the scheme than the average processing time.

[0099] The task scheduling module selects the best scheduling scheme from all the optional scheduling schemes according to the time weight and the cost weight of the current date to perform task scheduling;

[0100] The task scheduling module is configured with a task scheduling strategy, and the task scheduling strategy includes: sorting the processing time and the processing cost of all the optional scheduling schemes in descending order to obtain a processing time sequence and a processing cost sequence; the relative performance of each scheme in the time and cost dimensions is determined through the sorting

[0101] Please refer to Figure 3 For the first scheduling scheme, the positions of the processing time and the processing cost of the first scheduling scheme in the corresponding processing time sequence and processing cost sequence are obtained, and are denoted as AG1 and BG1 in order; and the processing time weight QG1 and the processing cost weight QG2 of the first scheduling scheme are calculated, wherein QG1 = (AG1 / W0) / [(AG1 / W0)+(BG1 / W0)], QG2=(BG1 / W0) / [(AG1 / W0)+(BG1 / W0)], the processing time weight and the processing cost weight of all the first scheduling schemes are repeatedly obtained, and are sorted in descending order, and are denoted as a time weight sequence and a cost weight sequence respectively; the relative positions of the schemes are converted into quantitative weights to reflect their comprehensive performance in the time and cost dimensions, for example, a certain scheme has a leading position in time and a trailing position in cost, then QG1 is low and QG2 is high, indicating that the cost advantage is significant but the time consumption is long;

[0102] If the current time weight TE=1 and the current cost weight CE=0 in the first current time period, the optional scheduling scheme with the largest processing time weight is marked as the best scheduling scheme; in a high-load scenario, the time priority strategy is used to ensure the timeliness of the task and avoid the task from being overdue due to the consideration of the cost;

[0103] Otherwise, obtain VE and NE, VE = MAX (TE, CE), NE = MIN (TE, CE), determine the dominant weight of the current time period; and according to the weight type of VE, record the time length weight sequence or the cost weight sequence as the first sequence, and record the other as the second sequence; for example, VE is the current time weight, record the time length weight sequence as the first sequence, and record the cost weight sequence as the second sequence;

[0104] Obtain the optional scheduling scheme in [k4*VE, k5*VE] in the first sequence sequence, record it as a candidate scheduling scheme; for any one candidate scheduling scheme, record it as a first candidate scheme, obtain the processing time length weight corresponding to VE and the processing cost weight as VG, and record the other as NG; and calculate the weight difference degree XG of the first candidate scheme, XG = (|VE-VG|VE) + (|NE-NG|NE); repeat to obtain the weight difference degree of all candidate scheduling schemes, and record the candidate scheduling scheme with the minimum weight difference degree as the best scheduling scheme; in this embodiment, k4 = 0.8, k5 = 1.2; [k4*VE, k5*VE] ensures that the candidate scheme meets the basic requirements in the dominant dimension, for example, only consider the upstream scheme in the time length weight when time priority is considered, and improve the screening efficiency;

[0105] According to the best scheduling scheme, perform task allocation scheduling;

[0106] In the specific implementation process, XG quantifies the matching degree of the scheme and the current weight through standardized absolute deviation, for example, a scheme VE = 0.7 corresponds to VG = 0.65, NE = 0.3 corresponds to NG = 0.35, XG = (0.05 / 0.7) + (0.05 / 0.3) ≈ 0.24, the smaller the difference degree, the more suitable for the scheduling target, and the excessive deviation from the secondary dimension is avoided when only the dominant dimension is pursued; for example, when time priority is considered, the cost rationality is still considered.

[0107] Embodiment 2, please refer to Figure 2 As shown in the figure, the present application provides a virtual digital identity-based employee task dynamic scheduling control method, which includes the following steps:

[0108] Step S1, collect the task amount of each time period and the completion of each task on different dates to obtain historical task reference data; step S1 includes the following substeps:

[0109] Step S101, divide the date type into weekdays and rest days; for any one historical date, record it as a first date; obtain the date type of the first date;

[0110] Step S102, divide the first date into multiple uniform time periods at a first time interval within the first date, and record any one time period as a first time period, wherein the first time interval is t1;

[0111] Step S103, obtaining the number of tasks received by the scheduling platform in the first time period, denoted as AF; and obtaining the time length of the limited duration of each task, denoted as the limited completion duration, and obtaining the actual time length of each task, denoted as the actual completion duration, to obtain the initial task-related data of the first time period after completion;

