Low-carbon efficient intelligent stereoscopic warehouse system and operation method thereof

Abstract

The invention relates to the technical field of intelligent storage, in particular to a low-carbon and efficient intelligent three-dimensional warehouse system and an operation method thereof. The method comprises the following steps: obtaining overall data of a three-dimensional warehouse, wherein the overall data of the three-dimensional warehouse comprises three-dimensional warehouse power consumption data, warehouse-in and warehouse-out order data, three-dimensional warehouse storage data, three-dimensional warehouse wharf position data and three-dimensional warehouse access equipment data; empty warehouse positioning is carried out on the storage data of the three-dimensional warehouse to obtain empty warehouse position data of the three-dimensional warehouse; performing channel network analysis on the empty warehouse position data of the three-dimensional warehouse and the wharf position data of the three-dimensional warehouse to obtain efficient operation area data; obtaining historical operation data of the three-dimensional warehouse; through comprehensive analysis and optimization decision, the purposes of efficient operation of the intelligent three-dimensional warehouse, energy consumption reduction and operation efficiency improvement can be achieved, and therefore a new thought and a new solution are provided for intelligent management of the three-dimensional warehouse.

Description

Technical Field

[0001] The present invention relates to the field of intelligent warehousing technology, and in particular to a low-carbon and high-efficiency intelligent stereoscopic warehouse system and an operation method thereof. Background Art

[0002] The stereoscopic warehouse initially relied on manual operation and traditional warehouse management methods. At this stage, warehouse operations mainly relied on manual labor and traditional stacking methods. This method has the defects of low efficiency and high labor costs. Manual operation is prone to errors, and for large-scale warehouses, manual labor requires a lot of time and resources. With the advancement of technology, stereoscopic warehouses gradually introduced mechanized equipment, such as shelf stackers, conveyors, and automatic guided vehicles. These mechanical equipment can automatically complete the storage, retrieval and handling of goods. The mechanization stage has improved the efficiency of warehouse operations and reduced human errors, but there are still some defects. First, these devices usually need to set fixed paths and rules in advance, and have limited ability to change inventory requirements and operating procedures. Secondly, the operation of mechanical equipment requires a certain amount of space, which puts forward certain requirements for the layout and design of the warehouse. The automation stage is an important stage in the development of stereoscopic warehouses. At this stage, stereoscopic warehouses introduce more advanced automation equipment and systems, such as automated storage and retrieval systems (AS / RS), automatic guided vehicle systems, and automatic sorting systems. These technologies enable warehouses to achieve highly automated operations and improve warehouse efficiency and accuracy. However, there are still some defects in the automation stage. The existing automated equipment still cannot handle the efficiency issues of warehousing and warehousing well, and the operating efficiency is low, resulting in more energy consumption. Summary of the invention

[0003] Based on this, it is necessary to provide a low-carbon and efficient intelligent stereoscopic warehouse system and its operation method to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a low-carbon and high-efficiency intelligent stereoscopic warehouse operation method comprises the following steps: Step S1: Acquire the overall data of the three-dimensional warehouse, wherein the overall data of the three-dimensional warehouse includes the electricity consumption data of the three-dimensional warehouse, the order data of inbound and outbound warehouses, the storage data of the three-dimensional warehouse, the location data of the three-dimensional warehouse terminal and the storage and access equipment data of the three-dimensional warehouse; perform empty warehouse positioning on the storage data of the three-dimensional warehouse to obtain the empty warehouse location data of the three-dimensional warehouse; perform channel network analysis on the empty warehouse location data of the three-dimensional warehouse and the location data of the three-dimensional warehouse terminal to obtain the efficient operation area data; Step S2: Acquire the historical operation data of the three-dimensional warehouse; perform frequent event sequence mining on the historical operation data of the three-dimensional warehouse and the in-and-out order data to obtain a frequent event sequence data set; perform in-and-out demand inference on the frequent event sequence data set to obtain in-and-out demand data for future time periods; Step S3: dynamically adjust the power of the three-dimensional warehouse access equipment data based on the future time period inbound and outbound demand data and the three-dimensional warehouse power consumption data to obtain the adjusted access equipment data; locate the goods to be shipped out of the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the position data of the goods to be shipped out; allocate the inbound storage location to the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the target storage location data of the goods; locate the buffer transfer area of ​​the target storage location data of the goods, the position data of the goods to be shipped out and the high-efficiency operation area data to generate the transportation buffer center point location data; Step S4: When the access device data is adjusted to high-load access device data, full-power in and out-of-warehouse operations are deployed for the high-load access device data to improve the efficiency of in and out-of-warehouse operations; when the access device data is adjusted to low-load access device data, low-power storage position transformation operations are deployed for the low-load access device data to achieve the purpose of carbon reduction and peak regulation.

