Warehousing operation and maintenance management system and method applied to intelligent manufacturing

Abstract

The invention discloses a warehouse operation and maintenance management system and method applied to intelligent manufacturing, and relates to the technical field of intelligent warehouse management, and the system comprises a data collection module, a data processing module, an inventory demand calculation module, an operation and maintenance strategy making module and a closed-loop execution optimization module. According to the invention, through the data acquisition module, comprehensive coverage and real-time acquisition of data of a demand end, a material end, an equipment end and a logistics end are realized, the data processing module processes the acquired multi-dimensional original data and eliminates abnormal values and noise, and the inventory demand calculation module uses an inventory threshold core algorithm and an inventory demand priority scoring algorithm to calculate the inventory demand. The inventory threshold and the priority score of each material are calculated, and then the inventory demand thermodynamic diagram is generated, so that the inventory state is more intuitive, the operation and maintenance strategy making module flexibly adjusts the warehousing replenishment rhythm and the material delivery sequence, efficient utilization of resources is realized, and the closed-loop execution optimization module ensures continuous optimization of the management process.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehouse management technology, specifically to a warehouse operation and maintenance management system and method applied to intelligent manufacturing. Background Technology

[0002] With the rapid development of intelligent manufacturing technology, modern manufacturing has placed higher demands on the intelligence and precision of warehouse management. Enterprises need to achieve efficient collaboration in production, logistics, and warehousing through digital means. In the context of intelligent manufacturing, the warehouse system needs to perceive multi-dimensional data from the demand side, material side, equipment side, and logistics side in real time, and optimize inventory strategies and operational efficiency through dynamic analysis. Currently, the integration of technologies such as the Internet of Things, big data, and artificial intelligence provides technical support for warehouse management. How to build an intelligent operation and maintenance system that covers the entire chain of data and achieves closed-loop optimization has become a key industry requirement.

[0003] However, traditional warehouse management systems typically rely on a single data source or static rules, making it difficult to comprehensively cover dynamic information from the demand side, material side, equipment side, and logistics side. For example, predicting demand solely based on historical order data ignores variables such as real-time production line fluctuations and supplier replenishment delays, leading to inaccurate inventory threshold calculations. Or, planning the outbound sequence solely based on the first-in-first-out principle fails to consider priority factors such as material shelf life and order urgency, easily resulting in resource waste or delivery delays. Furthermore, traditional systems lack closed-loop optimization mechanisms, separating data collection from strategy execution, making it impossible to dynamically adjust the replenishment rhythm or outbound sequence based on actual execution results, leading to stagnant operational efficiency. In terms of data quality, outliers, missing values, and noisy data are not effectively handled, further reducing the reliability of inventory demand calculations. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a warehouse operation and maintenance management system and method for intelligent manufacturing. This invention achieves comprehensive coverage and real-time acquisition of data from the demand side, material side, equipment side, and logistics side through a data acquisition module. The data processing module processes the collected multi-dimensional raw data, eliminating outliers and noise, thus improving data quality. The inventory demand calculation module uses a core inventory threshold algorithm and an inventory demand priority scoring algorithm to calculate the inventory threshold and priority score for each material, thereby generating an intuitive inventory demand heatmap, making the inventory status more intuitive. The operation and maintenance strategy formulation module flexibly adjusts the warehouse replenishment rhythm and material outbound sequence, achieving efficient resource utilization. The closed-loop execution optimization module ensures continuous optimization of the management process, forming an intelligent operation and maintenance closed loop, and improving inventory management and operation and maintenance efficiency.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On one hand, a warehouse operation and maintenance management system applied to intelligent manufacturing, the system comprising: Data acquisition module: Collects multi-dimensional raw data from the demand side, material side, equipment side and logistics side through API interface, sensor, direct database connection and IoT device; Data processing module: performs outlier handling, missing value filling, format standardization, magnitude balancing, and noise filtering on the collected multi-dimensional raw data, and finally outputs a standardized dataset; Inventory demand calculation module: Based on a standardized dataset, it calculates the inventory threshold for each material using the core inventory threshold algorithm, and then calculates the inventory demand priority score for each material using the inventory demand priority scoring algorithm. It generates an inventory demand heatmap with the physical layout of the warehouse racks as the spatial dimension and the inventory demand priority score as the quantitative dimension. Operation and maintenance strategy formulation module: Based on the inventory demand heat map and the inventory threshold of each material, determine the warehouse replenishment rhythm adjustment plan, and then calculate the outbound priority comprehensive score through the outbound priority comprehensive scoring algorithm to determine the material outbound sequence planning plan. Closed-loop execution optimization module: Executes the warehouse replenishment rhythm adjustment plan and the material outbound sequence planning plan, and collects multi-dimensional raw data during the execution process. It then redefines the warehouse replenishment rhythm adjustment plan and the material outbound sequence planning plan, forming a management process of data collection, data processing, inventory demand calculation, operation and maintenance strategy formulation, instruction execution, and closed-loop optimization.

