Raw material allocation methods, devices, terminals, and media based on the Industrial Internet of Things
By collecting data in real time through the Industrial Internet of Things and constructing objective functions and prediction models, the raw material allocation scheme is optimized, which solves the problems of lagging allocation schemes and insufficient consideration of multi-dimensional indicators in existing technologies, and achieves efficient and accurate raw material allocation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHENGDU QINCHUAN IOT TECH CO LTD
- Filing Date
- 2026-02-28
- Publication Date
- 2026-06-02
AI Technical Summary
Existing raw material allocation technologies rely on manual data entry or periodic batch updates, which cannot collect dynamic data such as production load, transportation equipment status, and storage environment temperature and humidity in real time. This results in lagging allocation plans, an inability to take into account multiple dimensions of indicators, and a mismatch between raw material supply and production demand, leading to production line shutdowns and raw material waste.
The Industrial Internet of Things (IIoT) approach uses IIoT sensors to collect real-time data on inventory, production, transportation, and the environment. It then constructs an objective function and constraints, utilizes a pre-set allocation effect prediction model to optimize the raw material allocation scheme, and adjusts weighting coefficients to achieve the goals of shortest time and highest inventory turnover.
It improved the efficiency and accuracy of raw material allocation, shortened allocation time, increased inventory turnover, and optimized the operational efficiency of the production line.
Smart Images
Figure CN122134012A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a raw material allocation method, apparatus, terminal and medium based on the Industrial Internet of Things. Background Technology
[0002] Raw material allocation is a core process in factory manufacturing. Essentially, it involves rationally allocating the types, quantities, and transportation routes of raw materials based on factors such as production demand, inventory status, and transportation capacity to ensure the continuous and stable operation of the production line. With the increasing prevalence of information technology in manufacturing, enterprises are introducing information technology tools such as Material Requirements Planning (MRP), Enterprise Resource Planning (ERP), and Warehouse Management Systems (WMS) to achieve electronic management of raw material allocation data. MRP systems can calculate the total demand and procurement time of raw materials based on production plans, while ERP systems integrate inventory, production, and financial data to formulate allocation plans. WMS is responsible for recording the inbound and outbound operations of raw materials in the warehousing process. The core technology at this stage is batch planning based on static data, with data update cycles typically on a daily or weekly basis, and is currently the mainstream allocation technology for medium to large-sized factories.
[0003] However, existing raw material allocation technologies have the following shortcomings: they rely on manual data entry or periodic batch updates; industrial IoT (IIoT) sensors are only used in localized processes and cannot collect dynamic data such as production load, transportation equipment status, and storage environment temperature and humidity in real time. This results in allocation plans being based on "lagging data," which can easily lead to a mismatch between raw material supply and actual production demand; they are designed only for the single objective of "lowest cost" or "shortest time," without taking into account multiple dimensions such as inventory turnover rate and production qualification rate, which can easily lead to problems such as "optimal cost but serious inventory backlog" or "optimal time but excessively high transportation costs"; if there are errors in the plan (such as the estimated transportation time not matching the actual time), they can only be discovered and adjusted manually after execution, causing losses such as production line shutdowns and raw material waste. Summary of the Invention
[0004] The main objective of this application is to provide a raw material allocation method, device, terminal, and medium based on the Industrial Internet of Things, which aims to simultaneously improve the allocation efficiency and accuracy of raw materials in factories.
[0005] To achieve the above objectives, this application provides a raw material allocation method based on the Industrial Internet of Things (IIoT), the method comprising: Obtain the raw material inventory data, production demand data, transportation condition data, and environmental data corresponding to the factory. Based on the raw material inventory data, the production demand data, the transportation condition data, and the environmental data, an objective function and constraints are constructed with the goal of minimizing raw material allocation time and maximizing raw material inventory turnover. Based on the constraints and the objective function, the objective function is solved to obtain the initial raw material allocation scheme. By using a preset allocation effect prediction model, based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan, the predicted allocation effect index is obtained. Based on the predicted allocation effect index and the preset allocation effect index threshold, the initial raw material allocation plan is processed to obtain the target raw material allocation plan.
[0006] Specifically, based on the raw material inventory data, the transportation condition data, and the environmental data, the objective function and constraints are constructed with the goal of minimizing raw material allocation time and maximizing raw material inventory turnover rate, including: Based on the transportation condition data and the environmental data, a first sub-objective function is constructed with the goal of minimizing the raw material allocation time. Based on the raw material inventory data and the production demand data, a second sub-objective function is constructed with the goal of maximizing the raw material inventory turnover rate. Based on the preset first weight coefficient corresponding to the first sub-objective function and the preset second weight coefficient corresponding to the second sub-objective function, the first sub-objective function and the second sub-objective function are weighted and summed to obtain the objective function. The constraints are constructed based on the raw material inventory data and the transportation condition data.
