Scheduling method, storage medium and electronic equipment

Abstract

The invention discloses a scheduling method, a storage medium and electronic equipment. The scheduling method comprises the following steps: acquiring a predicted order quantity in a preset time period; obtaining a pre-scheduling result based on the predicted order quantity, the historical scheduling condition, the employee reported working hours and the actual productivity of the employees; judging whether the pre-arrangement result meets the working hours reported by the employees or not; if yes, performing class table filling on a pre-scheduling result to generate a scheduling table; otherwise, after the employee shift information in the pre-scheduling result is adjusted, performing shift table filling to generate a shift scheduling table; by adopting the technology, the labor cost is optimized, and excessive configuration or insufficiency of manpower is avoided; according to the method, the generation of the scheduling table is optimally balanced in multiple aspects such as order quantity, actual productivity of employees, compliance and cost, the scheduling efficiency is greatly improved, enterprise management is promoted to be converted from experience driving to data driving, and the stability and flexibility of enterprise operation are guaranteed.

Description

Technical Field

[0001] The present application relates to the field of office automation technology, and in particular to a shift scheduling method, a storage medium and an electronic device. Background Art

[0002] In the order-driven e-commerce operation model, efficient and accurate scheduling management plays a vital role in the sustainable development and competitiveness improvement of enterprises. Especially for enterprises that distribute many different types of products, the order volume presents a complex and changeable trend. The traditional existing scheduling method mostly relies on manual experience to make arrangements. This method exposes many obvious defects when facing fluctuations in order volume. On the one hand, when the order volume suddenly increases, the existing scheduling often cannot allocate enough human resources in time to meet the needs of positions such as distribution and picking. For example, during special periods such as e-commerce shopping festivals and holiday promotions, the order volume of enterprises may increase several times or even dozens of times, but manual scheduling lacks accurate prediction of order trends and rapid adjustment mechanisms, making it difficult to quickly arrange more employees to work overtime or increase shifts, resulting in order backlogs, seriously affecting customer satisfaction and corporate reputation. On the other hand, when the order volume decreases, manual scheduling is difficult to flexibly cut unnecessary working hours, resulting in a waste of labor costs and eroding the profit margins of enterprises.

[0003] At the same time, the existing shift schedule is also extremely inconvenient to adjust. If you want to change the scheduled shifts, such as when employees suddenly take leave or equipment failures require temporary staff deployment, manual scheduling takes a lot of time and effort. Managers need to check employees' work arrangements, available time, skill matching and other information one by one. The process is cumbersome and prone to errors. Moreover, due to the lack of systematic planning and support from automated tools, the adjusted schedule may not be able to take into account multiple factors such as order volume demand, employee workload and corporate cost control, further exacerbating the difficulty of corporate operations management. Summary of the invention

[0004] In view of the above problems, the present application provides a scheduling method to solve the problem that the existing scheduling cannot meet the order volume and is inconvenient to adjust.

[0005] To achieve the above object, the inventor provides a scheduling method, which includes the following steps: Get the predicted order volume within a preset time period; Obtain scheduling results based on predicted order volume, historical shift scheduling, employee reported working hours, and employee actual capacity; Determine whether the pre-scheduling result satisfies the working hours reported by the employees; if so, fill in the shift table with the pre-scheduling result to generate the shift schedule; otherwise, adjust the employee shift information in the pre-scheduling result, and fill in the shift table to generate the shift schedule.

[0006] Furthermore, the predicted order quantity includes system predicted quantity, middle platform predicted quantity, and store manager predicted quantity.

[0007] Furthermore, the store manager's forecast is based on the actual order volume in the past, the weather forecast within a preset time period, and the order events within the preset time period to estimate the order volume.

[0008] Furthermore, before the step of determining whether the pre-scheduling result meets the requirements, it also includes determining whether the predicted capacity satisfaction rate of each pre-scheduling result meets the standard. If so, it is determined whether the pre-scheduling result meets the working hours reported by the employees.