[0112] Step S104, calculating the task density MP corresponding to the first time period according to the initial task-related data of the first time period, MP = AF / t1;

[0113] Step S105, obtaining the number of tasks in which the actual completion duration is not greater than the limited completion duration among all tasks received by the scheduling platform in the first time period, denoted as BF; and calculating the task completion rate RE corresponding to the first time period, RE = BF / AF;

[0114] Step S106, recording the task density MP and the task completion rate RE corresponding to the first time period as the first task-related data of the first time period;

[0115] Step S107, repeatedly obtaining the first task-related data of all time periods in the first date to obtain the first task reference data of the first date; and continuously repeatedly obtaining the first task reference data of each date, denoted as the historical task reference data.

[0116] Step S2, extracting a task density dynamic threshold based on the historical task reference data, and performing weight allocation processing according to the task density dynamic threshold to obtain time cost weight data; Step S2 includes the following sub-steps:

[0117] Step S201, according to the date type of the first date, obtaining k1 historical dates before the first date which have the same date type as the first date and are the closest to the first date, denoted as the reference date of the first date, and recording any one reference date as the first reference date, wherein k1 is the number of settings;

[0118] Step S202, calculating the average value of the task density of all time periods of the first reference date according to the first task reference data of the first reference date, denoted as the average task density of the first reference date;

[0119] Step S203, repeatedly obtaining the average task density of all reference dates of the first date, and sorting from far to near according to the corresponding date order, denoted as the average task density sequence; and calculating the reference task density CP0 of the first date according to the first formula, the first formula being as follows: Wherein TPi represents the i-th average task density in the average task density sequence;

[0120] Step S204, a time period in which the starting working time of the working day is located is recorded as a starting time period; according to the first task reference data of the first date, the task density MP and the task completion rate RE of all time periods in the first date are respectively sorted in time sequence starting from the starting time period, and the first task density sequence and the first task completion rate sequence are respectively obtained in sequence;

[0121] Step S205, for the starting time period of the first date, the completion rate change trend corresponding to the starting time period of the first date is calculated according to a second formula, and the second formula is as follows: , wherein AH represents the completion rate change trend; AS is a set task density sensitivity coefficient, 0<AS<1; MPi represents the i th task density in the first task density sequence; CP(i-1), if i-1=0, CP(i-1) is the reference task density CP0, if i-1>0, CP(i-1) represents the task density bearing value of the i-1 th time period of the first date;

[0122] Step S206, the task density bearing value corresponding to the starting time period of the first date is further calculated according to a third formula, and the third formula is as follows: , wherein AQ is a set density change amplitude coefficient, 0<AQ<1;

[0123] Step S207, the task density bearing values of all time periods in the first date are repeatedly calculated in sequence, and are arranged in the order of the corresponding time periods, and are recorded as the first density bearing value sequence; the weight coefficients of all time periods in the first date are set, and the sum of the weight coefficients of all time periods in the first date is ensured to be equal to 1; and the task density dynamic threshold YP of the first date is calculated according to a fourth formula, and the fourth formula is as follows: , wherein qi is the weight coefficient of the i th time period; n is the total number of time periods in the first date;

[0124] Step S208, the task density dynamic thresholds of all historical dates are repeatedly obtained according to the historical task reference data;

[0125] Step S209, for the first time period of the first date, according to the task density dynamic threshold YP of the first date and the task density MP corresponding to the first time period; if MP≥YP, the time weight TE of the first time period is set to 1, and the cost weight CE is set to 0; if MP<YP, the time weight TE of the first time period is set to MP / YP, and the cost weight CE is set to 1-MP / YP;

[0126] Step S210, the time weight and the cost weight of all time periods in the first date are repeatedly obtained, and the time cost weight information of the first date is obtained; and the time cost weight information of all historical dates is repeatedly obtained, and the time cost weight data is obtained.