[0007] Specifically, based on the transportation condition data and the environmental data, the first sub-objective function is constructed with the goal of minimizing the raw material allocation time, including: The first calculation result is obtained by calculating the quotient of the transportation route distance in the environmental data and the transportation equipment operating speed in the transportation condition data; The first calculation result is summed with the fixed loading and unloading time in the transportation condition data to obtain the transportation time of a single raw material among all raw materials. Based on the transportation time of the single raw material, the total transportation time of all raw materials is obtained by iterating through all raw materials. The first sub-objective function is determined based on the total transportation time.
[0008] Specifically, the second sub-objective function, constructed based on the raw material inventory data and the production demand data with the objective of maximizing the raw material inventory turnover rate, includes: The raw material inventory turnover rate is obtained by calculating the quotient between the total raw material demand in the production demand data and the average inventory in the raw material inventory data, wherein the average inventory is half of the sum of the initial inventory in the raw material inventory data and the final inventory in the raw material inventory data. The second sub-objective function is determined based on the raw material inventory turnover rate.
[0009] Specifically, the constraints include supply constraints and transportation constraints; Based on the raw material inventory data and the transportation condition data, the constraints are constructed, including: Based on the raw material inventory data, the supply constraints are constructed. Based on the transportation condition data, the transportation constraints are constructed.
[0010] Specifically, the preset allocation effect prediction model includes an input layer, a hidden layer, and an output layer; The step of obtaining predicted allocation effect indicators through a preset allocation effect prediction model, based on the raw material inventory data, production demand data, transportation condition data, environmental data, and the initial raw material allocation plan, includes: Through the input layer, a normalized input vector is obtained based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan. The output feature vector is obtained through the hidden layer based on the normalized input vector; The predicted allocation effect index is obtained through the output layer based on the output feature vector. The predicted allocation effect index includes the predicted value of allocation time error rate and the predicted value of inventory turnover rate achievement rate.
[0011] Specifically, the step of processing the initial raw material allocation plan based on the predicted allocation effect index and the preset allocation effect index threshold to obtain the target raw material allocation plan includes: The predicted values of the allocation time error rate and the inventory turnover rate are normalized to obtain the allocation effect index score. The blending effect index score is compared with the preset blending effect index threshold. If the blending effect index score is greater than the preset blending effect index threshold, the initial raw material blending scheme is determined to be the target raw material blending scheme. If the blending effect index score is less than or equal to the preset blending effect index threshold, the preset first weight coefficient or the preset second weight coefficient is adjusted to update the initial raw material blending scheme and make the blending effect index score corresponding to the initial raw material blending scheme greater than the preset blending effect index threshold.
[0012] To achieve the above objectives, this application also provides a raw material dispensing device based on the Industrial Internet of Things, the device comprising: The first unit is used to obtain the raw material inventory data, production demand data, transportation condition data and environmental data of the factory. The second unit is used to construct an objective function and constraints based on the raw material inventory data, the production demand data, the transportation condition data, and the environmental data, with the goal of minimizing the raw material allocation time and maximizing the raw material inventory turnover rate. The third unit is used to solve the objective function based on the constraints and the objective function, and obtain the initial raw material allocation scheme. The fourth unit is used to obtain the predicted allocation effect index based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan by using a preset allocation effect prediction model. The fifth unit is used to process the initial raw material allocation plan based on the predicted allocation effect index and the preset allocation effect index threshold to obtain the target raw material allocation plan.
[0013] To achieve the above objectives, this application also provides a terminal, including a memory storing multiple instructions; the processor loads instructions from the memory to execute the steps in any of the methods provided in this application.
[0014] To achieve the above objectives, this application also provides a medium storing a plurality of instructions adapted for loading by a processor to execute the steps in any of the methods provided in this application.