[0005] The present invention integrates multiple data sources, including electricity consumption data, order data, and storage data. The use of such comprehensive data can provide a more comprehensive three-dimensional warehouse operation status and historical operation trends. By locating the empty warehouse of the storage data and performing channel network analysis on the empty warehouse location data and the terminal location data, the efficient operation area can be determined, thereby optimizing the warehouse layout and the cargo storage and access path, and improving the operation efficiency. Frequent event sequence mining can reveal the patterns and trends of the in-and-out behavior, so as to better understand the future in-and-out demand and provide predictive guidance for warehouse operations. Dynamic power adjustment of the access equipment based on the future in-and-out demand data can enable the equipment to make reasonable power adjustments during high-load and low-load periods to improve energy efficiency and reduce carbon emissions. By locating the buffer transfer area of ​​the cargo storage location data, the location data of the cargo to be shipped out, and the efficient operation area data, the cargo movement distance can be reduced, the cargo flow path can be optimized, and the warehouse operation efficiency can be improved. The operation deployment strategy for different access equipment loads, including full-power in-and-out operations and low-power storage position conversion operations, this flexible operation deployment can maximize the efficiency of in-and-out operations and achieve the goals of energy conservation and carbon emission reduction according to actual conditions. It improves warehouse operation efficiency, optimizes resource utilization, reduces energy consumption, and has flexible response strategies to adapt to different operational needs and environmental conditions.

[0006] In this specification, a low-carbon and high-efficiency intelligent stereoscopic warehouse system is provided, which is used to execute the low-carbon and high-efficiency intelligent stereoscopic warehouse operation method as described above, including: The high-efficiency zone positioning module is used to obtain the overall data of the three-dimensional warehouse, where the overall data of the three-dimensional warehouse includes the electricity consumption data of the three-dimensional warehouse, the order data of inbound and outbound warehouses, the storage data of the three-dimensional warehouse, the location data of the three-dimensional warehouse terminal and the storage and access equipment data of the three-dimensional warehouse; obtain the empty warehouse location data of the three-dimensional warehouse; perform channel network analysis on the empty warehouse location data and the three-dimensional warehouse terminal location data of the three-dimensional warehouse to obtain the high-efficiency operation area data; The terminal status acquisition module is used to obtain the historical operation data of the three-dimensional warehouse; conduct frequent event sequence mining on the historical operation data of the three-dimensional warehouse and the inbound and outbound order data to obtain the frequent event sequence data set; perform inbound and outbound demand inference on the frequent event sequence data set to obtain the inbound and outbound demand data for the future time period; The buffer positioning module is used to dynamically adjust the power of the three-dimensional warehouse access equipment data based on the future time period inbound and outbound demand data and the three-dimensional warehouse power consumption data to obtain the adjusted access equipment data; locate the goods to be shipped out of the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the location data of the goods to be shipped out; allocate the inbound storage location to the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the target storage location data of the goods; locate the buffer transfer area for the target storage location data of the goods, the location data of the goods to be shipped out and the efficient operation area data to generate the transportation buffer center point location data; The mode switching module is used to deploy full-power in and out-of-warehouse operations for the high-load access device data when the access device data is adjusted to high-load access device data, so as to improve the efficiency of in and out-of-warehouse operations; when the access device data is adjusted to low-load access device data, low-power storage position transformation operations are deployed for the low-load access device data, so as to achieve the purpose of carbon reduction and peak regulation.