[0127] Step S3, obtaining the time weight and the cost weight of the current date based on the time cost weight data, and simultaneously scheduling multiple tasks based on the auction algorithm mechanism to obtain all selectable scheduling schemes; step S3 includes the following sub-steps:

[0128] Step S301, for the current date to be regulated, denoted as the first current date; any one time period in the first current date is denoted as the first current time period;

[0129] Step S302, obtaining the date type according to the first current date, obtaining k2 historical dates before the first current date which have the same date type as the first current date and are the closest, denoted as the weight reference date of the first current date, wherein k2 is the number set;

[0130] Step S303, according to the time cost weight information of the weight reference date, obtaining the time weight and the cost weight of the time period same as the first current time period in all weight reference dates, and respectively sorting according to the corresponding date order, and respectively denoted as the time weight reference sequence and the cost weight reference sequence;

[0131] Step S304, according to the time distance of each weight reference date from the first current date, setting a weight coefficient for each weight reference date, and ensuring that the sum of the weight coefficients of all weight reference dates is equal to 1;

[0132] Step S305, further according to the weight coefficient of each weight reference date, respectively taking the weighted average value of the time weight reference sequence and the cost weight reference sequence, and respectively obtaining the average time weight and the average cost weight in order, and respectively denoted as the current time weight and the current cost weight of the first current time period;

[0133] Step S306, repeating the obtaining of the current time weight and the current cost weight of all time periods in the first current date;

[0134] Step S307, for the first current time period, obtaining the number of currently idle virtual digital employees, denoted as LA; and any one idle virtual digital employee is denoted as the first idle employee; and obtaining the number of currently to-be-scheduled tasks, denoted as LB;

[0135] Step S308, based on the auction algorithm mechanism, and according to LA and LB, simultaneously auctioning LC to-be-scheduled tasks; wherein, if LA> LB, then LC=LB, otherwise LC=LA; obtaining the estimated processing time and the estimated processing cost given by the first idle employee for each auctioned task, denoted as the processing time and the processing cost respectively, obtaining the task processing data of the first idle employee; repeating the obtaining of the task processing data of all idle virtual digital employees, denoted as the task processing cost data;

[0136] Step S309, the LC tasks to be scheduled are assigned to the LA idle virtual digital employees, and all assignment schemes are obtained, denoted as optional scheduling schemes;

[0137] Step S310, for any optional scheduling scheme, denoted as a first scheduling scheme, the sum of the estimated processing costs of all tasks in the first scheduling scheme is calculated, denoted as the processing cost of the first scheduling scheme, the estimated processing times of all tasks in the first scheduling scheme are sorted in ascending order, and the k3th percentile is obtained, denoted as the processing time of the first scheduling scheme; wherein k3 is a set percentile; the processing time and the processing cost of each optional scheduling scheme are repeatedly obtained.

[0138] Step S4, the best scheduling scheme is selected from all optional scheduling schemes according to the time weight and the cost weight of the current date for task scheduling; step S4 includes the following sub-steps:

[0139] Step S401, the processing time and the processing cost of all optional scheduling schemes are sorted in descending order to obtain a processing time sequence and a processing cost sequence;

[0140] Step S402, for the first scheduling scheme, the positions of the processing time and the processing cost of the first scheduling scheme in the corresponding processing time sequence and processing cost sequence are obtained, and are denoted as AG1 and BG1 in order respectively; and the processing time weight QG1 and the processing cost weight QG2 of the first scheduling scheme are calculated, wherein QG1= (AG1 / W0) / [(AG1 / W0)+(BG1 / W0)], QG2= (BG1 / W0) / [(AG1 / W0)+(BG1 / W0)], the processing time weights and the processing cost weights of all first scheduling schemes are repeatedly obtained, and are sorted in descending order, denoted as a time weight sequence and a cost weight sequence respectively;

[0141] Step S403, if the current time weight TE=1 and the current cost weight CE=0 in the first current period, the optional scheduling scheme with the largest processing time weight is marked as the best scheduling scheme;

[0142] Step S404, otherwise, VE and NE are obtained, VE=MAX(TE, CE), NE=MIN(TE, CE), and the time weight sequence or the cost weight sequence is denoted as the first sequence according to the weight type of VE, and the other is denoted as the second sequence;

[0143] Step S405, obtain the optional scheduling scheme located at [k4*VE, k5*VE] in the first sequence sequence, denoted as a candidate scheduling scheme; for any one candidate scheduling scheme, denoted as a first candidate scheme, obtain the processing time weight corresponding to VE as the processing cost weight, denoted as VG, and the other as NG; and calculate the weight difference degree XG of the first candidate scheme, XG= (|VE-VG|VE) + (|NE-NG|NE); repeat obtaining the weight difference degree of all candidate scheduling schemes, and record the candidate scheduling scheme with the minimum weight difference degree as the best scheduling scheme;

[0144] Step S406, task allocation scheduling according to the best scheduling scheme.