[0015] This application provides a raw material allocation method, device, terminal, and medium based on the Industrial Internet of Things (IIoT). It first acquires raw material inventory data, production demand data, transportation condition data, and environmental data corresponding to the factory. Based on these data, an objective function and constraints are constructed, and the objective function is solved to obtain an initial raw material allocation plan. A pre-set allocation effect prediction model is used to obtain predicted allocation effect indicators based on the raw material inventory data, production demand data, transportation condition data, environmental data, and the initial raw material allocation plan. The initial raw material allocation plan is then processed to obtain a target raw material allocation plan, thereby simultaneously improving the allocation efficiency and accuracy of raw materials in the factory. Attached Figure Description
[0016] Figure 1 A flowchart illustrating the method provided in the embodiments of this application; Figure 2 This is a schematic diagram illustrating the application of the preset blending effect prediction model provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the terminal structure provided in an embodiment of this application. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0018] Existing raw material allocation technologies have the following shortcomings: they rely on manual data entry or periodic batch updates; Industrial Internet of Things (IIoT) sensors are only used in localized processes and cannot collect dynamic data such as production load, transportation equipment status, and storage environment temperature and humidity in real time. This results in allocation plans being based on "lagging data," which can easily lead to a mismatch between raw material supply and actual production demand; they are designed only for the single objective of "lowest cost" or "shortest time," without taking into account multiple dimensions such as inventory turnover rate and production qualification rate, which can easily lead to problems such as "optimal cost but serious inventory backlog" or "optimal time but excessively high transportation costs"; if there are errors in the plan (such as the estimated transportation time not matching the actual time), they can only be discovered and adjusted manually after execution, causing losses such as production line shutdowns and raw material waste.
[0019] Therefore, this application provides a raw material allocation method, device, terminal, and medium based on the Industrial Internet of Things to solve practical technical problems.
[0020] In some embodiments, with IIoT gateways (data transmission hubs), edge computing (real-time data processing), and data fusion algorithms (multi-source data integration) as the core foundation, the goal is to break through the limitations of "dispersed sensing and isolated processing" and achieve unified scheduling of multiple sensors, efficient data fusion, and accurate sensing, so as to provide reliable data support for decision optimization in industrial scenarios.
[0021] In some embodiments, the device may be integrated into an electronic device, such as a terminal or server.
[0022] In some embodiments, the server may also be implemented as a terminal.
[0023] The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.
[0024] The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server can be connected directly or indirectly through wired or wireless communication, which is not limited herein.
[0025] The following sections provide detailed descriptions of each example. It should be noted that the sequence numbers of the following embodiments are not intended to limit the preferred order of the embodiments.
[0026] This application provides a raw material allocation method based on the Industrial Internet of Things, which can simultaneously improve the allocation efficiency and accuracy of raw materials in a factory.
[0027] In some embodiments, taking a certain automobile engine block manufacturing plant as an example, the plant mainly produces 2.0T gasoline engine blocks (model A) and 1.5T diesel engine blocks (model B). The core raw materials are aluminum alloy ingots (raw material X), cast iron parts (raw material Y), and sealant (raw material Z). The plant deploys industrial Internet of Things (IIoT) sensors to collect inventory, production, transportation, and environmental data.
[0028] like Figure 1 The specific process of the method can be as follows: S110. Obtain the raw material inventory data, production demand data, transportation condition data, and environmental data corresponding to the factory.
[0029] In some embodiments, data is collected in real time through IIoT sensors and information systems deployed in the factory: Raw material inventory data: Aluminum alloy ingots (X) initial inventory 5000kg, final inventory (expected end of period) 3000kg; cast iron parts (Y) initial inventory 3000 pieces, final inventory 1800 pieces; sealant (Z) initial inventory 1200 tubes, final inventory 600 tubes; the current inventory of the three types of raw materials are 4200kg, 2500 pieces, and 900 tubes, respectively.
[0030] Production demand data: The daily production plan is 800 units of Model A cylinder block and 500 units of Model B cylinder block; the unit consumption of Model A cylinder block is X 5kg / unit, Y 2 pieces / unit, and Z 1 piece / unit; the unit consumption of Model B cylinder block is X 4kg / unit, Y 2 pieces / unit, and Z 1 piece / unit. The total daily raw material demand is: X = 800×5 + 500×4 = 6000kg, Y = 800×2 + 500×2 = 2600 pieces, and Z = 800×1 + 500×1 = 1300 pieces.
[0031] Transportation conditions data: The transportation route distance from the factory warehouse to the production line is 1.2km (shared by X and Y) and 0.8km (separate route for Z); the transportation equipment is an electric forklift with an operating speed of 0.3km / min and a fixed loading and unloading operation time of 5min / time; the rated load of the forklift is 1000kg / time (X and Y) and 500 pieces / time (Z).