[0007] The benefit of the present invention lies in that comprehensive analysis is performed by acquiring the overall data of the stereoscopic warehouse, including power consumption data, inbound and outbound order data, storage data, terminal location data and access equipment data. In this way, the operation of the stereoscopic warehouse can be fully understood, providing basic data for subsequent optimization and decision-making. By locating the empty warehouse of the stereoscopic warehouse storage data, the empty warehouse location data of the stereoscopic warehouse is obtained. In this way, it is possible to accurately know which positions in the warehouse are idle, which provides convenience for the subsequent storage and retrieval of goods. By performing channel network analysis on the empty warehouse location data and the terminal location data, efficient operation area data is obtained. In this way, the most suitable area for cargo storage and retrieval in the stereoscopic warehouse can be determined, which improves operation efficiency and reduces cargo transportation time. By mining the frequent event sequence of the stereoscopic warehouse historical operation data and inbound and outbound order data, a frequent event sequence data set is obtained. In this way, frequently occurring operation events can be discovered and predicted, providing a basis for subsequent planning and scheduling. By inferring the inbound and outbound demand of the frequent event sequence data set, the inbound and outbound demand data of the future time period is obtained. In this way, the inbound and outbound demand of the warehouse can be predicted in advance, providing a basis for the dynamic power adjustment and operation deployment of the access equipment. Based on the in-and-out demand data and power consumption data of the future time period, the data of the access equipment is dynamically adjusted for power, and the operation is deployed according to the adjusted data. In this way, the power and operation mode of the access equipment can be flexibly adjusted according to the actual demand and energy consumption, and the energy utilization efficiency and operation efficiency can be improved. By locating the buffer transfer area of ​​the target storage location data of the goods, the location data of the goods to be shipped out, and the data of the efficient operation area, the transportation buffer center point location data is generated. In this way, the transportation path and process of the goods can be optimized, and the transportation time and energy consumption can be reduced. According to the adjusted access equipment data, it is divided into high-load and low-load access equipment data, and full-power in-and-out operation deployment and low-power storage position conversion operation deployment are respectively carried out. In this way, flexible scheduling can be carried out according to the actual situation, improving the efficiency of in-and-out storage and saving energy consumption. Combining technologies such as data analysis, predictive inference and dynamic scheduling, through comprehensive analysis and optimization decision-making, the goals of efficient operation of intelligent stereoscopic warehouses, reducing energy consumption and improving operation efficiency can be achieved, thus providing new ideas and solutions for the intelligent management of stereoscopic warehouses. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of the steps of a low-carbon and high-efficiency intelligent stereoscopic warehouse operation method; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 4 for Figure 1Detailed implementation steps of step S4 in FIG.

[0009] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0010] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0011] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0012] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0013] To achieve this, please refer to Figures 1 to 4 , a low-carbon and high-efficiency intelligent stereoscopic warehouse operation method, comprising the following steps: Step S1: Acquire the overall data of the three-dimensional warehouse, wherein the overall data of the three-dimensional warehouse includes the electricity consumption data of the three-dimensional warehouse, the order data of inbound and outbound warehouses, the storage data of the three-dimensional warehouse, the location data of the three-dimensional warehouse terminal and the storage and access equipment data of the three-dimensional warehouse; perform empty warehouse positioning on the storage data of the three-dimensional warehouse to obtain the empty warehouse location data of the three-dimensional warehouse; perform channel network analysis on the empty warehouse location data of the three-dimensional warehouse and the location data of the three-dimensional warehouse terminal to obtain the efficient operation area data; Step S2: Acquire the historical operation data of the three-dimensional warehouse; perform frequent event sequence mining on the historical operation data of the three-dimensional warehouse and the in-and-out order data to obtain a frequent event sequence data set; perform in-and-out demand inference on the frequent event sequence data set to obtain in-and-out demand data for future time periods; Step S3: dynamically adjust the power of the three-dimensional warehouse access equipment data based on the future time period inbound and outbound demand data and the three-dimensional warehouse power consumption data to obtain the adjusted access equipment data; locate the goods to be shipped out of the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the position data of the goods to be shipped out; allocate the inbound storage location to the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the target storage location data of the goods; locate the buffer transfer area of ​​the target storage location data of the goods, the position data of the goods to be shipped out and the high-efficiency operation area data to generate the transportation buffer center point location data; Step S4: When the access device data is adjusted to high-load access device data, full-power in and out-of-warehouse operations are deployed for the high-load access device data to improve the efficiency of in and out-of-warehouse operations; when the access device data is adjusted to low-load access device data, low-power storage position transformation operations are deployed for the low-load access device data to achieve the purpose of carbon reduction and peak regulation.