[0006] Furthermore, in the data acquisition module, the demand-side data includes the material requirements of the production line MES orders, the delivery cycle of downstream customers, and the historical order requirements; the material-side data includes the material shelf life, the frequency of inbound and outbound operations, and the material storage status; the equipment-side data includes the AGV handling speed, the temperature and humidity of the storage environment, the shelf storage capacity, and the shelf storage occupancy status; and the logistics-side data includes the supplier replenishment cycle and the real-time location of materials in transit.

[0007] Furthermore, the data acquisition module collects material requirements from production line MES orders, downstream customer delivery cycles, and supplier replenishment cycles from the logistics side via API interfaces. It also collects real-time AGV handling speed, warehouse temperature and humidity, and material storage status data from the equipment side via sensors. In addition, it collects historical order demand from the demand side, material shelf life and inbound / outbound frequency data from the material side, and shelf storage capacity data from the equipment side via direct database connection. Finally, it collects real-time location data of materials in transit and shelf storage occupancy status data from the equipment side via IoT devices.

[0008] Furthermore, in the inventory demand calculation module, the calculation formula for the core algorithm of the inventory threshold is as follows: ,in, For the first Inventory threshold for a type of material This is the demand weighting coefficient, with a value ranging from 0.6 to 0.8. This is the fluctuation weighting coefficient, with a value ranging from 0.1 to 0.3. This is the logistics weighting coefficient, with a value ranging from 0.1 to 0.2. This is the equipment weighting coefficient, with a value ranging from 0.05 to 0.15. For the first The average daily demand for this material is calculated using the order demand from the production line's MES system and the delivery cycle of downstream customers. For the first The historical order volatility coefficient for this material is calculated by taking the standard deviation and mean of historical order demand. For the first The supplier replenishment cycle for this type of material. For the first The shelf life of this type of material. For the first The frequency of inbound and outbound movement of various materials For the first The AGV handling efficiency coefficient for a certain material is calculated by the average handling speed and the maximum handling speed of the AGV. This represents the total storage capacity of the shelving.

[0009] Furthermore, in the inventory demand calculation module, the calculation formula for the inventory demand priority scoring algorithm is as follows: ,in, For the first Inventory demand priority score for each type of material For the first Inventory threshold for a type of material For the first The actual inventory of the materials.

[0010] Furthermore, the specific steps for generating the inventory demand heatmap in the inventory demand calculation module are as follows: The storage area is divided into independent grid units according to the physical layout. Each grid unit is assigned a unique identification code. The coding rule is shelf number-layer number-column number-row number. A correspondence between the identification code and the actual storage location is established. The database links the material code, material name, and real-time actual inventory quantity stored within each grid cell. And the priority score of inventory demand for materials ; Define the three-color gradient color coding rules. When filled with dark red, it corresponds to a state of inventory shortage; When orange-yellow is filled in, it corresponds to an inventory warning status; When filled with dark green, it indicates a sufficient inventory status; A 3D model of the warehouse area is constructed using WebGL technology. The color identifiers of each grid cell are mapped to the corresponding storage locations in the 3D model, generating inventory demand heatmaps in both 2D and 3D formats. These heatmaps display the material codes and real-time inventory of the corresponding grid cells. Inventory threshold Inventory demand priority score and remaining shelf life .