[0032] Environmental data: The temperature and humidity in the raw material storage area are 25℃ and 60% (no abnormalities, and do not affect the availability of raw materials); there is no congestion on the transportation route, and the environment has no additional impact on transportation efficiency.
[0033] S120. Based on the raw material inventory data, the production demand data, the transportation condition data, and the environmental data, construct an objective function and constraints with the goal of minimizing raw material allocation time and maximizing raw material inventory turnover.
[0034] In some embodiments, the objective function and constraints are constructed based on the raw material inventory data, the transportation condition data, and the environmental data, with the goal of minimizing raw material allocation time and maximizing raw material inventory turnover. This includes steps A1 to A4 as shown below: A1. Based on the transportation condition data and the environmental data, construct the first sub-objective function with the goal of minimizing the raw material allocation time.
[0035] In some embodiments, the construction of the first sub-objective function based on the transportation condition data and the environmental data, with the goal of minimizing the raw material allocation time, includes the steps A11 to A14 shown below: A11. Calculate the quotient of the transportation route distance in the environmental data and the transportation equipment operating speed in the transportation condition data to obtain the first calculation result.
[0036] In some embodiments, the quotient of the transportation route distance and the operating speed of the transportation equipment is calculated: The distance of the transport route X / Y ÷ the forklift speed = 1.2km ÷ 0.3km / min = 4min; Z's transport route distance ÷ forklift operating speed = 0.8km ÷ 0.3km / min ≈ 2.67min.
[0037] A12. Calculate the sum of the first calculation result and the fixed loading and unloading time in the transportation condition data to obtain the transportation time of a single raw material among all raw materials.
[0038] In some embodiments, the transportation time for a single raw material is calculated: The transportation time for X is 4 min + 5 min = 9 min; the transportation time for Y is 4 min + 5 min = 9 min; the transportation time for Z is approximately 2.67 min + 5 min = 7.67 min.
[0039] A13. Based on the transportation time of the single raw material, iterate through all raw materials to obtain the total transportation time of all raw materials.
[0040] In some embodiments, all raw materials are traversed to calculate the total transportation time: Total transportation time = 9 min (X) + 9 min (Y) + 7.67 min (Z) = 25.67 min.
[0041] A14. Determine the first sub-objective function based on the total transportation time.
[0042] In some embodiments, the first sub-objective function is determined as F1=min(T(total)), where T(total) is the total transportation time of all raw materials, and the objective is to minimize T(total).
[0043] A2. Based on the raw material inventory data and the production demand data, construct the second sub-objective function with the goal of maximizing the raw material inventory turnover rate.
[0044] In some embodiments, the construction of the second sub-objective function based on the raw material inventory data and the production demand data, with the goal of maximizing the raw material inventory turnover rate, includes the steps A21 to A22 shown below: A21. Calculate the quotient between the total raw material demand in the production demand data and the average inventory in the raw material inventory data to obtain the raw material inventory turnover rate, wherein the average inventory is half of the sum of the initial inventory in the raw material inventory data and the final inventory in the raw material inventory data.
[0045] In some embodiments, the raw material inventory turnover rate is calculated: Average inventory calculation: Average inventory of X = (5000kg + 3000kg) ÷ 2 = 4000kg; Average inventory of Y = (3000 pieces + 1800 pieces) ÷ 2 = 2400 pieces; Average inventory of Z = (1200 pieces + 600 pieces) ÷ 2 = 900 pieces.
[0046] Inventory turnover rate calculation: X turnover rate = 6000kg ÷ 4000kg = 1.5; Y turnover rate = 2600 pieces ÷ 2400 pieces ≈ 1.08; Z turnover rate = 1300 pieces ÷ 900 pieces ≈ 1.44.
[0047] Overall inventory turnover rate (weighted average) = (1.5×0.5) + (1.08×0.3) + (1.44×0.2) = 1.356 (weights are set according to the proportion of raw material value).
[0048] A22. Determine the second sub-objective function based on the raw material inventory turnover rate.
[0049] In some embodiments, the second sub-objective function is determined as F2=max(R(total)), where R(total) is the raw material inventory turnover rate, and the objective is to maximize R(total).
[0050] A3. Based on the preset first weight coefficient corresponding to the first sub-objective function and the preset second weight coefficient corresponding to the second sub-objective function, the first sub-objective function and the second sub-objective function are weighted and summed to obtain the objective function.