[0014] The benefit of the present invention lies in that comprehensive analysis is performed by acquiring the overall data of the stereoscopic warehouse, including power consumption data, inbound and outbound order data, storage data, terminal location data and access equipment data. In this way, the operation of the stereoscopic warehouse can be fully understood, providing basic data for subsequent optimization and decision-making. By locating the empty warehouse of the stereoscopic warehouse storage data, the empty warehouse location data of the stereoscopic warehouse is obtained. In this way, it is possible to accurately know which positions in the warehouse are idle, which provides convenience for the subsequent storage and retrieval of goods. By performing channel network analysis on the empty warehouse location data and the terminal location data, efficient operation area data is obtained. In this way, the most suitable area for cargo storage and retrieval in the stereoscopic warehouse can be determined, which improves operation efficiency and reduces cargo transportation time. By mining the frequent event sequence of the stereoscopic warehouse historical operation data and inbound and outbound order data, a frequent event sequence data set is obtained. In this way, frequently occurring operation events can be discovered and predicted, providing a basis for subsequent planning and scheduling. By inferring the inbound and outbound demand of the frequent event sequence data set, the inbound and outbound demand data of the future time period is obtained. In this way, the inbound and outbound demand of the warehouse can be predicted in advance, providing a basis for the dynamic power adjustment and operation deployment of the access equipment. Based on the in-and-out demand data and power consumption data of the future time period, the data of the access equipment is dynamically adjusted for power, and the operation is deployed according to the adjusted data. In this way, the power and operation mode of the access equipment can be flexibly adjusted according to the actual demand and energy consumption, and the energy utilization efficiency and operation efficiency can be improved. By locating the buffer transfer area of ​​the target storage location data of the goods, the location data of the goods to be shipped out, and the data of the efficient operation area, the transportation buffer center point location data is generated. In this way, the transportation path and process of the goods can be optimized, and the transportation time and energy consumption can be reduced. According to the adjusted access equipment data, it is divided into high-load and low-load access equipment data, and full-power in-and-out operation deployment and low-power storage position conversion operation deployment are respectively carried out. In this way, flexible scheduling can be carried out according to the actual situation, improving the efficiency of in-and-out storage and saving energy consumption. Combining technologies such as data analysis, predictive inference and dynamic scheduling, through comprehensive analysis and optimization decision-making, the goals of efficient operation of intelligent stereoscopic warehouses, reducing energy consumption and improving operation efficiency can be achieved, thus providing new ideas and solutions for the intelligent management of stereoscopic warehouses.

[0015] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of a low-carbon and efficient intelligent stereoscopic warehouse operation method of the present invention. In this example, the low-carbon and efficient intelligent stereoscopic warehouse operation method includes the following steps: Step S1: Acquire the overall data of the three-dimensional warehouse, wherein the overall data of the three-dimensional warehouse includes the electricity consumption data of the three-dimensional warehouse, the order data of inbound and outbound warehouses, the storage data of the three-dimensional warehouse, the location data of the three-dimensional warehouse terminal and the storage and access equipment data of the three-dimensional warehouse; perform empty warehouse positioning on the storage data of the three-dimensional warehouse to obtain the empty warehouse location data of the three-dimensional warehouse; perform channel network analysis on the empty warehouse location data of the three-dimensional warehouse and the location data of the three-dimensional warehouse terminal to obtain the efficient operation area data; In the embodiment of the present invention, the power consumption data of the stereoscopic warehouse is collected: the power consumption of the stereoscopic warehouse is recorded, including information such as power consumption and power consumption period. The order data for entering and leaving the warehouse is collected: the order information for entering and leaving the warehouse is obtained, including the type, quantity, time of entering and leaving the warehouse, etc. The storage data of the stereoscopic warehouse is collected: the location and status information of the storage of goods in the stereoscopic warehouse is obtained, including the location of the stored goods, the location of the empty warehouse, etc. The location data of the stereoscopic warehouse terminal is collected: the location information of the terminal in the stereoscopic warehouse is recorded, including the terminal number, location coordinates, etc. The storage and access equipment data of the stereoscopic warehouse is collected: the equipment information used for storing and accessing goods in the stereoscopic warehouse is obtained, including mechanical arms, conveyor belts, etc. The storage data of the stereoscopic warehouse is analyzed to identify all empty warehouse locations. The empty warehouse location data is recorded as input for subsequent channel network analysis. The empty warehouse location data and the stereoscopic warehouse terminal location data are combined. The channel network between the cargo storage area and the terminal is determined using network analysis technology. According to the structure and characteristics of the channel network, the efficient operation area is identified, that is, the area with short and unobstructed cargo storage and access paths.