[0009] Further, the step of judging whether the predicted capacity satisfaction rate of each pre-scheduling result meets the standard comprises the following steps: Calculate the total capacity of the pre-scheduling results; Then the capacity fulfillment rate = total capacity of the pre-scheduled results / predicted order quantity * 100% Determine whether the capacity satisfaction rate exceeds the preset capacity satisfaction rate. If so, it meets the standard.

[0010] Furthermore, after adjusting the employee shift information in the pre-scheduling result, in the step of filling the shift table to generate the shift schedule, the adjustment of the employee shift information in the pre-scheduling result is performed by operating on a visual interface.

[0011] Furthermore, after the step of generating the shift schedule, the method also includes displaying the prediction analysis results of the corresponding shift schedule in real time.

[0012] Furthermore, after the step of generating the shift schedule, the shift schedule is sent to the employee's mobile device.

[0013] A storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the scheduling method are implemented.

[0014] An electronic device comprises a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the steps of the scheduling method are implemented.

[0015] Different from the existing technology, the above technical solution is based on dynamic predicted order volume and insight into the changing trend of order volume, so as to automatically schedule according to the predicted order volume fluctuations, so that the scheduling can flexibly respond to market cyclical changes; and then automatically generate pre-scheduling results based on historical scheduling, employee reported working hours and employee actual production capacity to optimize labor costs and avoid excessive or insufficient manpower; the generated schedule achieves the optimal balance in terms of order volume, employee actual production capacity, compliance and cost, greatly improving scheduling efficiency, prompting enterprise management to shift from experience-driven to data-driven, and ensuring the stability and flexibility of enterprise operations.

[0016] The above-mentioned records related to the invention content are only an overview of the technical solution of the present application. In order to enable ordinary technicians in the field to more clearly understand the technical solution of the present application, and then implement it according to the text of the specification and the contents recorded in the drawings, and to make the above-mentioned purpose and other purposes, features and advantages of the present application easier to understand, the following is an explanation in combination with the specific implementation mode and drawings of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings are only used to illustrate the principles, implementation methods, applications, characteristics and effects of the specific embodiments of the present invention and other related contents, and shall not be considered as limiting the present application.

[0018] In the drawings of the specification: Figure 1 It is a flowchart of the scheduling method described in the specific implementation method; Figure 2 It is a schematic diagram of predicted order quantity according to a specific implementation method; Figure 3 It is a schematic diagram of the pre-arrangement result described in the specific implementation method; Figure 4 A schematic diagram of adjusting the pre-arrangement results for a specific implementation method; Figure 5 A schematic diagram of employee shift information editing for a specific implementation method; Figure 6 It is a schematic diagram of prediction analysis results of a specific implementation method. DETAILED DESCRIPTION

[0019] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0020] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0021] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0022] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0023] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0024] Without further limitations, in this application, the words "include", "comprises", "has" or other similar open-ended expressions used in sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0025] Similar to the understanding in the Examination Guidelines, in this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0026] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0027] See also Figure 1-Figure 6As shown in the figure, a scheduling method is used, which is based on the dynamic forecast of order volume and the insight into the changing trend of order volume, so as to automatically schedule according to the forecast order volume fluctuation, so that the scheduling can flexibly respond to the cyclical changes in the market; and then automatically generate the pre-scheduling results based on the historical scheduling situation, the employees' reported working hours and the employees' actual production capacity, so as to optimize the labor cost and avoid the over-allocation or under-allocation of manpower; so that the generated schedule can achieve the optimal balance in terms of order volume, employees' actual production capacity, compliance and cost, which greatly improves the scheduling efficiency, promotes the transformation of enterprise management from experience-driven to data-driven, and ensures the stability and flexibility of enterprise operation.

