Traditional Chinese medicine dispensing task allocation method and system

Abstract

The invention discloses a traditional Chinese medicine dispensing task allocation method and system, relates to the technical field of traditional Chinese medicine prescription filling, and solves the problems that in the prior art, due to sequential alternate allocation according to the number of traditional Chinese medicine dispensing tasks, working groups are busy and idle unevenly, and the prescription filling efficiency is low, the method comprises the steps that dispensing tasks are generated according to traditional Chinese medicine prescriptions to be dispensed; and allocating the dispensing task to the working group with the shortest cumulative duration on the same day, predicting the predicted working duration of the working group for processing the dispensing task, accumulating the predicted working duration to the cumulative duration on the same day of the working group, and repeating the steps, so that a traditional Chinese medicine prescription can be obtained from an HIS system through the HIS system accessed to a hospital, and the dispensing task can be dispensed. And the dispensing task is automatically generated, and the dispensing task is allocated to the working group with the shortest accumulated time length on the current day, so that the task allocation is fair and reasonable, and the efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of Chinese medicine preparation, and in particular to a method and system for allocating Chinese medicine preparation tasks. Background Art

[0002] At present, the prescription adjustment task in Chinese medicine pharmacies is allocated in sequence according to the prescription. Since there is no quantifiable processing and results, the adjustment task allocation can only be allocated in turn by person or group, and manual distribution is required. As a result, prescriptions are overly concentrated on staff or working groups due to the difficulty of adjustment or the number of patches or flavors, resulting in uneven workload distribution, resulting in uneven busyness of adjustment personnel or groups, affecting work progress and adjustment efficiency. If manual intervention in the adjustment task is carried out due to the lack of quantified workload, it is easy to cause opposition and resistance from the working group.

[0003] Although there are some systems that can automatically assign prescription adjustment tasks, the allocation is done through system software intervention, and it is also allocated to each working group according to the number of prescriptions. It is fixed and mechanically allocated in turn. The specific prescription adjustment work is not quantified, and the difference in workload of different prescription adjustments is not resolved. It can only be managed roughly according to the number of prescriptions. As a result, some working groups assigned to multiple prescription adjustment tasks with large workloads are very busy, while working groups assigned to multiple prescription adjustment tasks with small workloads are very idle, making the processing cycle of prescription adjustment tasks with large workloads very long, which not only leads to uneven workload among working groups, but also affects work progress and efficiency in obtaining medicines, causing some customers to wait a long time before getting their medicines, seriously affecting their experience in getting medicines. Summary of the invention

[0004] The purpose of this application is to overcome the problems of uneven workload and low efficiency in obtaining medicines caused by the rotation of tasks according to the number of Chinese medicine dispensing tasks in the prior art, and to provide a method and system for allocating Chinese medicine dispensing tasks.

[0005] In a first aspect, a method for allocating tasks of traditional Chinese medicine dispensing is provided, comprising:

[0006] Obtain prescriptions of Chinese medicine to be dispensed;

[0007] Generate a dispensing task based on the Chinese medicine prescription to be dispensed;

[0008] Allocate the adjustment task to the working group with the shortest cumulative working time on that day, wherein, if the number of working groups with the shortest cumulative working time on that day is greater than 1, then allocate the adjustment task to the working group with the highest preset priority among the working groups with the shortest cumulative working time on that day;

[0009] Using a pre-trained adjustment duration prediction model to predict the estimated working time of the working group in processing the adjustment task;

[0010] The estimated working time is added to the cumulative working time of the working group on the day.

[0011] In some possible implementations, the Chinese medicine prescription includes: medicine name, number of doses, number of flavors, number of packages, number of unpacked packages, and number of additional packages.

[0012] In some possible implementations, the method for setting the preset priority includes: sorting the work groups, determining the work group with the highest priority for the day, and sorting the priorities of other work groups after the work group with the highest priority for the day.