[0051] In some embodiments, the two sub-objective functions are weighted and summed based on a preset first weight coefficient (0.4) and a second weight coefficient (0.6) (the reciprocal of F1 is first normalized to unify the objective as maximization): The normalized F1 = 1 / T(total) = 1 / 25.67, the normalized F2 = R(total) / 2 = 1.356 ÷ 2 = 0.678, then the objective function F = 0.4 × normalized F1 + 0.6 × normalized F2 = 0.4 × 0.039 + 0.6 × 0.678 = 0.4224.
[0052] A4. Based on the raw material inventory data and the transportation condition data, construct the constraints.
[0053] In some embodiments, the constraints include supply constraints and transportation constraints; The process of constructing the constraints based on the raw material inventory data and the transportation condition data includes the steps A41 to A42 shown below: A41. Based on the raw material inventory data, construct the supply constraint conditions.
[0054] In some embodiments, the supply constraints are: raw material allocation quantity ≤ current inventory quantity, i.e., X allocation quantity ≤ 4200 kg, Y allocation quantity ≤ 2500 pieces, and Z allocation quantity ≤ 900 pieces.
[0055] A42. Based on the transportation condition data, construct the transportation constraints.
[0056] In some embodiments, the transportation constraints are as follows: the total weight of raw materials transported in a single trip is less than or equal to the rated load of the forklift, i.e., X / Y single trip ≤ 1000 kg / trip, Z single trip ≤ 500 pieces / trip.
[0057] S130. Based on the constraints and the objective function, solve the objective function to obtain the initial raw material allocation scheme.
[0058] In some embodiments, by combining constraints and the overall objective function, the initial raw material allocation scheme is obtained by traversing the allocation combinations that satisfy the constraints. Raw material allocation quantities: X allocates 4200kg (transported in 5 batches, 800kg each time), Y allocates 2500 pieces (transported in 3 batches, 800 pieces in the first 2 batches and 900 pieces in the last batch), Z allocates 900 pieces (transported in 2 batches, 500 pieces in the first batch and 400 pieces in the second batch). Transportation scheduling: Forklift 1 is responsible for X / Y transportation, and forklift 2 is responsible for Z transportation; the time window for X / Y transportation is 8:00-8:45, and the time window for Z transportation is 8:00-8:20. Supply priority: X (core raw material for Model A production) > Y > Z.
[0059] S140. By using a preset allocation effect prediction model, based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan, a predicted allocation effect index is obtained.
[0060] In some embodiments, such as Figure 2 The preset adjustment effect prediction model includes an input layer, a hidden layer, and an output layer.
[0061] Specifically, the step of obtaining predicted allocation effect indicators by using a preset allocation effect prediction model, based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan, includes the steps B1 to B3 as shown below: B1. Through the input layer, a normalized input vector is obtained based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan.
[0062] In some embodiments, the raw material inventory data (4200kg, 2500 pieces, 900 pieces), production demand data (6000kg, 2600 pieces, 1300 pieces), transportation condition data (1.2km, 0.8km, 0.3km / min), environmental data (25℃, 60%), and initial allocation scheme parameters (allocation quantity, number of transportations, time window) are normalized through the input layer to obtain an 8-dimensional normalized input vector: [0.84, 0.83, 0.75, 0.6, 0.93, 0.4, 0.5, 0.6] (each dimension corresponds to the normalized value of the above data).
[0063] B2. The output feature vector is obtained through the hidden layer based on the normalized input vector.
[0064] In some embodiments, after weight matrix operations and nonlinear transformations of the hidden layer (which may contain a 2-layer sub-hidden layer structure, with 17 nodes in the first layer and 8 nodes in the second layer, and the activation function being ReLU), an 8-dimensional output feature vector is obtained: [0.21, 0.56, 0.78, 0.32, 0.89, 0.45, 0.67, 0.91].
[0065] B3. Through the output layer, the predicted allocation effect index is obtained based on the output feature vector, wherein the predicted allocation effect index includes the predicted value of allocation time error rate and the predicted value of inventory turnover rate compliance rate.
[0066] In some embodiments, after processing by the output layer (4 nodes, activation function is Sigmoid), the predicted allocation effect indicators are output: the predicted allocation time error rate is 3.2% (normalized to 0.032), and the predicted inventory turnover rate is 92% (normalized to 0.92).
[0067] S150. Based on the predicted allocation effect index and the preset allocation effect index threshold, process the initial raw material allocation scheme to obtain the target raw material allocation scheme.
