Production and marketing data processing method and device based on industrial Internet of Things, equipment and medium
By obtaining and processing historical and real-time sales and inventory data in industrial Internet of Things systems, calculating return rates and repurchase rates, adjusting demand volume and forecasting production demand, the data silos problem is solved, and dynamic adjustments and resources of production plans are achieved. Optimization improves production efficiency and inventory management accuracy.
Patent Information
- Application Number
- CN202411452665.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-17
- Publication Date
- 2025-02-07
AI Technical Summary
In existing industrial Internet of Things services, production data and sales data are recorded in different systems respectively, resulting in data integration difficulties and information silos, resulting in static production plans and inventory management problems.
Preprocessing and calculations are performed by obtaining historical sales data, current actual sales volume and current real-time inventory data, including calculating return rates and repurchase rates, adjusting demand volume, predicting demand volume, and determining dynamic batch production volume based on these data.
More accurate production demand forecasts and dynamic adjustments have been achieved, the flexibility and accuracy of production planning have been improved, the risks of inventory backlog and out of stock have been reduced, resource utilization has been optimized and production costs have been reduced.
Smart Images
Figure CN119398409A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a production and sales data processing method, device, equipment and medium based on industrial Internet of Things. Background Art
[0002] With the development of industrial and IoT technologies, traditional manufacturing is transforming into intelligent manufacturing. In this context, IoT based on industrial production, as an advanced technology that connects sensors, machines, factories and business processes, has become an important means to improve production efficiency, optimize resource utilization and reduce operating costs.
[0003] The results of existing industrial manufacturing depend not only on the automation of the production process and the interconnection of equipment, but also on market demand and customer consumption behavior. However, the production data and sales data in the existing industrial Internet of Things service production and sales data processing system are usually recorded in the product back-end and product front-end systems respectively, which is not conducive to effective data integration and sharing. This deficiency can easily lead to the static nature of production plans and the generation of data island problems, resulting in overproduction or underproduction, inventory backlogs or out-of-stock situations. Summary of the invention
[0004] The main purpose of this application is to provide a production and sales data processing method, device, equipment and medium based on the industrial Internet of Things, aiming to solve the technical problems that production data and sales data are recorded in different systems, resulting in difficulties in data integration and the existence of information islands.
[0005] To achieve the above objectives, in a first aspect, the present application provides a production and sales data processing method based on the industrial Internet of Things, comprising:
[0006] Get historical sales data, current actual sales volume and current real-time inventory data;
[0007] Preprocess the historical sales data to filter out the historical confirmed receipt quantity, the historical actual return quantity and the historical repurchase quantity, and store the historical confirmed receipt quantity, the historical actual return quantity, the historical repurchase quantity, the current actual sales volume and the current real-time inventory data in a database;
[0008] Substituting the historical confirmed receipt quantity and the historical actual return quantity into a first calculation formula to calculate a return rate, and adjusting the actual demand quantity for a first time based on the return rate to obtain a first adjusted demand quantity, substituting the historical confirmed receipt quantity and the historical repurchase quantity into a second calculation formula to calculate a repurchase rate, and adjusting the actual demand quantity for a second time based on the repurchase rate and the first adjusted demand quantity to obtain and output a second adjusted demand quantity;
[0009] Obtaining a current forecast demand based on the second adjusted demand and the growth factor;
[0010] Determine a target total demand based on the current predicted demand, and determine a production quantity for each batch based on the target total demand and the batch quantity;
[0011] Based on the production volume of each batch and the adjustment coefficient, a dynamic batch production volume is obtained;
[0012] The production data is updated and output based on the dynamic batch production volume, and the production data includes: production demand, production progress and inventory.
[0013] Optionally, the historical sales data, the current actual sales volume and the current real-time inventory data are obtained through an enterprise resource planning device, a customer relationship management device, an e-commerce platform data and a point-of-sale management device.
[0014] Optionally, substituting the historical confirmed receipt quantity and the historical actual return quantity into a first calculation formula to calculate a return rate, and performing a first adjustment on the actual demand quantity based on the return rate to obtain a first adjusted demand quantity includes:
[0015] The return rate is calculated using the first calculation formula shown in formula (1):
[0016]
[0017] The actual demand is adjusted for the first time using the following formula (2) to obtain the first adjusted demand:
[0018]
[0019] Where D1 is the first adjustment demand, T is the time period, S t is the historical confirmed receipt quantity, R t is the actual historical return quantity, is the return rate.