[0011] Furthermore, in the operation and maintenance strategy formulation module, the specific steps for determining the warehouse replenishment rhythm adjustment plan are as follows: based on the actual inventory of materials... With inventory threshold ratio Develop differentiated replenishment plans, when Emergency replenishment will be carried out when necessary; Regular replenishment is carried out when necessary; At that time, maintain the current replenishment pace, when At that time, replenishment will be suspended until... It was later lifted.

[0012] Furthermore, in the operation and maintenance strategy formulation module, the specific steps for determining the material outbound sequence planning scheme are as follows: The outbound priority score of the material is calculated using an outbound priority comprehensive scoring algorithm. The higher the outbound priority score, the higher the priority for outbound delivery. The calculation formula for the outbound priority comprehensive scoring algorithm is: ,in, For the first The priority score for the outbound delivery of various materials. This is the weighting factor for the pledged option, with a value ranging from 0.3 to 0.5. This is the order urgency weighting coefficient, with a value ranging from 0.3 to 0.5. This is a weighting coefficient for handling efficiency, with a value ranging from 0.1 to 0.2. For the first The remaining shelf life of the material. For the first The urgency quantification value for a particular material is determined by the difference between the planned delivery time of the order and the current time.

[0013] On the other hand, the warehouse operation and maintenance management method applied to intelligent manufacturing has the following specific steps: Data Acquisition: Collect multi-dimensional raw data from the demand side, material side, equipment side, and logistics side through API interfaces, sensors, direct database connections, and IoT devices; Data processing: The collected multi-dimensional raw data is processed for outliers, missing values ​​are filled in, format is standardized, magnitude is balanced and noise is filtered, and finally a standardized dataset is output. Inventory demand calculation: Based on a standardized dataset, the inventory threshold of each material is calculated using the final inventory threshold core algorithm, and the inventory demand priority score of each material is calculated using the inventory demand priority scoring algorithm. An inventory demand heat map is generated with the physical layout of the warehouse racks as the spatial dimension and the inventory demand priority score as the quantitative dimension. Operation and maintenance strategy formulation: Based on the inventory demand heat map and the inventory threshold of each material, determine the warehouse replenishment rhythm adjustment plan, and then calculate the outbound priority comprehensive score through the outbound priority comprehensive scoring formula to determine the material outbound sequence planning plan. Closed-loop execution optimization: Implement the warehouse replenishment rhythm adjustment plan and material outbound sequence planning plan, and collect multi-dimensional raw data during the execution process to redetermine the warehouse replenishment rhythm adjustment plan and material outbound sequence planning plan, forming a management process of data collection - data processing - inventory demand calculation - operation and maintenance strategy formulation - instruction execution - closed-loop optimization.

[0014] Compared with existing technologies, this warehouse operation and maintenance management system and method applied to intelligent manufacturing has the following beneficial effects: This invention achieves comprehensive coverage and real-time acquisition of data from the demand, material, equipment, and logistics ends through a data acquisition module. The data processing module processes the collected multi-dimensional raw data, eliminating outliers and noise to improve data quality. The inventory demand calculation module uses a core inventory threshold algorithm and an inventory demand priority scoring algorithm to calculate the inventory threshold and priority score for each material, thereby generating an intuitive inventory demand heatmap, making inventory status more transparent. The operation and maintenance strategy formulation module flexibly adjusts the warehousing replenishment rhythm and material outbound sequence, achieving efficient resource utilization. The closed-loop execution optimization module ensures continuous optimization of the management process, forming an intelligent operation and maintenance closed loop and improving inventory management and operation and maintenance efficiency.