[0028] Combination Figure 1 The present application provides an implementation of a scheduling method, which includes the following steps: S1. Obtain the predicted order volume within a preset time period; S2. Obtain the scheduling results based on the predicted order volume, historical scheduling, employee reported working hours and employee actual production capacity; S3. Determine whether the pre-scheduling result satisfies the working hours reported by the employees; if so, fill in the shift table with the pre-scheduling result to generate the shift schedule; otherwise, adjust the employee shift information in the pre-scheduling result, and fill in the shift table to generate the shift schedule.

[0029] See also Figure 2 As shown, the above-mentioned predicted order volume is the order volume predicted in a preset time period in the future, which can be predicted through multi-dimensional data; such as by discovering the correlation between order volume and time, weather forecast data (such as temperature, precipitation and other meteorological factors affecting aquatic product consumption) and order events (such as holidays, local large-scale events, typhoon weather, promotions, epidemics, etc.), etc., to provide data support for predicting order volume. Specifically, by analyzing the order volume in the same period last week, the temperature last week, and order events, combined with the current market trends, the daily order volume during the upcoming promotional activities is estimated. The predicted order volume may include but is not limited to one or more of the system predicted volume, the middle station predicted volume, the store manager predicted volume, etc.

[0030] The above-mentioned system prediction is made with the help of data analysis models and algorithms (such as time series analysis models (ARIMA, etc.), machine learning models (linear regression, decision tree, random forest, etc.) or deep learning models (LSTM, etc.)), using historical data (including but not limited to actual order volume, weather forecast data and order events, etc.) to train the model, and by adjusting the model parameters, the model can accurately fit the changing patterns of historical order data; then the relevant feature data within the preset time period is input into the trained model to automatically generate order volume prediction results.

[0031] The above-mentioned middle platform forecast takes into account various factors that affect the order volume from the macro level and the overall operation of the enterprise, and mainly collects information from the sales department, marketing department, supply chain department, etc., such as recent sales trends, marketing activity plans, competitor dynamics, raw material supply, etc., analyzes the overall trend of the current product market, such as the growth or decline of market demand, changes in consumer preferences, etc., studies the impact of changes in policies and regulations in the industry and the application of new technologies on product sales, and evaluates the company's own sales strategy adjustments within the preset time period, such as new product launch plans, price strategy changes, promotion activities arrangements, etc. The potential impact on the order volume; combined with the collected information, market analysis results and corporate strategy evaluation, the order volume is comprehensively forecasted to form the order volume results predicted by the middle platform, starting from the macro level and the overall operation of the enterprise, comprehensively considering various factors that affect the order volume. It can be obtained through channels such as order management systems and operation data systems.

[0032] The above store manager's forecast can reflect the uniqueness and actual demand of the local market. It is mainly based on the actual order volume in the past, the weather forecast in the preset time period and the special events in the preset time period to estimate the order volume. That is, based on the actual order volume in the past, the weather forecast in the preset time period and the order events in the preset time period, the store manager uses his experience accumulated in the local market (such as the market situation in the region, the characteristics of customer demand and the in-depth understanding of the daily operation of the store) to make a comprehensive judgment and forecast of the store's order volume in the preset time period, and form the order volume result predicted by the store manager. The device can be obtained through, for example, the store management system.

[0033] The above-mentioned steps of obtaining the predicted order quantity within the preset time period can be one or more of the system prediction quantity, the middle platform prediction quantity, the store manager prediction quantity, etc., so as to obtain the pre-scheduling results based on one or more of the system prediction quantity, the middle platform prediction quantity, the store manager prediction quantity, etc., historical scheduling conditions, employee reported working hours and employee actual production capacity; it can also be the integration of multiple prediction quantities of the system prediction quantity, the middle platform prediction quantity, the store manager prediction quantity, etc. to form a comprehensive predicted order quantity; such as summarizing the order quantities obtained by the system prediction, the middle platform prediction, and the store manager prediction, and adopting the weighted average method and other methods to combine these three prediction results, such as assigning different weights according to the accuracy of past predictions, the system prediction weight is set to 0.4, the middle platform prediction weight is set to 0.3, and the store manager prediction weight is set to 0.3, and the comprehensive predicted order quantity is calculated.