[0020] Optionally, substituting the historical confirmed receipt quantity and the historical repurchase quantity into a second calculation formula to calculate a repurchase rate, and performing a second adjustment on the actual demand quantity based on the repurchase rate and the first adjusted demand quantity to obtain and output a second adjusted demand quantity, comprises:
[0021] The repurchase rate is calculated using the second calculation formula shown in the following formula (3):
[0022]
[0023] Based on the repurchase rate and the first adjusted demand, the actual demand is adjusted for the second time using the following formula (4) to obtain and output a second adjusted demand:
[0024] D2=D1×(1+F) (4)
[0025] Where D1 is the second adjustment demand, P t is the historical repurchase quantity, and F is the repurchase rate.
[0026] Optionally, the obtaining the current predicted demand based on the second adjusted demand and the growth factor includes:
[0027] Based on the second adjusted demand and the growth factor, the current forecast demand is calculated using the following formula (5):
[0028] Q1=D2×G (5)
[0029] Among them, Q1 is the current predicted demand, and G is the growth factor, which is set by external factors.
[0030] Optionally, determining a target total demand based on the current predicted demand, and substituting the target total demand and the batch quantity into a third calculation formula to obtain the production quantity of each batch includes:
[0031] Determining the current predicted demand as the target total demand;
[0032] Based on the target total demand and the batch number, the production volume of each batch is determined using the following third calculation formula (6):
[0033]
[0034] Among them, Q2 is the total target demand, S1 is the production volume of each batch, and n is the number of batches.
[0035] Optionally, obtaining the dynamic batch production volume based on the production volume of each batch and the adjustment coefficient includes:
[0036] The dynamic batch production volume is calculated using the following formula (7):
[0037] S2=S1+α(AY) (7)
[0038] Among them, S2 is the dynamic batch production volume, A is the current actual sales volume, Y is the current predicted sales volume, and α is the adjustment coefficient.
[0039] In a second aspect, an embodiment of the present application provides a production and sales data processing device based on the industrial Internet of Things, the device comprising:
[0040] The acquisition module is used to obtain historical sales data, current actual sales volume and current real-time inventory data;
[0041] A processing module is used to pre-process the historical sales data, filter out the historical confirmed receipt quantity, the historical actual return quantity and the historical repurchase quantity, and store the historical confirmed receipt quantity, the historical actual return quantity, the historical repurchase quantity, the current actual sales volume and the current real-time inventory data in a database;
[0042] a calculation module, configured to substitute the historical confirmed receipt quantity and the historical actual return quantity into a first calculation formula to calculate a return rate, and make a first adjustment to the actual demand quantity based on the return rate to obtain a first adjusted demand quantity, substitute the historical confirmed receipt quantity and the historical repurchase quantity into a second calculation formula to calculate a repurchase rate, and make a second adjustment to the actual demand quantity based on the repurchase rate and the first adjusted demand quantity to obtain and output a second adjusted demand quantity;
[0043] A prediction module, configured to obtain a current predicted demand based on the second adjusted demand and the growth factor;
[0044] An adjustment module, configured to determine a target total demand based on the current predicted demand, and substitute the target total demand and the batch quantity into a third calculation formula to obtain a production volume per batch; and obtain a dynamic batch production volume based on the production volume per batch and an adjustment coefficient;
[0045] The feedback module updates and outputs production data based on the dynamic batch production volume, wherein the production data includes: production demand, production progress and inventory.
[0046] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, any of the above-mentioned production and sales data processing methods based on the industrial Internet of Things is implemented.
[0047] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, any of the above-mentioned production and sales data processing methods based on the industrial Internet of Things is implemented.