[0013] In some possible implementations, the method for determining the work group with the highest priority for the day includes: rotating the work group with the highest priority for the day on a daily basis according to the sorting of the work groups, or determining the work group with the same sorting number as the remainder obtained by dividing the date of the day by the number of work groups as the work group with the highest priority for the day.

[0014] In some possible implementations, the training method of the adjustment duration prediction model includes:

[0015] Create an adjustment time prediction model, where the input of the adjustment time prediction model includes the number of work group members, the number of packages, the number of unpacked packages, and the number of additional packages. The output of the adjustment time prediction model includes the estimated working time. The calculation formula for creating the adjustment time prediction model is:

[0016] T=X1*V1*C+Q+X2*V2+X3*V3-X4G;

[0017] In the formula, T is the expected working time, X1 is the number of packets, V1 is the packet capture speed, C is the coefficient, Q is the variable, X2 is the number of unpacked packets, V2 is the unpacking speed, X3 is the number of additional packets, V3 is the additional packet speed, X4 is the number of working groups, and G is the public time. Among them, C, Q, V2, V3 and G all change with the change of X1, and V1 changes with the change of the number of packets and the number of working groups;

[0018] Obtain historical data, including the number of people in the work group, the number of packages, the number of unpacked packages, the number of repacked packages, and the actual working hours;

[0019] The parameters of the adjustment time prediction model are adjusted using the historical data until the difference between the expected working time and the actual working time in the historical data is less than the preset time and the proportion reaches a preset ratio.

[0020] In some possible implementations, if an adjustment task is revoked after being assigned to a working group, the estimated working hours of the revoked adjustment task will be deducted from the accumulated working hours of the working group on that day.

[0021] In a second aspect, a Chinese medicine dispensing task management system is provided, comprising:

[0022] An acquisition module is used to acquire the Chinese medicine prescription to be dispensed;

[0023] A task generation module, used to generate a dispensing task according to a Chinese medicine prescription to be dispensed;

[0024] An allocation module, used to allocate the adjustment task to the working group with the shortest cumulative working time on that day, wherein, if the number of working groups with the shortest cumulative working time on that day is greater than 1, the adjustment task is allocated to the working group with the highest preset priority among the working groups with the shortest cumulative working time on that day;

[0025] A duration prediction module, used to predict the estimated working time of the working group for processing the adjustment task by using a pre-trained adjustment duration prediction model;

[0026] The duration accumulation module is used to accumulate the estimated working duration into the cumulative duration of the working group on the day.

[0027] In a further embodiment, it further includes: a model training module, the model training module including:

[0028] A creation module is used to create an adjustment time prediction model, wherein the input of the adjustment time prediction model includes the number of working group members, the number of packages, the number of unpacked packages, and the number of additional packages. The output of the adjustment time prediction model includes the estimated working time. The calculation formula for creating the adjustment time prediction model is:

[0029] T=X1*V1*C+Q+X2*V2+X3*V3-X4G;

[0030] In the formula, T is the expected working time, X1 is the number of packets, V1 is the packet capture speed, C is the coefficient, Q is the variable, X2 is the number of unpacked packets, V2 is the unpacking speed, X3 is the number of additional packets, V3 is the additional packet speed, X4 is the number of working groups, and G is the public time. Among them, C, Q, V2, V3 and G all change with the change of X1, and V1 changes with the change of the number of packets and the number of working groups;

[0031] The data acquisition module acquires historical data, wherein the historical data includes the number of people in the work group, the number of packages, the number of unpacked packages, the number of repacked packages and the actual working time;

[0032] The parameter adjustment module is used to adjust the parameters of the adjustment time prediction model using the historical data until the difference between the expected working time and the actual working time in the historical data is less than the preset time and the proportion reaches a preset ratio.

[0033] In a third aspect, a computer-readable storage medium is provided, wherein the computer-readable medium stores a program code for execution by a device, wherein the program code includes steps for executing the method in any one of the implementations of the first aspect described above.