[0015] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 A flowchart for warehouse operation and maintenance management methods applied to intelligent manufacturing; Figure 2 This is a framework diagram of a warehouse operation and maintenance management system applied to intelligent manufacturing. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0019] Example 1: In the scenario of warehouse operation and maintenance management in an intelligent manufacturing plant for automotive parts, the plant mainly produces core components such as engine blocks, transmission gears, rubber seals, and precision bearings, serving multiple vehicle manufacturers. Firstly, it comprehensively collects multi-dimensional raw data from the demand side, material side, equipment side, and logistics side through four methods: API interfaces, sensors, direct database connections, and IoT devices. Demand-side data includes the order material requirements for various components such as engine blocks and transmission gears in the production line's MES, the delivery cycles of downstream vehicle manufacturers, and historical order demand over the past year. Material-side data includes the shelf life of rubber seals, the frequency of entry and exit of standard parts such as bolts and nuts, and the storage status of bearings. Equipment-side data includes the handling speed of AGVs responsible for component transfer within the plant area, real-time temperature and humidity in the warehouse, storage capacity of different shelves, and the current storage occupancy status of each shelf. Logistics-side data includes the supplier's bolt and nut replenishment cycle, the supplier's engine block replenishment cycle, and the real-time location information of components in transit.

[0020] The collected multi-dimensional raw data is processed, including outlier handling for abnormally high or low AGV handling speeds caused by temporary equipment malfunctions; supplementing missing demand data for niche components in some historical orders; standardizing the formats of data from different sources; balancing data of different magnitudes, such as daily demand and replenishment cycles; and filtering out noise data such as instantaneous fluctuations caused by personnel temporarily opening doors in warehouse temperature and humidity monitoring. The final output is a standardized dataset, such as... Figure 1 As shown.

[0021] Based on a standardized dataset, the inventory threshold for each material, including engine blocks, rubber seals, and precision bearings, is calculated using a core inventory threshold algorithm. The formula for this algorithm is as follows: ,in, For the first Inventory threshold for a type of material This is the demand weighting coefficient, with a value ranging from 0.6 to 0.8. This is the fluctuation weighting coefficient, with a value ranging from 0.1 to 0.3. This is the logistics weighting coefficient, with a value ranging from 0.1 to 0.2. This is the equipment weighting coefficient, with a value ranging from 0.05 to 0.15. For the first The average daily demand for this material is calculated using the order demand from the production line's MES system and the delivery cycle of downstream customers. For the first The historical order volatility coefficient for this material is calculated by taking the standard deviation and mean of historical order demand. For the first The supplier replenishment cycle for this type of material. For the first The shelf life of this type of material. For the first The frequency of inbound and outbound movement of various materials For the first The AGV handling efficiency coefficient for a certain material is calculated by the average handling speed and the maximum handling speed of the AGV. The total shelf storage capacity is given; then, the inventory demand priority score for each material is calculated using an inventory demand priority scoring algorithm. The formula for the inventory demand priority scoring algorithm is as follows: ,in, For the first Inventory demand priority score for each type of material For the first Inventory threshold for a type of material For the first The actual inventory of each material is determined. Next, the entire storage area is divided into independent grid units according to the physical layout of the shelving. Each grid unit is assigned a unique identifier code, establishing a correspondence between the code and the actual storage location. Then, the database is used to link the material code, material name, real-time actual inventory quantity, and corresponding inventory demand priority score stored within each grid unit. Following a set three-color gradient color coding rule, the storage area for rubber seals with an inventory demand priority score higher than 60 is filled with dark red, corresponding to an inventory shortage; the storage area for gearbox gears with a score between 30 and 60 is filled with orange-yellow, corresponding to an inventory warning; and the storage area for bolts and nuts with a score lower than 30 is filled with dark green, corresponding to an adequate inventory. Finally, a 3D model of the storage area is constructed using WebGL technology, mapping the color codes of each grid unit to the corresponding storage locations in the 3D model. This generates inventory demand heatmaps in both 2D and 3D formats, clearly displaying key information such as the material code, real-time inventory, inventory threshold, inventory demand priority score, and remaining shelf life for each grid unit.