[0048] The production and marketing data processing method based on the industrial Internet of Things provided in the embodiment of the present application firstly obtains historical sales data, current actual sales volume and current real-time inventory data, pre-processes the historical sales data, screens out the historical confirmed receipt quantity, historical actual return quantity and historical repurchase quantity, and stores the historical confirmed receipt quantity, historical actual return quantity, historical repurchase quantity, current actual sales volume and current real-time inventory data in a database; secondly, substitutes the historical confirmed receipt quantity and the historical actual return quantity into a first calculation formula to calculate the return rate, and makes a first adjustment to the actual demand quantity based on the return rate to obtain a first adjusted demand quantity, substitutes the historical confirmed receipt quantity and the historical repurchase quantity into a second calculation formula to calculate the repurchase rate, and makes a second adjustment to the actual demand quantity based on the repurchase rate and the first adjusted demand quantity to obtain and output the second adjusted demand quantity, and by combining the historical sales data, the historical return quantity and the historical Based on the real-time data and historical repurchase data, it can more accurately predict production demand. At the same time, dynamic adjustments can be made based on real-time data, so that the production plan can quickly respond to market changes and reduce inventory backlogs and out-of-stock risks. Compared with traditional static forecasting methods, it can respond to market fluctuations more flexibly and improve the accuracy of production plans; then, the current forecast demand is obtained based on the second adjusted demand and the growth factor; the total target demand is determined based on the current forecast demand, and the total target demand and the number of batches are substituted into the third calculation formula to obtain the production volume of each batch; the dynamic batch production volume is obtained based on the production volume of each batch and the adjustment coefficient. By optimizing batch production planning and dynamic adjustment, this application can effectively reduce resource waste in the production process and improve production efficiency. Compared with traditional production planning methods, this application can better balance supply and demand and reduce production costs; finally, the production plan volume is updated and output based on the dynamic batch production volume. BRIEF DESCRIPTION OF THE DRAWINGS
[0049] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.
[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0051] Figure 1 A flowchart of a production and sales data processing method based on the industrial Internet of Things provided in this application;
[0052] Figure 2 A schematic diagram of the structure of a production and sales data processing device based on the industrial Internet of Things provided in an embodiment of the present application;
[0053] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
[0054] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION
[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0056] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.
[0057] The terms "first", "second", and "third" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second", and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of this application (such as up, down, left, right, front, back...) are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. The terms "including" and "having" in the embodiments of this application and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or components inherent to these processes, methods, products, or devices.
[0058] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.
[0059] The present application is described in detail below with reference to the accompanying drawings and embodiments.
[0060] Figure 1 A flowchart of a production and sales data processing method based on the industrial Internet of Things provided in this application is provided. Figure 2 A schematic diagram of the structure of a production and sales data processing device based on the industrial Internet of Things provided in an embodiment of the present application, Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
[0061] See also Figure 1 The present application provides an embodiment of a production and marketing data processing method based on the industrial Internet of Things, and the production and marketing data processing method based on the industrial Internet of Things includes the following steps:
[0062] S110, obtaining historical sales data, current actual sales volume and current real-time inventory data.
[0063] In some embodiments, historical sales data, current actual sales volume and current real-time inventory data can be obtained through enterprise resource planning systems, customer relationship management systems, e-commerce platform data and point-of-sale management systems, and historical and real-time production and sales data can be collected through industrial Internet of Things devices and system interfaces to support predictive analysis and production planning.
[0064] S120, pre-process the historical sales data, filter out the historical confirmed receipt quantity, the historical actual return quantity and the historical repurchase quantity, and store the historical confirmed receipt quantity, the historical actual return quantity, the historical repurchase quantity, the current actual sales volume and the current real-time inventory data in the database.
[0065] In some embodiments, the above step S120 (preprocessing historical sales data) may include preprocessing the historical sales data such as cleaning, deduplication, and format standardization to ensure data quality and consistency.
[0066] S130. Substitute the historical confirmed receipt quantity and the historical actual return quantity into the first calculation formula to calculate the return rate, and make a first adjustment to the actual demand based on the return rate to obtain a first adjusted demand, substitute the historical confirmed receipt quantity and the historical repurchase quantity into the second calculation formula to calculate the repurchase rate, and make a second adjustment to the actual demand based on the repurchase rate and the first adjusted demand to obtain and output a second adjusted demand.
[0067] In some embodiments, the above step S130 (substituting the historical confirmed receipt quantity and the historical actual return quantity into the first calculation formula to calculate the return rate, and making a first adjustment to the actual demand based on the return rate to obtain a first adjusted demand) includes:
[0068] The return rate is calculated using the first calculation formula shown in formula (1):
[0069]
[0070] The actual demand is adjusted for the first time using the following formula (2) to obtain the first adjusted demand:
[0071]
[0072] Where D1 represents the first adjustment demand, T is the time period, S t The received quantity can be confirmed for the history, R t The actual return quantity can be the historical It can be the return rate.