[0034] In a fourth aspect, an electronic device is provided, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements a method as in any one of the implementations in the first aspect above.

[0035] The present application has the following beneficial effects: it can access the hospital's HIS system, obtain Chinese medicine prescriptions from the HIS system, automatically generate dispensing tasks, and assign the dispensing tasks to the working group with the shortest cumulative time on the current day, wherein the pre-trained dispensing time prediction model can be used to predict the time required for the working group to handle the dispensing task and add it to the working group's cumulative time on the current day; when a new task is assigned, it is assigned to the group with the shortest cumulative estimated time at the current moment, thereby achieving fairness and rationality in task allocation, improving efficiency, and effectively reducing the average waiting time for patients to get Chinese medicine. At the same time, it cancels the prescription distribution positions and avoids the situations in which label posting errors, repeated task allocation, and assignment tasks that do not match signs and identifications that are prone to occur during manual distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application.

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 is a flow chart of the Chinese medicine dispensing task allocation method of Example 1 of the present application;

[0039] Figure 2 This is an example diagram of the parameters of the pre-trained preparation duration prediction model in the method for allocating tasks for traditional Chinese medicine preparation in Example 1 of the present application;

[0040] Figure 3 This is an example diagram of a Chinese medicine prescription in the Chinese medicine dispensing task allocation method of Example 1 of the present application;

[0041] Figure 4It is a structural block diagram of the Chinese medicine dispensing task management system of Example 2 of the present application;

[0042] Figure 5 It is a schematic diagram of the internal structure of the electronic device of Example 4 of the present application.

[0043] Reference numerals:

[0044] 100, acquisition module; 200, task generation module; 300, allocation module; 400, duration prediction module; 500, duration accumulation module. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Example 1

[0047] like Figure 1 As shown, a method for allocating tasks of traditional Chinese medicine preparation involved in Example 1 of the present application includes:

[0048] S100, by accessing the HIS system of the hospital, obtaining the Chinese medicine prescription to be dispensed from the HIS system, wherein the Chinese medicine prescription includes: medicine name, number of doses, number of flavors, number of packages, number of unpacked packages, number of additional packages, etc.;

[0049] S200, generating a dispensing task according to the Chinese medicine prescription to be dispensed, wherein one dispensing task is generated for one patient's Chinese medicine prescription;

[0050] S300, assigning the adjustment task to the working group with the shortest cumulative duration on that day, wherein, if the number of working groups with the shortest cumulative duration on that day is greater than 1, assigning the adjustment task to the working group with the highest preset priority among the working groups with the shortest cumulative duration on that day, for example: at the beginning of each day, the cumulative duration of each working group on that day is 0, or there are at least two groups with the same and shortest cumulative duration, in this case, in order to select a working group from the working groups with the shortest cumulative duration on that day to assign the adjustment task, this is achieved by setting the priority of the working groups;

[0051] Specifically, the method for setting the preset priority includes: sorting the working groups, determining the working group with the highest priority for the day, and the priority order of other working groups is arranged behind the working group with the highest priority for the day. The method for determining the working group with the highest priority for the day includes: taking turns as the working group with the highest priority for the day on a daily basis according to the sorting of the working groups, or taking the working group with the same sorting number as the remainder obtained by dividing the date of the day by the number of working groups as the working group with the highest priority for the day, which can ensure the fairness and balance of the working group as the group with the highest priority as much as possible.