[0068] In some embodiments, the process of processing the initial raw material allocation plan based on the predicted allocation effect index and a preset allocation effect index threshold to obtain the target raw material allocation plan includes the following steps C1 to C2: C1. Normalize the predicted values of the allocation time error rate and the inventory turnover rate to obtain the allocation effect index score.
[0069] In some embodiments, the predicted allocation effect index is normalized to obtain the allocation effect index score = (normalized value of allocation time error rate × 0.4) + (normalized value of inventory turnover rate compliance rate × 0.6) = (0.032 × 0.4) + (0.92 × 0.6) = 0.5648. Among them, the lower the time error rate, the better, so the original value is directly used in the calculation.
[0070] C2. Compare the blending effect index score with the preset blending effect index threshold. If the blending effect index score is greater than the preset blending effect index threshold, then determine the initial raw material blending scheme as the target raw material blending scheme. If the blending effect index score is less than or equal to the preset blending effect index threshold, then adjust the preset first weight coefficient or the preset second weight coefficient to update the initial raw material blending scheme and make the blending effect index score corresponding to the initial raw material blending scheme greater than the preset blending effect index threshold.
[0071] In some embodiments, since 0.5648 < 0.85, the preset weighting coefficients need to be adjusted—the first weighting coefficient (allocation time) is reduced from 0.4 to 0.2, and the second weighting coefficient (inventory turnover rate) is increased from 0.6 to 0.8. The objective function is then resolved and the initial allocation scheme is updated. That is, the new objective function F′=0.2×0.039+0.8×0.678=0.549, and the new initial allocation plan is adjusted as follows: X allocates 4200kg (in 4 transports, 1000kg each time), Y allocates 2500 pieces (in 3 transports, 800 / 800 / 900 pieces each time), and Z allocates 900 pieces (in 2 transports, 500 / 400 pieces each time); forklift 1 is responsible for the overall transportation of X / Y / Z, and the total transportation time is shortened to 20 minutes after optimizing the transportation route.
[0072] The updated initial allocation plan was input again into the preset allocation effect prediction model, resulting in a new allocation effect index score of 0.88 (>0.85). Therefore, this updated initial allocation plan is the target raw material allocation plan. Ultimately, the factory completed the raw material allocation based on this target plan, reducing the actual allocation time by 30% compared to the original manual plan and increasing inventory turnover by 25%, achieving a dual optimization of allocation efficiency and accuracy.
[0073] In summary, this application provides a raw material allocation method based on the Industrial Internet of Things, which can simultaneously improve the allocation efficiency and accuracy of raw materials in factories.
[0074] To better implement the above methods, this application also provides a raw material allocation device based on the Industrial Internet of Things (IIoT). This device can be integrated into an electronic device, such as a terminal or server. The terminal can be a mobile phone, tablet, smart Bluetooth device, laptop, personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers.
[0075] For example, in this embodiment, the method of this application embodiment will be described in detail by taking the integration of a raw material allocation device based on the Industrial Internet of Things into a terminal as an example.
[0076] For example, such as Figure 3 As shown, the raw material dispensing device 300 based on the Industrial Internet of Things may include a first unit 301, a second unit 302, a third unit 303, a fourth unit 304, and a fifth unit 305. The device includes: The first unit is used to obtain the raw material inventory data, production demand data, transportation condition data and environmental data of the factory. The second unit is used to construct an objective function and constraints based on the raw material inventory data, the production demand data, the transportation condition data, and the environmental data, with the goal of minimizing the raw material allocation time and maximizing the raw material inventory turnover rate. The third unit is used to solve the objective function based on the constraints and the objective function, and obtain the initial raw material allocation scheme. The fourth unit is used to obtain the predicted allocation effect index based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan by using a preset allocation effect prediction model. The fifth unit is used to process the initial raw material allocation plan based on the predicted allocation effect index and the preset allocation effect index threshold to obtain the target raw material allocation plan.
[0077] In practice, each of the above units can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units, please refer to the previous method embodiments, which will not be repeated here.
[0078] As can be seen from the above, the embodiments of this application can simultaneously improve the efficiency and accuracy of raw material allocation in the factory.
[0079] This application also provides an electronic device, which can be a terminal, a server, or other similar device. The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers, etc.
[0080] In some embodiments, the product processing device may also be integrated into multiple electronic devices, such as multiple servers, with the multiple servers implementing the raw material allocation method based on the Industrial Internet of Things of this application.