[0022] Based on the inventory demand heatmap and inventory thresholds for each material, a warehouse replenishment schedule adjustment plan is determined: for example, for rubber seals marked in dark red, if the ratio of actual inventory to the inventory threshold is 30%, emergency replenishment is initiated, and suppliers are contacted to expedite the allocation of goods; for gearbox gears marked in orange-yellow, and... Perform the standard replenishment process and place purchase orders with suppliers according to the preset replenishment cycle; for bolts and nuts marked in dark green, and At the same time, maintain the current replenishment pace; however, for some older models of bearings with accumulated inventory, and Suspend replenishment until inventory drops to 100%-120% of its original level, then lift the suspension. Next, calculate the outbound priority score for each material using an outbound priority comprehensive scoring algorithm to determine the material outbound sequence planning scheme. The calculation formula for the outbound priority comprehensive scoring algorithm is as follows: ,in, For the first The priority score for the outbound delivery of various materials. This is the weighting factor for the pledged option, with a value ranging from 0.3 to 0.5. This is the order urgency weighting coefficient, with a value ranging from 0.3 to 0.5. This is a weighting coefficient for handling efficiency, with a value ranging from 0.1 to 0.2. For the first The remaining shelf life of the material. For the first The quantifiable value of the urgency of orders for each type of material is determined by the difference between the planned delivery time of the order and the current time. For example, rubber seals with short remaining shelf life and engine blocks corresponding to urgent orders from downstream vehicle manufacturers have high scores in shelf life and order urgency, respectively. Bolts and nuts with high handling efficiency have high scores in handling efficiency, so all three have high priority scores for outbound shipment and are prioritized for shipment. On the other hand, large engine blocks with long remaining shelf life, no urgent order association, and high handling difficulty have relatively low priority for outbound shipment and are scheduled for shipment later.

[0023] Operations were executed according to the established warehouse replenishment schedule and material outbound sequence plan. Urgently procured rubber seals were received into the warehouse as planned, while engine blocks and rubber seals required for urgent orders were prioritized for outbound delivery. Throughout the execution of the plan, multi-dimensional raw data was continuously collected, including the actual arrival time of replenished materials, their storage status after receipt, AGV handling efficiency during outbound processes, and order delivery completion status. This newly collected multi-dimensional data was processed, and the inventory thresholds and inventory demand priority scores for each material were recalculated, generating an updated inventory demand heatmap. Based on the new inventory demand heatmap and data, the replenishment schedule and material outbound sequence were adjusted, forming a complete management process of data collection, data processing, inventory demand calculation, operation and maintenance strategy formulation, instruction execution, and closed-loop optimization, continuously improving warehouse operation and maintenance efficiency.

[0024] In summary, in the warehousing and operation of an intelligent manufacturing plant for automotive parts, data from the demand side, material side, equipment side, and logistics side are first collected and processed to obtain a standardized dataset. Then, the data is calculated using a core algorithm for inventory thresholds and an inventory demand priority scoring algorithm to generate an inventory demand heatmap. Subsequently, based on the inventory demand heatmap and inventory thresholds, a replenishment rhythm adjustment plan and an outbound sequence planning plan are determined. During the execution of the plan, multi-dimensional raw data is continuously collected, and the plan is dynamically optimized to form a complete closed-loop management process, ensuring efficient warehousing and operation.

[0025] Example 2: In the scenario of warehouse operation and maintenance management in an intelligent manufacturing factory for electronic components, the main products of the intelligent manufacturing factory for electronic components include chip wafers, capacitors and resistors, battery accessories, connectors, etc., and customers include mobile phone brand manufacturers. First, multi-dimensional raw data is collected through API interfaces, sensors, direct database connections, and IoT devices. Among them, demand-side data includes the order material requirements of various components such as chip wafers, capacitors and resistors in the production line MES, the delivery cycle of downstream mobile phone manufacturers, and the historical order demand of the past two years; material-side data includes the shelf life of battery accessories, the frequency of inbound and outbound of capacitors and resistors, and the storage status of chip wafers; equipment-side data includes the handling speed of AGVs in the factory area, the real-time temperature and humidity of the warehouse, the storage capacity of various types of shelves, and the current storage occupancy status; logistics-side data includes the replenishment cycle of capacitors and resistors from suppliers, the replenishment cycle of chip wafers from suppliers, and the real-time location information of components in transit.