[0145] Embodiment 3, please refer to Figure 4 as shown, Figure 4 An example of a structural diagram of an electronic device, which can include: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface, the memory complete mutual communication through the communication bus. The memory stores computer readable instructions, and the processor can call the instructions in the memory, when the computer readable instructions are executed by the processor, run the steps in the employee task dynamic scheduling control method based on virtual digital identity, to realize the following functions: collect the task amount of each time period and the completion of each task of different dates, obtain historical task reference data; extract the task density dynamic threshold based on the historical task reference data, and perform weight allocation processing according to the task density dynamic threshold to obtain time cost weight data; obtain the time weight and cost weight of the current date based on the time cost weight data, and simultaneously schedule multiple tasks based on the auction algorithm mechanism to obtain all optional scheduling schemes; select the best scheduling scheme from all optional scheduling schemes according to the time weight and cost weight of the current date to perform task scheduling.

[0146] In addition, the logical instructions in the memory described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various program code storage media.

[0147] In some embodiments, the application provides a computer readable storage medium, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above method for dynamically scheduling tasks of employees based on a virtual digital identity are executed to achieve the following functions: collecting the amount of tasks in each time period of different dates and the completion of each task to obtain historical task reference data; extracting a task density dynamic threshold based on the historical task reference data, and performing weight distribution processing according to the task density dynamic threshold to obtain time cost weight data; obtaining time weight and cost weight of the current date based on the time cost weight data, and simultaneously scheduling multiple tasks based on an auction algorithm mechanism to obtain all selectable scheduling schemes; and selecting the best scheduling scheme from all selectable scheduling schemes according to the time weight and cost weight of the current date to schedule the tasks.

[0148] Through the description of the above embodiments, the embodiments of the application can be provided as a method, a system or a computer program product. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the method described in each embodiment or some parts of the embodiment.

[0149] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division, and there can be another division manner in actual implementation. For example, a plurality of modules or units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed modules can be indirect coupling or communication connection through some communication interfaces. The coupling or communication connection can be electrical, mechanical or in other forms.

[0150] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for dynamic scheduling and control of employee tasks based on virtual digital identity, characterized in that, Includes the following steps: Collect the task volume and completion status of each task for different time periods on different dates to obtain historical task reference data; Based on historical task reference data, a dynamic threshold for task density is extracted, and weight allocation is performed according to the dynamic threshold for task density to obtain time cost weight data. The time and cost weights of the current date are obtained based on the time cost weight data, and multiple tasks are simultaneously scheduled based on the auction algorithm mechanism to obtain all possible scheduling schemes. Based on the time and cost weights of the current date, the best scheduling scheme is selected from all available scheduling schemes for task scheduling; The process of obtaining the time and cost weights for the current date based on time-cost weight data, and simultaneously scheduling multiple tasks using an auction algorithm to obtain all available scheduling schemes, also includes the following sub-steps: For the first current time period, obtain the number of currently idle virtual digital employees, denoted as LA; and denote any idle virtual digital employee as the first idle employee; And obtain the number of tasks currently waiting to be scheduled, denoted as LB; Based on the auction algorithm mechanism, LC tasks to be scheduled are auctioned simultaneously according to LA and LB; where LA>LB, then LC=LB, otherwise LC=LA; the estimated processing time and estimated processing cost given by the first idle employee for each auctioned task are obtained and recorded as processing time and processing cost respectively, to obtain the task processing data of the first idle employee; the task processing data of all idle virtual digital employees are repeatedly obtained and recorded as task processing cost data. Assign LC tasks to be scheduled to LA idle virtual digital employees, obtain all assignment schemes, and denote them as optional scheduling schemes; For any available scheduling scheme, denoted as the first scheduling scheme, calculate the sum of the estimated processing costs of all tasks in the first scheduling scheme, denoted as the processing cost of the first scheduling scheme. Sort the estimated processing times of all tasks in the first scheduling scheme in ascending order and obtain the k3th percentile, denoted as the processing time of the first scheduling scheme; where k3 is the set percentile. Repeat the process to obtain the processing time and processing cost of each available scheduling scheme. Selecting the optimal scheduling scheme from all available scheduling schemes based on the time weight and cost weight of the current date for task scheduling includes the following sub-steps: Sort the processing time and processing cost of all available scheduling schemes in descending order to obtain the processing time sequence and processing cost sequence; For the first scheduling scheme, obtain the positions of the processing time and processing cost of the first scheduling scheme in the corresponding processing time sequence and processing cost sequence, and denote them as AG1 and BG1 respectively in order; and calculate the processing time weight QG1 and processing cost weight QG2 of the first scheduling scheme, where QG1=AG1 / (AG1+BG1), QG2=BG1 / (AG1+BG1). Repeat the process of obtaining the processing time weight and processing cost weight of all first scheduling schemes, and sort them according to their size, and denote them as the time weight sequence and cost weight sequence respectively. If the current time weight TE=1 and the current cost weight CE=0 for the first current time period, then the optional scheduling scheme with the largest processing time weight is marked as the best scheduling scheme. Otherwise, obtain VE and NE, VE=MAX(TE,CE), NE=MIN(TE,CE), and record either the duration weight sequence or the cost weight sequence as the first sequence according to the weight type of VE, and record the other as the second sequence; Obtain the available scheduling schemes located in [k4*VE, k5*VE] in the first sequence, and denote them as candidate scheduling schemes; for any candidate scheduling scheme, denote it as the first candidate scheme, and denote the processing time weight or processing cost weight corresponding to VE as VG, and denote the other as NG; and calculate the weight difference degree XG of the first candidate scheme, XG=(|VE-VG| / VE)+(|NE-NG| / NE); repeatedly obtain the weight difference degree of all candidate scheduling schemes, and denote the candidate scheduling scheme with the smallest weight difference degree as the best scheduling scheme; Tasks are allocated and scheduled according to the optimal scheduling scheme.