[0073] It should be noted that the historical confirmed receipt quantity and the historical actual return quantity mentioned in the above step S130 can be monthly, quarterly, or annual historical sales data, T is the corresponding monthly, quarterly, or annual time period, and the first adjustment demand takes into account the impact of returns.
[0074] In some embodiments, the above step S130 (substituting the historical confirmed receipt quantity and the historical repurchase quantity into the second calculation formula to calculate the repurchase rate, and making a second adjustment to the actual demand based on the repurchase rate and the first adjusted demand to obtain and output the second adjusted demand) includes:
[0075] The repurchase rate is calculated using the second calculation formula shown in the following formula (3):
[0076]
[0077] Based on the repurchase rate and the first adjusted demand, the actual demand is adjusted for the second time using the following formula (4) to obtain and output the second adjusted demand:
[0078] D2=D1×(1+F)(4)
[0079] Where D1 represents the second adjustment demand, T is the time period, S t The quantity received can be confirmed for history, P t It can be the historical repurchase quantity, and F can be the repurchase rate.
[0080] It should be noted that the historical confirmed receipt quantity and historical repeat purchase quantity mentioned in the above step S130 can be monthly, quarterly or annual historical sales data, T is the corresponding monthly, quarterly or annual time period, and the second adjusted demand quantity takes into account the impact of repeat purchase behavior.
[0081] S140. Obtain the current predicted demand based on the second adjusted demand and the growth factor.
[0082] For example, the current forecast demand can be calculated based on the second adjusted demand and the growth factor using the following formula (5):
[0083] Q1=D2×G(5)
[0084] Among them, Q1 represents the current predicted demand, and G can be a growth factor, which can be set in combination with market trends and economic factors.
[0085] Specifically, the growth factor G is a proportional factor used to adjust the forecast demand, reflecting the impact of market trends, economic factors or other external factors. Its value is usually greater than or equal to 1, indicating growth; less than 1 indicates market shrinkage or reduced demand. The growth factor G can be dynamically adjusted according to actual conditions to adapt to market changes, and no specific numerical limit is given here.
[0086] The average of the annual growth rates over the past few years can be used as the basis for the future growth factor, which is recorded as AG. The specific reference formula is as follows:
[0087]
[0088] Among them, AG can be the future growth factor, Si can be the sales volume in the i-th year, and n can be the number of years (i.e., the number of years used to calculate the growth rate).
[0089] S150. Determine a target total demand based on the current predicted demand, and determine a production quantity for each batch based on the target total demand and the batch quantity.
[0090] Specifically, the current forecast demand can be determined as the total target demand;
[0091] Then, the total target demand can be expressed as Q2, then we have,
[0092] Q2=Q1,
[0093] Furthermore, the production volume of each batch is determined based on the target total demand and the batch number using the following third calculation formula (6):
[0094]
[0095] Among them, S1 can be the production volume of each batch, n can be the number of batches, and the number of batches can be determined according to the production cycle and market demand fluctuations.
[0096] S160. Obtain dynamic batch production volume based on each batch production volume and the adjustment coefficient.
[0097] Exemplarily, the dynamic batch production volume can be expressed as S2, and the dynamic batch production volume can be calculated using the following formula (7):
[0098] S2 = S1 + α(A - Y)(7)
[0099] Wherein, A may be the current actual sales volume, Y may be the current predicted sales volume, and α may be the adjustment coefficient.
[0100] It should be noted that α is an adjustment coefficient and a flexible factor used to control the sensitivity of adjusting the production batch quantity according to the difference between the actual sales and the predicted sales. If an enterprise hopes to quickly respond to market changes, a larger α value (such as 0.5 - 1.0) can be selected; if the enterprise is more inclined to a conservative adjustment strategy and avoids frequent changes in the production plan, a smaller α value (such as 0.1 - 0.3) can be selected. The adjustment coefficient α can be dynamically adjusted according to the actual situation to adapt to market changes, and specific numerical limitations are not given here.
[0101] When A > Y, it indicates that the actual sales volume exceeds the predicted value, the market demand is greater than expected. Therefore, S2 will increase, meaning that the production volume of the next batch needs to be increased to avoid out-of-stock situations.