[0034] In the above-mentioned step of obtaining the pre-scheduling result based on the predicted order volume, historical scheduling, employee-submitted working hours, and employee actual production capacity, the historical scheduling refers to the scheduling records for a period of time in the past (such as the past year), including employee working hours on different dates and in different time periods. The employee-submitted working hours refer to employees submitting their own work or rest within a preset time, where work includes work in a set time period (start time and end time of work), total working time, etc. The employee actual production capacity refers to the actual hourly production capacity of employees calculated by counting the amount of work tasks completed by employees within a certain period of time. For example, the total number of orders delivered by each employee in a month is counted and divided by the total number of working hours to obtain the number of orders delivered by the employee per hour, which is used to measure the actual production capacity of the employee. The above-mentioned pre-scheduling results refer to the pre-set rules and algorithms (such as the use of intelligent optimization algorithms, such as genetic algorithms, simulated annealing algorithms, etc.), with the predicted order volume as the demand orientation, based on the historical scheduling situation, and referring to the scheduling strategies under similar order volume fluctuations in the past. At the same time, the employee's reported working hours data is included. The reported working hours here record in detail the time invested by the employees in the tasks (such as delivery, picking, etc.), accurate to the start and end time of each work. In addition, the actual production capacity of the employees will be considered to generate one or more relatively preferred pre-scheduling results (such as Figure 3 Each pre-scheduled result represents a possible scheduling combination. By automatically generating pre-scheduled results based on preset rules and algorithms, manual intervention can be reduced and the accuracy and efficiency of scheduling can be improved.

[0035] The above judgment on whether the pre-scheduling results meet the working hours reported by employees is to compare the generated pre-scheduling results with the working hours reported by employees one by one, and judge whether the working time of each employee in the pre-scheduling results is within the available working time range of the corresponding employee's reported working hours, and whether it meets the employee's requirements for working hours and rest time. For example, if an employee reports working 9 hours a day and needs to have a 1-hour rest time in the middle, check whether the pre-scheduling results meet this condition. If the pre-scheduling result does not meet the working hours reported by the employees, in the step of adjusting the employee shift information in the pre-scheduling result, the employee shift information in the pre-scheduling result may be adjusted by replacing the employee and editing the employee working hours for the part of the pre-scheduling result that does not meet the reported working hours. The above adjustment of the employee shift information in the pre-scheduling result can be made by manual intervention, automatic adjustment, or a combination of manual intervention and automatic adjustment. Among them, in terms of automatic adjustment, an algorithm can be used to reallocate the working hours of employees, giving priority to adjusting those shifts whose working hours exceed or fall short of the working hours reported by the employees. Manual intervention is for the scheduling manager to manually adjust the pre-scheduling results according to actual conditions, such as special needs of employees, teamwork and other factors. See. Figure 4As shown, specifically, it can be performed by operating on the visual interface. For example, by operating on the visual interface, for example, by clicking on Replace to replace an employee; or by clicking on the Gantt chart on the visual interface to make the employee's working time editable to adjust the employee's working time; or by clicking on the employee's portrait on the visual interface to enter the employee's shift information for editing, to replace an employee and / or adjust the working time (see Figure 5 As shown). The visual interface provides convenient interactive operation functions and displays the scheduling results in intuitive charts, tables, etc., making the scheduling situation clear at a glance and easy to view and understand. Of course, different colors can also be used to distinguish whether the employee's reported working hours are met. When it is met, the row or corresponding cell is filled with gray; otherwise, it is filled with other colors. In this way, in the entire table, rows or cells of different colors clearly show the matching status of the scheduling and the employee's reported working hours.