2. The employee task dynamic scheduling and control method based on virtual digital identity according to claim 1, characterized in that, Collecting task volume and completion status for each time period on different dates to obtain historical task reference data includes the following sub-steps: Divide the date type into weekdays and rest days; for any historical date, denote it as the first date; obtain the date type of the first date; The first date is divided into multiple uniform time periods within the first date, and any one of these time periods is designated as the first time period, where the first time interval is t1. Get the number of tasks received by the scheduling platform within the first time period, denoted as AF; get the time limit for each task, denoted as the limited completion time; get the actual time taken to complete each task, denoted as the actual completion time; and obtain the initial task-related data for the first time period after completion.

3. The employee task dynamic scheduling and control method based on virtual digital identity according to claim 2, characterized in that, Collecting task volume and completion status for each time period on different dates to obtain historical task reference data also includes the following sub-steps: Based on the initial task-related data of the first time period, calculate the task density MP corresponding to the first time period, MP=AF / t1; The task completion rate (RE) for the first time period is calculated as RE = BF / AF. The task number of tasks received by the scheduling platform within the first time period is denoted as BF. The task density MP and task completion rate RE corresponding to the first time period are recorded as the first task-related data for the first time period. Repeatedly retrieve the first task-related data for all time periods within the first date to obtain the first task reference data for the first date; The system continuously retrieves the first task reference data for each date and records it as historical task reference data.

4. The employee task dynamic scheduling and control method based on virtual digital identity according to claim 3, characterized in that, Extracting a dynamic threshold for task density based on historical task reference data, and then performing weight allocation processing based on the dynamic threshold for task density to obtain time cost weight data includes the following sub-steps: Obtain the k1 historical dates that are the same as the date type of the first date and the most recent before the first date according to the date type of the first date, and record them as the reference dates of the first date. Denote any one of the reference dates as the first reference date, where k1 is the set number; Calculate the average of the task densities of all time periods of the first reference date according to the first task reference data of the first reference date, and record it as the average task density of the first reference date; Repeatedly obtain the average task density of all baseline reference dates for the first date, and sort them from oldest to newest according to their corresponding date order, denoted as the average task density sequence; then calculate the baseline task density CP0 for the first date according to the first formula, which is as follows: , where TPi represents the i-th average task density in the average task density sequence.