[0102] When A < Y, it indicates that the actual sales volume is lower than the predicted value, the market demand is lower than expected. Therefore, S2 will decrease, meaning that the production volume of the next batch can be reduced to avoid product backlogs.
[0103] In some embodiments, by real-time monitoring various key indicators in the production process, including production progress, inventory level, sales situation, etc., and using the monitoring data, the adjustment coefficient α and the growth factor G are dynamically optimized to continuously improve the prediction model and the production plan.
[0104] S170. Update and output production data based on the dynamic batch production volume. The production data may include: production demand, production progress, and inventory level.
[0105] Specifically, making a detailed plan for the production data through the dynamic batch production volume may include adjusting the data affecting the production plan such as production demand, production progress, and inventory level. It should be noted that updating and outputting production data based on the dynamic batch production volume can generate regular reports according to various data, prediction results, and production planned quantities to assist management in decision-making.
[0106] The production and marketing data processing method based on the industrial Internet of Things provided in the embodiment of the present application first obtains historical sales data, current actual sales volume and current real-time inventory data, pre-processes the historical sales data, screens out the historical confirmed receipt quantity, historical actual return quantity and historical repurchase quantity, and stores the historical confirmed receipt quantity, historical actual return quantity, historical repurchase quantity, current actual sales volume and current real-time inventory data in a database; secondly, substitutes the historical confirmed receipt quantity and the historical actual return quantity into a first calculation formula to calculate the return rate, and makes a first adjustment to the actual demand quantity based on the return rate to obtain a first adjusted demand quantity, substitutes the historical confirmed receipt quantity and the historical repurchase quantity into a second calculation formula to calculate the repurchase rate, and makes a second adjustment to the actual demand quantity based on the repurchase rate and the first adjusted demand quantity to obtain and output the second adjusted demand quantity; by combining the historical sales data, historical return data and historical repurchase data, it is possible to better The invention can more accurately predict production demand, and at the same time, make dynamic adjustments based on real-time data, so that the production plan can quickly respond to market changes and reduce inventory backlog and out-of-stock risks. Compared with the traditional static forecasting method, it can respond to market fluctuations more flexibly and improve the accuracy of the production plan; then, the current predicted demand is obtained based on the second adjusted demand and the growth factor; the total target demand is determined based on the current predicted demand, and the total target demand and the batch quantity are substituted into the third calculation formula to obtain the production volume of each batch; the dynamic batch production volume is obtained based on the production volume of each batch and the adjustment coefficient. By optimizing batch production planning and dynamic adjustment, the present application can effectively reduce resource waste in the production process and improve production efficiency. Compared with the traditional production planning method, the present application can better balance the supply and demand relationship and reduce production costs; finally, the production data is updated and output based on the dynamic batch production volume. The production data may include: production demand, production progress and inventory.
[0107] The present application also provides a production and sales data processing device based on the industrial Internet of Things. Figure 2 , Figure 2 201 is a schematic diagram of the structure of a production and marketing data processing device based on the industrial Internet of Things provided in an embodiment of the present application. The production and marketing data processing device based on the industrial Internet of Things includes: an acquisition module 201, a processing module 202, a calculation module 203, a prediction module 204, an adjustment module 205 and a feedback module 206, wherein:
[0108] The acquisition module 201 can be used to acquire historical sales data, current actual sales volume and current real-time inventory data;
[0109] The processing module 202 can be used to pre-process the historical sales data, filter out the historical confirmed receipt quantity, the historical actual return quantity and the historical repurchase quantity, and store the historical confirmed receipt quantity, the historical actual return quantity, the historical repurchase quantity, the current actual sales volume and the current real-time inventory data in a database;
[0110] The calculation module 203 may be used to substitute the historical confirmed receipt quantity and the historical actual return quantity into a first calculation formula to calculate a return rate, and make a first adjustment to the actual demand based on the return rate to obtain a first adjusted demand, substitute the historical confirmed receipt quantity and the historical repurchase quantity into a second calculation formula to calculate a repurchase rate, and make a second adjustment to the actual demand based on the repurchase rate and the first adjusted demand to obtain and output a second adjusted demand;
[0111] The prediction module 204 may be used to obtain a current predicted demand based on the second adjusted demand and the growth factor;
[0112] The adjustment module 205 may be used to determine a target total demand based on the current predicted demand, and substitute the target total demand and the batch quantity into a third calculation formula to obtain a production volume per batch; and obtain a dynamic batch production volume based on the production volume per batch and the adjustment coefficient;
[0113] The feedback module 206 may be used to update and output production data based on the dynamic batch production volume. The production data may include: production demand, production progress and inventory.