[0026] The system processes the collected multi-dimensional raw data, handles outliers for occasional abnormal data in chip wafer storage status monitoring, supplements missing historical order demand data for some customized connectors, unifies data formats across different platforms, balances data of different magnitudes such as daily demand and long-term replenishment cycles, filters out noise data such as minor fluctuations caused by the start and stop of the air conditioning system in warehouse temperature and humidity monitoring, and finally outputs a standardized data set to ensure data consistency and availability.

[0027] Based on a standardized dataset, the inventory demand calculation module first uses an inventory threshold core algorithm to calculate the inventory threshold for each material, including chip wafers, battery components, capacitors, and resistors. The calculation formula for the inventory threshold core algorithm is as follows: Then, the inventory demand priority score for each material is calculated using an inventory demand priority scoring algorithm. The calculation formula for the inventory demand priority scoring algorithm is as follows: Next, the warehouse area is divided into independent grid units according to the physical layout of the shelves. A unique identifier is assigned and a correspondence is established with the actual storage locations. The database is used to link the material code, material name, real-time actual inventory quantity, and inventory demand priority score within each grid unit. Following a three-color gradient color coding rule, for example, imported chip wafer storage areas with an inventory demand priority score higher than 60 are filled with dark red, corresponding to an inventory shortage; battery accessory storage areas with scores between 30 and 60 are filled with orange-yellow, corresponding to an inventory warning; and capacitor and resistor storage areas with scores lower than 30 are filled with dark green, corresponding to an ample inventory. Finally, a 3D model of the warehouse area is constructed using WebGL technology, mapping the color identifiers of each grid unit to the corresponding storage locations in the 3D model, generating 2D and 3D inventory demand heatmaps. Figure 2As shown, it intuitively displays information such as material code, real-time inventory, inventory threshold, inventory demand priority score, and remaining shelf life for each grid cell.

[0028] Based on the inventory demand heatmap and inventory thresholds for each material, a warehouse replenishment schedule adjustment plan is determined: for example, for imported chip wafers marked in dark red, the ratio of their actual inventory to the inventory threshold is... An emergency replenishment process was initiated, and suppliers were coordinated to expedite transportation; for battery accessories marked in orange-yellow, and Implement the standard replenishment plan, placing replenishment orders with suppliers according to the established cycle; for capacitors and resistors marked in dark green, and At the same time, maintain the current replenishment pace; for some older model connectors with accumulated inventory, and Suspend replenishment until inventory drops to 100%-120% of its original level, then lift the suspension. Regarding material outbound sequence planning, an outbound priority scoring algorithm is used to calculate the outbound priority score for each material, thereby determining the material outbound sequence planning scheme. The calculation formula for the outbound priority scoring algorithm is as follows: For example, battery accessories with short remaining shelf life and chip wafers corresponding to urgent orders for new product launches from downstream mobile phone manufacturers have high scores in terms of shelf life and order urgency. Capacitors and resistors with simple and efficient handling processes have high scores in terms of handling efficiency, so they have the highest priority scores for outbound shipment and are prioritized for shipment. On the other hand, large chip wafer pallets with long remaining shelf life, no urgent order association, and special protection required during handling have lower priority scores for outbound shipment and are scheduled for shipment later.

[0029] The established replenishment schedule adjustment plan and outbound sequence planning plan are implemented. Imported chip wafers purchased urgently are delivered to the warehouse through an expedited process. Chip wafers, battery accessories, and other items required for expedited orders are prioritized for outbound delivery. During the implementation of the plan, multi-dimensional raw data is continuously collected, processed, and the inventory thresholds and inventory demand priority scores of each material are recalculated to generate an updated inventory demand heatmap. Based on the new inventory demand heatmap and data, the replenishment schedule and outbound sequence are dynamically adjusted to form a complete closed-loop management process, continuously optimizing the efficiency of warehouse operation and maintenance.