[0081] In this embodiment, the electronic device will be described in detail as a terminal, for example, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the terminal 400 involved in an embodiment of this application. Specifically: The terminal 400 may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more media, a power supply 403, an input module 404, and a communication module 405. Those skilled in the art will understand that... Figure 4 The terminal 400 structure shown does not constitute a limitation on the terminal 400, and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 401 is the control center of the terminal 400. It connects various parts of the terminal 400 via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 402, and by calling data stored in the memory 402, thereby providing overall monitoring of the terminal 400. In some embodiments, the processor 401 may include one or more processing cores; in some embodiments, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 401.
[0082] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the terminal 400, etc. In addition, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.
[0083] The terminal 400 also includes a power supply 403 that supplies power to the various components. In some embodiments, the power supply 403 can be logically connected to the processor 401 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 403 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.
[0084] The terminal 400 may also include an input module 404, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
[0085] The terminal 400 may also include a communication module 405. In some embodiments, the communication module 405 may include a wireless module. The terminal 400 can perform short-range wireless transmission through the wireless module of the communication module 405, thereby providing users with wireless broadband Internet access. For example, the communication module 405 can be used to help users send and receive emails, browse web pages, and access streaming media.
[0086] Although not shown, terminal 400 may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, processor 401 in terminal 400 loads the executable files corresponding to the processes of one or more applications into memory 402 according to the following instructions, and processor 401 runs the applications stored in memory 402 to realize various functions, as follows: Obtain the raw material inventory data, production demand data, transportation condition data, and environmental data corresponding to the factory. Based on the raw material inventory data, the production demand data, the transportation condition data, and the environmental data, an objective function and constraints are constructed with the goal of minimizing raw material allocation time and maximizing raw material inventory turnover. Based on the constraints and the objective function, the objective function is solved to obtain the initial raw material allocation scheme. By using a preset allocation effect prediction model, based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan, the predicted allocation effect index is obtained. Based on the predicted allocation effect index and the preset allocation effect index threshold, the initial raw material allocation plan is processed to obtain the target raw material allocation plan.
[0087] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.
[0088] As can be seen from the above, the embodiments of this application can simultaneously improve the efficiency and accuracy of raw material allocation in the factory.
[0089] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be accomplished by instructions, or by instructions controlling related hardware. These instructions can be stored in a medium and loaded and executed by a processor.
[0090] To this end, embodiments of this application provide a medium storing multiple instructions that can be loaded by a processor to execute steps in any of the raw material allocation methods based on the Industrial Internet of Things provided in embodiments of this application. For example, the instructions can execute the following steps: Obtain the raw material inventory data, production demand data, transportation condition data, and environmental data corresponding to the factory. Based on the raw material inventory data, the production demand data, the transportation condition data, and the environmental data, an objective function and constraints are constructed with the goal of minimizing raw material allocation time and maximizing raw material inventory turnover. Based on the constraints and the objective function, the objective function is solved to obtain the initial raw material allocation scheme. By using a preset allocation effect prediction model, based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan, the predicted allocation effect index is obtained. Based on the predicted allocation effect index and the preset allocation effect index threshold, the initial raw material allocation plan is processed to obtain the target raw material allocation plan.
[0091] The medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0092] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a medium. A processor of a computer device reads the computer instructions from the medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations of the above embodiments.
[0093] Since the instructions stored in the medium can execute the steps in any of the raw material allocation methods based on the Industrial Internet of Things provided in the embodiments of this application, the beneficial effects that any of the raw material allocation methods based on the Industrial Internet of Things provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.
[0094] The above provides a detailed description of a raw material allocation method, apparatus, terminal, and medium based on the Industrial Internet of Things (IIoT) provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A raw material allocation method based on the Industrial Internet of Things, characterized in that, The method includes: Obtain the raw material inventory data, production demand data, transportation condition data, and environmental data corresponding to the factory. Based on the raw material inventory data, the production demand data, the transportation condition data, and the environmental data, an objective function and constraints are constructed with the goal of minimizing raw material allocation time and maximizing raw material inventory turnover. Based on the constraints and the objective function, the objective function is solved to obtain the initial raw material allocation scheme. By using a preset allocation effect prediction model, based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan, the predicted allocation effect index is obtained. Based on the predicted allocation effect index and the preset allocation effect index threshold, the initial raw material allocation plan is processed to obtain the target raw material allocation plan.