[0114] In some embodiments, the production and sales data processing device based on the industrial Internet of Things may also include: a display module 207, which can be used to visualize various data, forecast results and production plans, and generate regular reports to help management make decisions. By visually displaying the data processing results, forecast information, production adjustment status, etc. of each stage, dashboards and reports are generated and provided to the management. The management makes further decisions and strategic adjustments based on the generated reports and visualized data.
[0115] In actual applications, enterprises can make full use of industrial Internet of Things technology and data analysis methods to achieve accurate prediction and dynamic adjustment of production plans, effectively respond to market changes, and optimize the management of inventory and production resources.
[0116] Those skilled in the art should understand that the division of the various modules in the embodiment is merely a division of logical functions, and in actual application, all or part of them can be integrated into one or more actual carriers, and these modules can all be implemented in the form of software called through a processing unit, or all in the form of hardware, or in the form of a combination of software and hardware. It should be noted that each module in the production and sales data processing device based on the industrial Internet of Things in this embodiment corresponds one by one to each step in the production and sales data processing method based on the industrial Internet of Things in the above-mentioned embodiment. Therefore, the specific implementation method of this embodiment can refer to the implementation method of the above-mentioned production and sales data processing method based on the industrial Internet of Things, which will not be repeated here.
[0117] The production and sales data processing device based on the industrial Internet of Things provided in this embodiment can more accurately predict production demand by combining historical sales data, historical return data and historical repeat purchase data. At the same time, it can make dynamic adjustments based on real-time data, so that the production plan can respond quickly to market changes and reduce inventory backlogs and out-of-stock risks. It has significant prompts and improvements in the accuracy, flexibility, data integration capabilities and real-time response capabilities of the production plan.
[0118] The present application also provides an electronic device. Figure 3 , Figure 3 301 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the computer program is executed by the processor 301, the production and sales data processing method based on the industrial Internet of Things as described in any of the above embodiments is implemented.
[0119] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media that include computer-usable program code.
[0120] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
[0121] Memory 302 may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
[0122] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, any of the above-mentioned production and sales data processing methods based on the industrial Internet of Things is implemented. The computer includes a personal computer, a tablet computer, a laptop computer, a PDA, a mobile phone or a server.
[0123] The computer-readable storage medium may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor device, device or component, a magnetic card, an IC card, a USB flash drive, an SD card, etc. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a phase change memory (PRAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), other types of random access memory (RAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory or other memory technology, a CD-ROM, a digital versatile disk (DVD), an optical storage device, a magnetic storage device, or any combination of the above. The computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution device, device or component. The computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any combination thereof. A computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in conjunction with an instruction execution device, apparatus, or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, optical cable, radio frequency signals, and the like.
[0124] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.
Claims
1. A production and marketing data processing method based on industrial Internet of Things, characterized in that: include: Get historical sales data, current actual sales volume and current real-time inventory data; Preprocess the historical sales data to filter out the historical confirmed receipt quantity, the historical actual return quantity and the historical repurchase quantity, and store the historical confirmed receipt quantity, the historical actual return quantity, the historical repurchase quantity, the current actual sales volume and the current real-time inventory data in a database; Substituting the historical confirmed receipt quantity and the historical actual return quantity into a first calculation formula to calculate a return rate, and adjusting the actual demand quantity for a first time based on the return rate to obtain a first adjusted demand quantity, substituting the historical confirmed receipt quantity and the historical repurchase quantity into a second calculation formula to calculate a repurchase rate, and adjusting the actual demand quantity for a second time based on the repurchase rate and the first adjusted demand quantity to obtain and output a second adjusted demand quantity; Obtaining a current forecast demand based on the second adjusted demand and the growth factor; Determine a target total demand based on the current predicted demand, and determine a production quantity for each batch based on the target total demand and the batch quantity; Based on the production volume of each batch and the adjustment coefficient, a dynamic batch production volume is obtained; The production data is updated and output based on the dynamic batch production volume, and the production data includes: production demand, production progress and inventory.