[0052] As shown in Table 1, at the beginning of each day, the cumulative working time of each group is 0. Therefore, it is necessary to find the first group (that is, the working group that handles the first adjustment task every day) and the order of the first allocation of adjustment tasks for each subsequent working group according to the priority. Assuming that there are four groups, the working group with the same sorting number as the remainder obtained by dividing the date of the day by the number of working groups is the working group with the highest priority on that day. If the remainder is 0, the fourth group is the working group with the highest priority on that day. For example: on the 1st of each month, the remainder when divided by 4 is 1, then the priority of the working group is: Group 1>Group 2>Group 3>Group 4; on the 2nd of each month, the remainder when divided by 4 is 2, then the priority of the working group is: Group 2>Group 3>Group 4>Group 1; on the 3rd of each month, the remainder when divided by 4 is 3, then the priority of the working group is: Group 3>Group 4>Group 1>Group 2; on the 4th of each month, the remainder when divided by 4 is 0, then the priority of the working group is: Group 4>Group 1>Group 2>Group 3, and so on.

[0053] Table 1:

[0054] Serial number date Remainder after division by 4 1 group 2 groups 3 groups 4 groups First opening group 1 1st 1 1 2 3 4 1 group 2 2nd 2 4 1 2 3 2 groups 3 3rd 3 3 4 1 2 3 groups 4 4th 0 2 3 4 1 4 groups 5 5th 1 1 2 3 4 1 group 6 6th 2 4 1 2 3 2 groups 7 7th 3 3 4 1 2 3 groups 8 8th 0 2 3 4 1 4 groups 9 9th 1 1 2 3 4 1 group ... ... ... ... ... ... ... ... 30 30th 2 4 1 2 3 2 groups

[0055] S400, using a pre-trained adjustment duration prediction model to predict the estimated working time of the working group in processing the adjustment task;

[0056] The training method of the adjustment duration prediction model includes:

[0057] Create an adjustment time prediction model, where the input of the adjustment time prediction model includes the number of work group members, the number of packages, the number of unpacked packages, and the number of additional packages. The output of the adjustment time prediction model includes the estimated working time. The calculation formula for creating the adjustment time prediction model is:

[0058] T=X1*V1*C+Q+X2*V2+X3*V3-X4G;

[0059] In the formula, T is the expected working time, X1 is the number of packets, V1 is the packet capture speed, C is the coefficient, Q is the variable, X2 is the number of unpacked packets, V2 is the unpacking speed, X3 is the number of additional packets, V3 is the additional packet speed, X4 is the number of working groups, and G is the public time. Among them, C, Q, V2, V3 and G all change with the change of X1, and V1 changes with the change of the number of packets and the number of working groups;

[0060] Obtain historical data, including the number of people in the work group, the number of packages, the number of unpacked packages, the number of repacked packages, and the actual working hours;

[0061] The parameters of the adjustment time prediction model are adjusted using the historical data until the difference between the expected working time and the actual working time in the historical data is less than the preset time and the proportion reaches a preset ratio.

[0062] It should be noted that the actual operation time is collected and recorded, and the large difference in time is compared and analyzed to find out the cause and determine whether it is a routine situation in daily work. The method of calculating time is continuously optimized and improved by using the continuous self-learning function to make it as close to the actual situation as possible.

[0063] Taking into account the actual situation, in some time periods, the reviewers need to take into account the review tasks of two or even three groups. The number of people in each group may not be an integer. The coefficient is used for adjustment. The actual situation of many applications is different. For example, the setting of the warehouse, the location and height of the workbench and warehouse will affect the adjustment time. There may be a big difference in the calculation of the estimated time at the beginning. But the application is not affected because the standards for the degree of leniency or severity of each group are the same. For example, the actual time is added with a coefficient, and each working group has the same standard. In order to improve the accuracy of the estimated working time as much as possible, the parameters of the estimated time can include: number of doses, number of flavors, number of packages, number of unpacking, number of additional packages, review, re-weighing, etc. The more parameters of the estimated time, the more conducive to improving the accuracy of the estimated working time.