[0036] See also Figure 3 As shown, in some embodiments, before the step of determining whether the pre-scheduling result meets the requirements, it also includes determining whether the predicted capacity satisfaction rate of each pre-scheduling result meets the standard. If it meets the standard, it is determined whether the pre-scheduling result meets the employee's reported working hours. Specifically, it includes the following steps: Calculate the total capacity of the pre-scheduling results; Then the capacity fulfillment rate = total capacity of the pre-scheduled results / predicted order quantity * 100% Determine whether the capacity satisfaction rate exceeds the preset capacity satisfaction rate. If so, it meets the standard.

[0037] For example, calculate the system capacity satisfaction rate of the system forecast and the store manager's capacity satisfaction rate of the store manager's forecast one by one; the target is met only when both the system capacity satisfaction rate and the store manager's capacity satisfaction rate exceed the preset capacity satisfaction rate.

[0038] In the step of filling the shift table and generating the shift schedule, when the pre-scheduled result meets the employee's reported working hours or meets the requirements after adjustment, the system will fill the shift table with the pre-scheduled result. Fill in the information such as employee name, shift, working hours, rest time, etc. in the shift table. At the same time, optimize the format of the shift table to make it clear and easy to read, so that employees can view it and managers can execute it. For example, different shifts can be marked with different colors, and important information such as employee rest time can be highlighted, and finally an executable shift table can be generated.

[0039] See also Figure 6As shown, in some embodiments, after the step of generating the shift schedule, the prediction analysis results of the corresponding shift schedule are displayed in real time. When the shift schedule is generated, the prediction analysis process is automatically triggered to quickly obtain the prediction analysis results of order processing capacity, labor cost, employee workload, etc. The prediction results are presented in the form of charts, tables, etc. Take the chart as an example to further explain. For example, a bar chart is used to show the predicted number of people and the number of people on duty in different time periods to represent the prediction analysis results of the number of employees, and a line chart is used to show the predicted order volume and the number of orders to be fulfilled to represent the order prediction analysis results. The prediction analysis results provide managers with an immediate decision-making basis, so that they can understand the possible business impact of the scheduling plan while seeing the shift schedule. In the face of fluctuations in order volume, by viewing the order processing capacity prediction results, managers can quickly determine whether the current schedule can meet the order demand, and then decide whether to adjust the schedule, increase or reduce manpower, and avoid waste of labor costs caused by unreasonable scheduling. Enterprises can quickly discover problems in the schedule and make timely adjustments.

[0040] In some embodiments, after the step of generating the shift schedule, the shift schedule is also sent to the employee's mobile device. The above-mentioned mobile device refers to a smart phone, tablet computer, smart watch, smart glasses, etc. that can receive and view the shift schedule through various applications, and can also perform instant messaging, email sending and receiving, and other operations. The shift schedule sent to the employee's mobile device can be pushed to the relevant employees in real time through email, instant messaging APP, message reminders from the company's internal management system, etc., to ensure that employees can obtain the latest shift schedule in a timely manner. Employees can obtain the shift schedule through their mobile devices anytime and anywhere, without having to go to the office to view the paper shift schedule or log in to the computer system, which greatly saves time and energy. When working away from home, on vacation, or commuting, employees can easily check their work arrangements, prepare in advance, and improve work efficiency.

[0041] The present invention also provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for directly converting and putting goods on the shelves are implemented.

[0042] The present invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the steps of the method for directly converting and putting goods on the shelves are implemented.