5. The employee task dynamic scheduling and control method based on virtual digital identity according to claim 4, characterized in that, Extracting the dynamic threshold of task density based on historical task reference data and performing weight allocation processing according to the dynamic threshold of task density to obtain the time-cost weight data further includes the following sub-steps: Denote the time period where the starting working time of a working day is located as the starting time period; according to the first task reference data of the first date, sort the task densities MP and task completion rates RE of all time periods within the first date in chronological order starting from the starting time period, and obtain the first task density sequence and the first task completion rate sequence in order; For the starting time period of the first date, calculate the completion rate change trend corresponding to the starting time period of the first date according to the second formula, and the second formula is as follows: , where AH represents the completion rate change trend; AS is the set task density sensitivity coefficient, 0 < AS < 1; MPi represents the i-th task density in the first task density sequence; CP(i - 1), if i - 1 = 0, then CP(i - 1) is the reference task density CP0, if i - 1 > 0, then CP(i - 1) represents the task density carrying value of the (i - 1)-th time period of the first date; Then, calculate the task density carrying capacity value corresponding to the starting time period of the first date according to the third formula, which is as follows: Where AQ is the set density variation amplitude coefficient, 0 <AQ<1; The task density carrying capacity value for all time periods within the first date is calculated repeatedly and arranged in the order of the corresponding time periods, denoted as the first density carrying capacity value sequence; weight coefficients are set for all time periods within the first date, ensuring that the sum of the weight coefficients for all time periods within the first date equals 1; and the dynamic threshold YP of the task density for the first date is calculated according to the fourth formula, which is as follows: , where qi is the weight coefficient of the i-th time period; n is the total number of time periods within the first date; Repeatedly obtain the dynamic thresholds of task density for all historical dates according to historical task reference data.

6. The employee task dynamic scheduling and control method based on virtual digital identity according to claim 5, characterized in that, Extracting the dynamic threshold of task density based on historical task reference data and performing weight allocation processing according to the dynamic threshold of task density to obtain the time-cost weight data further includes the following sub-steps: For the first time period of the first date, according to the dynamic threshold of task density YP of the first date and the task density MP corresponding to the first time period; if MP ≥ YP, set the time weight TE of the first time period to 1 and the cost weight CE to 0; if MP < YP, set the time weight TE of the first time period to MP / YP and the cost weight CE to 1 - MP / YP; Repeatedly obtain the time weights and cost weights of all time periods within the first date to obtain the time-cost weight information of the first date; and repeatedly obtain the time-cost weight information of all historical dates to obtain the time-cost weight data.

7. The employee task dynamic scheduling and control method based on virtual digital identity according to claim 6, characterized in that, Obtain the time weight and cost weight of the current date based on the time-cost weight data, and simultaneously schedule multiple tasks based on the auction algorithm mechanism to obtain all optional scheduling plans, which further includes the following sub-steps: For the current date to be regulated, denote it as the first current date; denote any time period within the first current date as the first current time period; Obtain the k2 historical dates that are the same as the date type of the first current date and the most recent before the first current date according to the date type of the first current date, and record them as the weight reference dates of the first current date, where k2 is the set number; According to the time-cost weight information of the weight reference dates, obtain the time weights and cost weights of the time periods that are the same as the first current time period among all weight reference dates, and sort them in the corresponding date order respectively, and record them as the time weight reference sequence and the cost weight reference sequence in order; Based on the time distance between each weight reference date and the first current date, a weight coefficient is set for each weight reference date, and the sum of the weight coefficients of all weight reference dates is guaranteed to be equal to 1; Then, based on the weight coefficient of each weight reference date, calculate the weighted average of the time weight reference sequence and the cost weight reference sequence, and obtain the average time weight and average cost weight in order, and mark them in order as the current time weight and current cost weight of the first current period. Repeatedly obtain the current time weight and current cost weight for all time periods within the first current date.

8. A dynamic scheduling and control system for employee tasks based on virtual digital identity, used to implement the dynamic scheduling and control method for employee tasks based on virtual digital identity as described in any one of claims 1-7, characterized in that, It includes a data acquisition module, a threshold weighting module, a scheduling preset module, and a task scheduling module; The data acquisition module is used to collect the task volume and completion status of each task for different time periods on different dates to obtain historical task reference data. The threshold weight module includes a threshold extraction unit and a weight allocation unit. The threshold extraction unit extracts a dynamic threshold for task density based on historical task reference data, and the weight allocation unit performs weight allocation processing based on the dynamic threshold for task density to obtain time cost weight data. The scheduling preset module obtains the time weight and cost weight of the current date based on the time cost weight data, and simultaneously schedules multiple tasks based on the auction algorithm mechanism to obtain all available scheduling schemes. The task scheduling module selects the best scheduling scheme from all available scheduling schemes based on the time weight and cost weight of the current date for task scheduling.

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