[0064] Exemplary: Figure 2 As shown in, it is the instance value of each parameter in the adjustment time prediction model, such as Figure 3 Shown is a patient's traditional Chinese medicine prescription, 14 prescriptions with 10 kinds of traditional Chinese medicine, each prescription contains 7 packages of medicine, 1 kind of medicine is packed separately, and 5 kinds of medicine are unpacked, that is, 1 kind of medicine is packed separately (centipede), and 5 kinds of medicine are unpacked (Epimedium, Taxus chinensis, Phragmites australis, Poria cocos, Polygonum cuspidatum, Angelica dahurica).

[0065] from Figure 2 and Figure 3It can be seen that the parameters of the adjustment time prediction model, the total number of small packages is 98, the total number of packages is between 0-101, take the last row, if each group of adjustment personnel is 4 people, the vertical intersection of 4 people and the last row is 1.2, that is, when the number of small packages is 1 second per package, the length coefficient is 1.2. The total small package adjustment estimated time calculation: X1*V1*C=98 packages*1 second / package*1.2 coefficient=117.6 seconds.

[0066] Fixed increase or decrease quantity: Q=120 seconds.

[0067] Unpacking time: X2*V2=1 type*20 seconds / type=20 seconds.

[0068] Additional package duration: X3*V 3= 5 types * 20 seconds / type = 100 seconds.

[0069] The common time is 5 seconds, which is multiplied by 4 working groups: X4G=4*5 seconds=20 seconds, and this value is subtracted.

[0070] That is, the total estimated duration is: T=X1*V1*C+Q+X2*V2+X3*V3-X4G=117.6 seconds+120 seconds+20 seconds+100 seconds-20 seconds=337.6 seconds.

[0071] S500: Add the estimated working time to the cumulative working time of the working group on the day.

[0072] For example, if the current adjustment task is assigned to Group 1, then the cumulative working time of Group 1 on that day plus the estimated working time plus 337.6 seconds, and so on. When there is a new adjustment task, the adjustment task will continue to be assigned to the working group with the shortest cumulative working time on that day and the estimated working time for the working group to handle the adjustment task will be predicted.

[0073] In this embodiment, the estimated completion time can be calculated according to the number of doses, flavors, packages, unpacking, and other packages of the prescription, and the working group accumulates the estimated time according to the assigned tasks. The new task is assigned to the working group with the shortest cumulative estimated time on the day from the current time, thereby achieving fairness and rationality in task allocation, improving efficiency, and at the same time improving the service level and capacity of the hospital, thereby reducing the average waiting time for patients to get medicine. The method also eliminates the positions of prescription distribution and avoids the occurrence of label posting errors, repeated task allocation, and inconsistent distribution of assigned tasks with signs and identification in the manual distribution process.

[0074] Example 2

[0075] like Figure 4 As shown, a Chinese medicine dispensing task management system involved in Example 2 of the present application includes:

[0076] The acquisition module 100 is used to acquire the Chinese medicine prescription to be adjusted;

[0077] The task generation module 200 is used to generate a dispensing task according to the Chinese medicine prescription to be dispensed;

[0078] The allocation module 300 is used to allocate the adjustment task to the working group with the shortest cumulative working time on that day, wherein, if the number of working groups with the shortest cumulative working time on that day is greater than 1, the adjustment task is allocated to the working group with the highest preset priority among the working groups with the shortest cumulative working time on that day;

[0079] The duration prediction module 400 is used to predict the estimated working time of the working group to process the adjustment task by using the pre-trained adjustment duration prediction model;

[0080] The duration accumulation module 500 is used to accumulate the estimated working duration to the cumulative duration of the working group on the day;

[0081] The model training module 600 includes:

[0082] A creation module is used to create an adjustment time prediction model, wherein the input of the adjustment time prediction model includes the number of working group members, the number of packages, the number of unpacked packages, and the number of additional packages. The output of the adjustment time prediction model includes the estimated working time. The calculation formula for creating the adjustment time prediction model is:

[0083] T=X1*V1*C+Q+X2*V2+X3*V3-X4G;