2. The method as described in claim 1, characterized in that, Based on the raw material inventory data, transportation condition data, and environmental data, an objective function and constraints are constructed with the goal of minimizing raw material allocation time and maximizing raw material inventory turnover rate. These constraints include: Based on the transportation condition data and the environmental data, a first sub-objective function is constructed with the goal of minimizing the raw material allocation time. Based on the raw material inventory data and the production demand data, a second sub-objective function is constructed with the goal of maximizing the raw material inventory turnover rate. Based on the preset first weight coefficient corresponding to the first sub-objective function and the preset second weight coefficient corresponding to the second sub-objective function, the first sub-objective function and the second sub-objective function are weighted and summed to obtain the objective function. The constraints are constructed based on the raw material inventory data and the transportation condition data.
3. The method as described in claim 2, characterized in that, Based on the transportation condition data and the environmental data, and with the goal of minimizing the raw material allocation time, the first sub-objective function is constructed, including: The first calculation result is obtained by calculating the quotient of the transportation route distance in the environmental data and the transportation equipment operating speed in the transportation condition data; The first calculation result is summed with the fixed loading and unloading time in the transportation condition data to obtain the transportation time of a single raw material among all raw materials. Based on the transportation time of the single raw material, the total transportation time of all raw materials is obtained by iterating through all raw materials. The first sub-objective function is determined based on the total transportation time.
4. The method as described in claim 2, characterized in that, Based on the raw material inventory data and the production demand data, and with the goal of maximizing the raw material inventory turnover rate, the second sub-objective function is constructed, including: The raw material inventory turnover rate is obtained by calculating the quotient between the total raw material demand in the production demand data and the average inventory in the raw material inventory data, wherein the average inventory is half of the sum of the initial inventory in the raw material inventory data and the final inventory in the raw material inventory data. The second sub-objective function is determined based on the raw material inventory turnover rate.
5. The method as described in claim 2, characterized in that, The constraints include supply constraints and transportation constraints. The constraint conditions are constructed based on the raw material inventory data and the transportation condition data, including: Based on the raw material inventory data, the supply constraints are constructed. Based on the transportation condition data, the transportation constraints are constructed.
6. The method as described in claim 1, characterized in that, The preset adjustment effect prediction model includes an input layer, a hidden layer, and an output layer; The step of obtaining predicted allocation effect indicators through a preset allocation effect prediction model, based on the raw material inventory data, production demand data, transportation condition data, environmental data, and the initial raw material allocation plan, includes: Through the input layer, a normalized input vector is obtained based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan. The output feature vector is obtained through the hidden layer based on the normalized input vector; The predicted allocation effect index is obtained through the output layer based on the output feature vector. The predicted allocation effect index includes the predicted value of allocation time error rate and the predicted value of inventory turnover rate achievement rate.
7. The method as described in claim 6, characterized in that, The step of processing the initial raw material allocation plan based on the predicted allocation effect index and the preset allocation effect index threshold to obtain the target raw material allocation plan includes: The predicted values of the allocation time error rate and the inventory turnover rate are normalized to obtain the allocation effect index score. The blending effect index score is compared with the preset blending effect index threshold. If the blending effect index score is greater than the preset blending effect index threshold, the initial raw material blending scheme is determined to be the target raw material blending scheme. If the blending effect index score is less than or equal to the preset blending effect index threshold, the preset first weight coefficient or the preset second weight coefficient is adjusted to update the initial raw material blending scheme and make the blending effect index score corresponding to the initial raw material blending scheme greater than the preset blending effect index threshold.
8. A raw material dispensing device based on the Industrial Internet of Things, characterized in that, The device includes: The first unit is used to obtain the raw material inventory data, production demand data, transportation condition data and environmental data of the factory. The second unit is used to construct an objective function and constraints based on the raw material inventory data, the production demand data, the transportation condition data, and the environmental data, with the goal of minimizing the raw material allocation time and maximizing the raw material inventory turnover rate. The third unit is used to solve the objective function based on the constraints and the objective function, and obtain the initial raw material allocation scheme. The fourth unit is used to obtain the predicted allocation effect index based on the raw material inventory data, the production demand data, the transportation condition data, the environmental data, and the initial raw material allocation plan by using a preset allocation effect prediction model. The fifth unit is used to process the initial raw material allocation plan based on the predicted allocation effect index and the preset allocation effect index threshold to obtain the target raw material allocation plan.
9. A terminal, characterized in that, The method includes a processor and a memory, the memory storing multiple instructions; the processor loads instructions from the memory to perform the steps of the method as described in any one of claims 1 to 7.
10. A medium, characterized in that, The medium stores a plurality of instructions adapted for loading by a processor to execute the steps of the method according to any one of claims 1 to 7.