[0030] In summary, in the warehousing and maintenance of intelligent manufacturing plants for electronic components, multi-terminal data is collected and processed into a standardized dataset. This dataset is then calculated using a core algorithm based on inventory thresholds and an inventory demand priority scoring algorithm to generate an inventory demand heatmap. Based on the inventory demand heatmap and inventory thresholds, replenishment rhythm adjustment schemes and outbound sequence planning schemes are determined. During the execution of these schemes, multi-dimensional raw data is continuously collected to dynamically optimize the schemes, forming a closed-loop management process that adapts to the storage and maintenance needs of electronic components.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A warehouse operation and maintenance management system applied to intelligent manufacturing, characterized in that, The system includes: Data acquisition module: Collects multi-dimensional raw data from the demand side, material side, equipment side and logistics side through API interface, sensor, direct database connection and IoT device; Data processing module: performs outlier handling, missing value filling, format standardization, magnitude balancing, and noise filtering on the collected multi-dimensional raw data, and finally outputs a standardized dataset; Inventory demand calculation module: Based on a standardized dataset, it calculates the inventory threshold for each material using the core inventory threshold algorithm, and then calculates the inventory demand priority score for each material using the inventory demand priority scoring algorithm. It generates an inventory demand heatmap with the physical layout of the warehouse racks as the spatial dimension and the inventory demand priority score as the quantitative dimension. Operation and maintenance strategy formulation module: Based on the inventory demand heat map and the inventory threshold of each material, determine the warehouse replenishment rhythm adjustment plan, and then calculate the outbound priority comprehensive score through the outbound priority comprehensive scoring algorithm to determine the material outbound sequence planning plan. Closed-loop execution optimization module: Executes the warehouse replenishment rhythm adjustment plan and the material outbound sequence planning plan, and collects multi-dimensional raw data during the execution process. It then redefines the warehouse replenishment rhythm adjustment plan and the material outbound sequence planning plan, forming a management process of data collection, data processing, inventory demand calculation, operation and maintenance strategy formulation, instruction execution, and closed-loop optimization.

2. The warehouse operation and maintenance management system for intelligent manufacturing according to claim 1, characterized in that, In the data acquisition module, the demand-side data includes the material requirements of the production line MES orders, the delivery cycle of downstream customers, and the historical order requirements; the material-side data includes the material shelf life, the frequency of inbound and outbound operations, and the material storage status; the equipment-side data includes the AGV handling speed, the temperature and humidity of the storage environment, the shelf storage capacity, and the shelf storage occupancy status; and the logistics-side data includes the supplier replenishment cycle and the real-time location of materials in transit.

3. The warehouse operation and maintenance management system for intelligent manufacturing according to claim 2, characterized in that, The data acquisition module collects material requirements from production line MES orders, downstream customer delivery cycles, and supplier replenishment cycles from the logistics side via API interfaces. It also collects real-time AGV handling speed, warehouse temperature and humidity, and material storage status data from the equipment side via sensors. Furthermore, it collects historical order demand from the demand side, material shelf life and inbound / outbound frequency data from the material side, and shelf storage capacity data from the equipment side via direct database connection. Finally, it collects real-time location data of materials in transit and shelf storage occupancy status data from the equipment side via IoT devices.

4. The warehouse operation and maintenance management system for intelligent manufacturing according to claim 1, characterized in that, In the inventory demand calculation module, the calculation formula for the core algorithm of the inventory threshold is as follows: ,in, For the first Inventory threshold for a type of material This is the demand weighting coefficient, with a value ranging from 0.6 to 0.

8. This is the fluctuation weighting coefficient, with a value ranging from 0.1 to 0.

3. This is the logistics weighting coefficient, with a value ranging from 0.1 to 0.

2. This is the equipment weighting coefficient, with a value ranging from 0.05 to 0.

15. For the first The average daily demand for this material is calculated using the order demand from the production line's MES system and the delivery cycle of downstream customers. For the first The historical order volatility coefficient for this material is calculated by taking the standard deviation and mean of historical order demand. For the first The supplier replenishment cycle for this type of material. For the first The shelf life of this type of material. For the first The frequency of inbound and outbound movement of various materials For the first The AGV handling efficiency coefficient for a certain material is calculated by the average handling speed and the maximum handling speed of the AGV. This represents the total storage capacity of the shelving.

5. The warehouse operation and maintenance management system for intelligent manufacturing according to claim 4, characterized in that, In the inventory demand calculation module, the calculation formula for the inventory demand priority scoring algorithm is as follows: ,in, For the first Inventory demand priority score for each type of material For the first Inventory threshold for a type of material For the first The actual inventory of the materials.