[0084] In the formula, T is the expected working time, X1 is the number of packets, V1 is the packet capture speed, C is the coefficient, Q is the variable, X2 is the number of unpacked packets, V2 is the unpacking speed, X3 is the number of additional packets, V3 is the additional packet speed, X4 is the number of working groups, and G is the public time. Among them, C, Q, V2, V3 and G all change with the change of X1, and V1 changes with the change of the number of packets and the number of working groups;

[0085] The data acquisition module acquires historical data, wherein the historical data includes the number of people in the work group, the number of packages, the number of unpacked packages, the number of repacked packages and the actual working time;

[0086] The parameter adjustment module is used to adjust the parameters of the adjustment time prediction model using the historical data until the difference between the expected working time and the actual working time in the historical data is less than the preset time and the proportion reaches a preset ratio.

[0087] It should be noted that other specific implementation methods of the Chinese medicine dispensing task management system in this embodiment can refer to the specific implementation methods of the above-mentioned Chinese medicine dispensing task allocation method. In order to avoid redundancy, they will not be repeated here.

[0088] Example 3

[0089] A computer-readable storage medium according to Embodiment 3 of the present application, wherein the computer-readable storage medium stores a program code for execution by a device, wherein the program code includes steps for executing a method in any one of the implementations in Embodiment 1 of the present application;

[0090] Among them, the computer-readable storage medium can be a read-only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM); the computer-readable storage medium can store program code, and when the program stored in the computer-readable storage medium is executed by the processor, the processor is used to execute the steps of the method in any one of the implementation methods in Example 1 of the present application.

[0091] Example 4

[0092] like Figure 5 As shown, an electronic device involved in Embodiment 4 of the present application includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, and when the program or instruction is executed by the processor, a method in any one of the implementations in Embodiment 1 of the present application is implemented;

[0093] Among them, the processor can adopt a general-purpose central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), a graphics processing unit (GPU) or one or more integrated circuits to execute relevant programs to implement the method in any one of the implementation methods in Example 1 of the present application.

[0094] The processor may also be an integrated circuit electronic device with signal processing capability. In the implementation process, each step of the method in any implementation of Embodiment 1 of the present application may be completed by an integrated logic circuit of hardware in the processor or by instructions in software form.

[0095] The above-mentioned processor may also be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware decoding processor, or may be executed by a combination of hardware and software modules in a decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory, and completes the functions required to be performed by the unit included in the data processing device of the embodiment of the present application in combination with its hardware, or executes the method in any one of the implementation modes in the embodiment 1 of the present application.

[0096] The above are only preferred specific implementations of the present application; however, the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and its improved ideas of the present application within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for allocating tasks for traditional Chinese medicine preparation, characterized in that: include: Obtain prescriptions of Chinese medicine to be dispensed; Generate a dispensing task based on the Chinese medicine prescription to be dispensed; Allocate the adjustment task to the working group with the shortest cumulative working time on that day, wherein, if the number of working groups with the shortest cumulative working time on that day is greater than 1, then allocate the adjustment task to the working group with the highest preset priority among the working groups with the shortest cumulative working time on that day; Using a pre-trained adjustment duration prediction model to predict the estimated working time of the working group in processing the adjustment task; The estimated working time is added to the cumulative working time of the working group on the day.

2. The method for allocating Chinese medicine preparation tasks according to claim 1, characterized in that: The Chinese medicine prescription includes: medicine name, number of doses, number of flavors, number of packages, number of unpacked packages and number of additional packages.

3. The Chinese medicine preparation task allocation method according to claim 1, characterized in that: The method for setting the preset priority includes: sorting the working groups, determining the working group with the highest priority on the day, and sorting the priorities of other working groups after the working group with the highest priority on the day.