[0043] The computer program involved in the embodiment can be stored in a computer device readable storage medium, which includes but is not limited to a disk, a tape, a magnetic card, a floppy disk, a flash memory, an optical disk, an optical card, a read-only memory (ROM), a random access memory (RAM), an erasable programmable ROM (EPROM) and an electrically erasable programmable ROM (EEPROM), etc., and also includes other biological, physical or chemical structures that can achieve the same or equivalent functions as the storage media listed above, such as DNA, RNA, protein and other units with information storage capabilities. In a specific embodiment, the storage medium involved can be one of the above-mentioned media types, or a combination of the above-mentioned media types. In different embodiments, the computer program involved in the embodiment can be stored in a single medium in a centralized manner, or it can be stored in multiple media in a distributed manner. The memory containing the computer device readable storage medium can be a non-volatile memory or a random access memory. These computer device readable storage media can be built into the device, or they can be connected to the device involved in the embodiment as an external device or a part of an external device. In some embodiments, a memory with a computer device readable storage medium is deployed locally; in other embodiments, a solution of deploying the memory away from the processor may also be adopted, such as a network attached memory accessed via an RF circuit or an external port and a communication network, wherein the communication network may be the Internet, one or more intranets, a local area network (LAN), a wide area network (WLAN), a storage area network (SAN), etc., or a suitable combination thereof, as long as the computer device can access the memory. In addition, the computer program involved in the embodiment may be stored in plaintext / ciphertext form, or may be designed as training data, which may be integrated and reorganized through model training and implicitly stored in the parameter state of a deep neural network or other machine learning model.

[0044] The processor described in the embodiments of the present application can be implemented by hardware, firmware, software or a combination thereof, and can use a circuit, a single or multiple application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), a digital signal processor (Digital Signal Processor, DSP), a digital signal processing device (Digital Signal Processing Device, DSPD), a programmable logic device (Programmable Logic Device, PLD), a field programmable gate array (Field Programmable Gate Array, FPGA), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, at least one of a microprocessor, and also includes other physical, biological or chemical structures that can achieve similar or equivalent functions to the processors listed above, such as biological neurons, quantum computing units, DNA computing units, etc., so that the processor can execute some or all of the steps in the computer program or method involved in the various embodiments of the present application, or any combination of the steps mentioned therein.

[0045] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. A scheduling method, characterized in that: The following steps are involved: Get the predicted order volume within a preset time period; Obtain scheduling results based on predicted order volume, historical shift scheduling, employee reported working hours, and employee actual capacity; Determine whether the pre-scheduling result satisfies the working hours reported by the employee; if so, fill in the shift table with the pre-scheduling result to generate the shift schedule; Otherwise, after adjusting the employee shift information in the pre-scheduling results, fill in the shift table to generate the shift schedule.

2. The shift scheduling method according to claim 1, characterized in that: The predicted order quantity includes system predicted quantity, middle platform predicted quantity, and store manager predicted quantity.

3. The shift scheduling method according to claim 2, characterized in that: The store manager's forecast is based on the actual order volume in the past, the weather forecast within a preset time period, and the order events within the preset time period to estimate the order volume.

4. The shift scheduling method according to claim 1, characterized in that: Before the step of judging whether the pre-scheduling result meets the requirements, it also includes judging whether the predicted capacity satisfaction rate of each pre-scheduling result meets the standard. If it meets the standard, it is judged whether the pre-scheduling result meets the working hours reported by the employees.

5. The shift scheduling method according to claim 4, characterized in that: The step of judging whether the predicted capacity satisfaction rate of each pre-scheduling result meets the standard comprises the following steps: Calculate the total capacity of the pre-scheduling results; Then the capacity fulfillment rate = total capacity of the pre-scheduled results / predicted order quantity * 100% Determine whether the capacity satisfaction rate exceeds the preset capacity satisfaction rate. If so, it meets the standard.

6. The shift scheduling method according to claim 1, characterized in that: After adjusting the employee shift information in the pre-scheduling result, in the step of filling the shift table to generate the shift schedule, the adjustment of the employee shift information in the pre-scheduling result is performed by operating on a visual interface.

7. The shift scheduling method according to claim 1, characterized in that: It also includes displaying the prediction analysis results of the corresponding shift schedule in real time after the shift schedule is generated.

8. The shift scheduling method according to claim 1, characterized in that: It also includes sending the shift schedule to the employee's mobile device after the shift schedule is generated.

9. A storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the scheduling method according to any one of claims 1 to 8 are implemented.

10. An electronic device, characterized in that The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the scheduling method according to any one of claims 1 to 8 are implemented.

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