2. The production and marketing data processing method based on the industrial Internet of Things according to claim 1 is characterized in that: The historical sales data, the current actual sales volume and the current real-time inventory data are obtained through an enterprise resource planning device, a customer relationship management device, an e-commerce platform data and a point-of-sale management device.
3. The production and marketing data processing method based on the industrial Internet of Things according to claim 1 is characterized in that: Substituting the historical confirmed receipt quantity and the historical actual return quantity into a first calculation formula to calculate a return rate, and making a first adjustment to the actual demand quantity based on the return rate to obtain a first adjusted demand quantity, includes: The return rate is calculated using the first calculation formula shown in formula (1): The actual demand is adjusted for the first time using the following formula (2) to obtain the first adjusted demand: Where D1 is the first adjustment demand, T is the time period, S t is the historical confirmed receipt quantity, R t is the actual historical return quantity, is the return rate.
4. The production and marketing data processing method based on the industrial Internet of Things according to claim 3 is characterized in that: Substituting the historical confirmed receipt quantity and the historical repurchase quantity into a second calculation formula to calculate a repurchase rate, and performing a second adjustment on the actual demand based on the repurchase rate and the first adjusted demand to obtain and output a second adjusted demand, includes: The repurchase rate is calculated using the second calculation formula shown in the following formula (3): Based on the repurchase rate and the first adjusted demand, the actual demand is adjusted for the second time using the following formula (4) to obtain and output a second adjusted demand: D2=D1×(1+F) (4) Where D1 is the second adjustment demand, P t is the historical repurchase quantity, and F is the repurchase rate.
5. The production and marketing data processing method based on the industrial Internet of Things according to claim 4 is characterized in that: The obtaining the current predicted demand based on the second adjusted demand and the growth factor includes: Based on the second adjusted demand and the growth factor, the current forecast demand is calculated using the following formula (5): Q1=D2×G (5) Among them, Q1 is the current predicted demand, and G is the growth factor, which is set by external factors.
6. The production and marketing data processing method based on the industrial Internet of Things according to claim 5 is characterized in that: The determining of the target total demand based on the current predicted demand, and determining the production volume of each batch based on the target total demand and the batch quantity, includes: Determining the current predicted demand as the target total demand; Based on the target total demand and the batch number, the production volume of each batch is determined using the following third calculation formula (6): Among them, Q2 is the total target demand, S1 is the production volume of each batch, and n is the number of batches.
7. The production and marketing data processing device based on the industrial Internet of Things according to claim 1 is characterized in that: The obtaining of dynamic batch production volume based on the production volume of each batch and the adjustment coefficient includes: The dynamic batch production volume is calculated using the following formula (7): S2=S1+α(AY) (7) Among them, S2 is the dynamic batch production volume, A is the current actual sales volume, Y is the current predicted sales volume, and α is the adjustment coefficient.
8. A production and marketing data processing device based on industrial Internet of Things, characterized in that: include: The acquisition module is used to obtain historical sales data, current actual sales volume and current real-time inventory data; A processing module is used to pre-process the historical sales data, filter out the historical confirmed receipt quantity, the historical actual return quantity and the historical repurchase quantity, and store the historical confirmed receipt quantity, the historical actual return quantity, the historical repurchase quantity, the current actual sales volume and the current real-time inventory data in a database; a calculation module, configured to substitute the historical confirmed receipt quantity and the historical actual return quantity into a first calculation formula to calculate a return rate, and make a first adjustment to the actual demand quantity based on the return rate to obtain a first adjusted demand quantity, substitute the historical confirmed receipt quantity and the historical repurchase quantity into a second calculation formula to calculate a repurchase rate, and make a second adjustment to the actual demand quantity based on the repurchase rate and the first adjusted demand quantity to obtain and output a second adjusted demand quantity; A prediction module, configured to obtain a current predicted demand based on the second adjusted demand and the growth factor; An adjustment module, configured to determine a target total demand based on the current predicted demand, and substitute the target total demand and the batch quantity into a third calculation formula to obtain a production quantity for each batch; Based on the production volume of each batch and the adjustment coefficient, a dynamic batch production volume is obtained; The feedback module updates and outputs production data based on the dynamic batch production volume, wherein the production data includes: production demand, production progress and inventory.
9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the production and sales data processing method based on the industrial Internet of Things as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the method for processing production and sales data based on the industrial Internet of Things as claimed in any one of claims 1 to 7 is implemented.