6. The warehouse operation and maintenance management system for intelligent manufacturing according to claim 5, characterized in that, The specific steps for generating the inventory demand heatmap in the inventory demand calculation module are as follows: The storage area is divided into independent grid units according to the physical layout. Each grid unit is assigned a unique identification code. The coding rule is shelf number-layer number-column number-row number. A correspondence between the identification code and the actual storage location is established. The database links the material code, material name, and real-time actual inventory quantity stored within each grid cell. And the priority score of inventory demand for materials ; Define the three-color gradient color identification rules. When filled with dark red, it corresponds to a state of inventory shortage; When orange-yellow is filled in, it corresponds to an inventory warning status; When filled with dark green, it indicates a sufficient inventory status; A 3D model of the warehouse area is constructed using WebGL technology. The color identifiers of each grid cell are mapped to the corresponding storage locations in the 3D model, generating inventory demand heatmaps in both 2D and 3D formats. These heatmaps display the material codes and real-time inventory of the corresponding grid cells. Inventory threshold Inventory demand priority score and remaining shelf life .

7. The warehouse operation and maintenance management system for intelligent manufacturing according to claim 1, characterized in that, In the operation and maintenance strategy formulation module, the specific steps for determining the warehouse replenishment rhythm adjustment plan are as follows: based on the actual inventory of materials... With inventory threshold ratio Develop differentiated replenishment plans, when Emergency replenishment will be carried out when necessary; Regular replenishment is carried out when necessary; At that time, maintain the current replenishment pace, when At that time, replenishment will be suspended until... It was later lifted.

8. The warehouse operation and maintenance management system for intelligent manufacturing according to claim 1, characterized in that, In the operation and maintenance strategy formulation module, the specific steps for determining the material outbound sequence planning scheme are as follows: The outbound priority score of the material is calculated using an outbound priority comprehensive scoring algorithm. The higher the outbound priority score, the higher the priority for outbound delivery. The calculation formula for the outbound priority comprehensive scoring algorithm is: ,in, For the first The priority score for the outbound delivery of various materials. This is the weighting factor for the pledged option, with a value ranging from 0.3 to 0.

5. This is the order urgency weighting coefficient, with a value ranging from 0.3 to 0.

5. This is a weighting coefficient for handling efficiency, with a value ranging from 0.1 to 0.

2. For the first The remaining shelf life of the material. For the first The urgency quantification value for a particular material is determined by the difference between the planned delivery time of the order and the current time.

9. A warehouse operation and maintenance management method applied to intelligent manufacturing, wherein the method is applicable to the warehouse operation and maintenance management system for intelligent manufacturing as described in any one of claims 1-8, characterized in that, The specific steps of this method are as follows: Data Acquisition: Collect multi-dimensional raw data from the demand side, material side, equipment side, and logistics side through API interfaces, sensors, direct database connections, and IoT devices; Data processing: The collected multi-dimensional raw data is processed for outliers, missing values ​​are filled in, format is standardized, magnitude is balanced and noise is filtered, and finally a standardized dataset is output. Inventory demand calculation: Based on a standardized dataset, the inventory threshold of each material is calculated using the final inventory threshold core algorithm, and the inventory demand priority score of each material is calculated using the inventory demand priority scoring algorithm. An inventory demand heat map is generated with the physical layout of the warehouse racks as the spatial dimension and the inventory demand priority score as the quantitative dimension. Operation and maintenance strategy formulation: Based on the inventory demand heat map and the inventory threshold of each material, determine the warehouse replenishment rhythm adjustment plan, and then calculate the outbound priority comprehensive score through the outbound priority comprehensive scoring formula to determine the material outbound sequence planning plan. Closed-loop execution optimization: Implement the warehouse replenishment rhythm adjustment plan and material outbound sequence planning plan, and collect multi-dimensional raw data during the execution process to redetermine the warehouse replenishment rhythm adjustment plan and material outbound sequence planning plan, forming a management process of data collection - data processing - inventory demand calculation - operation and maintenance strategy formulation - instruction execution - closed-loop optimization.

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