4. The method for allocating Chinese medicine preparation tasks according to claim 3, characterized in that: The method for determining the highest priority working group for the day includes: rotating the working group with the highest priority for the day in units of days according to the sorting of the working groups, or determining the working group with the same sorting number as the remainder obtained by dividing the date of the day by the number of working groups as the highest priority working group for the day.

5. The method for allocating Chinese medicine preparation tasks according to any one of claims 1 to 4, characterized in that: The training method of the adjustment duration prediction model includes: Create an adjustment time prediction model, where the input of the adjustment time prediction model includes the number of work group members, the number of packages, the number of unpacked packages, and the number of additional packages. The output of the adjustment time prediction model includes the estimated working time. The calculation formula for creating the adjustment time prediction model is: T=X1*V1*C+Q+X2*V2+X3*V3-X4G; In the formula, T is the expected working time, X1 is the number of packets, V1 is the packet capture speed, C is the coefficient, Q is the variable, X2 is the number of unpacked packets, V2 is the unpacking speed, X3 is the number of additional packets, V3 is the additional packet speed, X4 is the number of working groups, and G is the public time. Among them, C, Q, V2, V3 and G all change with the change of X1, and V1 changes with the change of the number of packets and the number of working groups; Obtain historical data, including the number of people in the work group, the number of packages, the number of unpacked packages, the number of repacked packages, and the actual working hours; The parameters of the adjustment time prediction model are adjusted using the historical data until the difference between the expected working time and the actual working time in the historical data is less than the preset time and the proportion reaches a preset ratio.

6. The method for allocating Chinese medicine preparation tasks according to claim 5, characterized in that: If an adjustment task is assigned to a working group and then cancelled, the estimated working hours of the cancelled adjustment task will be deducted from the working group's accumulated working hours for the day.

7. A Chinese medicine dispensing task management system, characterized in that: include: An acquisition module is used to acquire the Chinese medicine prescription to be dispensed; A task generation module, used to generate a dispensing task according to a Chinese medicine prescription to be dispensed; An allocation module, used to allocate the adjustment task to the working group with the shortest cumulative working time on that day, wherein, if the number of working groups with the shortest cumulative working time on that day is greater than 1, the adjustment task is allocated to the working group with the highest preset priority among the working groups with the shortest cumulative working time on that day; A duration prediction module, used to predict the estimated working time of the working group for processing the adjustment task by using a pre-trained adjustment duration prediction model; The duration accumulation module is used to accumulate the estimated working duration into the cumulative duration of the working group on the day.

8. The Chinese medicine dispensing task management system according to claim 7, characterized in that: Also includes: A model training module, wherein the model training module comprises: A creation module is used to create an adjustment time prediction model, wherein the input of the adjustment time prediction model includes the number of working group members, the number of packages, the number of unpacked packages, and the number of additional packages. The output of the adjustment time prediction model includes the estimated working time. The calculation formula for creating the adjustment time prediction model is: T=X1*V1*C+Q+X2*V2+X3*V3-X4G; In the formula, T is the expected working time, X1 is the number of packets, V1 is the packet capture speed, C is the coefficient, Q is the variable, X2 is the number of unpacked packets, V2 is the unpacking speed, X3 is the number of additional packets, V3 is the additional packet speed, X4 is the number of working groups, and G is the public time. Among them, C, Q, V2, V3 and G all change with the change of X1, and V1 changes with the change of the number of packets and the number of working groups; The data acquisition module acquires historical data, wherein the historical data includes the number of people in the work group, the number of packages, the number of unpacked packages, the number of repacked packages and the actual working time; The parameter adjustment module is used to adjust the parameters of the adjustment time prediction model using the historical data until the difference between the expected working time and the actual working time in the historical data is less than the preset time and the proportion reaches a preset ratio.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program code for execution by a device, wherein the program code includes steps for executing the method according to any one of claims 1 to 6.

10. An electronic device, characterized in that: The electronic device comprises a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction implements the method as claimed in any one of claims 1 to 6 when executed by the processor.

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