[0016] Step S2: Acquire the historical operation data of the three-dimensional warehouse; perform frequent event sequence mining on the historical operation data of the three-dimensional warehouse and the in-and-out order data to obtain a frequent event sequence data set; perform in-and-out demand inference on the frequent event sequence data set to obtain in-and-out demand data for future time periods; In an embodiment of the present invention, the operational data of the stereoscopic warehouse over a period of time in the past is collected, including cargo storage, in-and-out storage records, and equipment operating status. Ensure the integrity and accuracy of historical data for subsequent analysis and use. Integrate historical operational data and in-and-out storage order data together, and use frequent event sequence mining technology, such as sequence pattern mining or sequence rule mining, to find event sequences that frequently appear in historical data. According to the mining results, a frequent event sequence data set is obtained, which contains frequently occurring event sequences and their support information. Based on the frequent event sequence data set, in-and-out storage demand inference is performed, and the historical event sequence data is analyzed to identify patterns or rules related to in-and-out storage. According to these patterns or rules, inference is made on the in-and-out storage demand in the future time period, including cargo type, quantity, and time. Use data mining algorithms, such as the Apriori algorithm or the FP-growth algorithm, to mine frequent event sequences. For in-and-out storage demand inference, methods such as time series analysis, machine learning models (such as neural networks, random forests, etc.) or rule engines can be used.

[0017] Step S3: dynamically adjust the power of the three-dimensional warehouse access equipment data based on the future time period inbound and outbound demand data and the three-dimensional warehouse power consumption data to obtain the adjusted access equipment data; locate the goods to be shipped out of the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the position data of the goods to be shipped out; allocate the inbound storage location to the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the target storage location data of the goods; locate the buffer transfer area of ​​the target storage location data of the goods, the position data of the goods to be shipped out and the high-efficiency operation area data to generate the transportation buffer center point location data; In the embodiment of the present invention, based on the in-and-out demand data of the future time period and the power consumption data of the stereoscopic warehouse, the load condition and power consumption of the access device are analyzed, and according to the analysis results, the power of the access device is dynamically adjusted to optimize energy utilization and equipment operation efficiency, and intelligent control algorithms such as PID control, fuzzy control or reinforcement learning are used to achieve dynamic power adjustment. According to the in-and-out order data, the goods to be shipped out and their storage locations are determined, and the stereoscopic warehouse storage data is analyzed to locate the exact location of the goods to be shipped out, ensuring that the goods are taken out in a timely and accurate manner. Combined with the target storage location data of the goods, the location data of the goods to be shipped out, and the efficient operation area data, the buffer transfer area is located, and the optimal transfer area location is determined to achieve efficient transportation and operation of the goods. According to the positioning results of the buffer transfer area, the transportation buffer center point location data is generated, and the center point location data is used to guide the transportation path planning and operation process optimization of the goods.

[0018] Step S4: When the access device data is adjusted to high-load access device data, full-power in and out-of-warehouse operations are deployed for the high-load access device data to improve the efficiency of in and out-of-warehouse operations; when the access device data is adjusted to low-load access device data, low-power storage position transformation operations are deployed for the low-load access device data to achieve the purpose of carbon reduction and peak regulation.

[0019] In an embodiment of the present invention, when the access device data is adjusted to high-load access device data, a full-power in-and-out warehouse operation deployment strategy is adopted for high-load conditions to maximize the efficiency of in-and-out warehouses, ensure stable equipment operation under high-load conditions, and make relevant preparations in advance to cope with the demand for in-and-out warehouses during peak hours. When the access device data is adjusted to low-load access device data, a low-power storage position transformation operation deployment strategy is adopted according to the low-load conditions to achieve the purpose of carbon reduction and peak regulation, and reduce energy consumption and carbon emissions by reducing power or adjusting the equipment operation mode, while ensuring basic access functions. Formulate specific control strategies under high-load and low-load conditions, including equipment scheduling, energy management and other aspects, ensure the effectiveness and stability of the control strategies, adjust and optimize according to real-time data, and continuously monitor the operating status and energy consumption of the access devices after implementation and deployment.

[0020] Preferably, step S1 comprises the following steps: Step S11: Acquire the overall data of the three-dimensional warehouse, wherein the overall data of the three-dimensional warehouse includes the electricity consumption data of the three-dimensional warehouse, the inbound and outbound order data, the storage data of the three-dimensional warehouse, the location data of the three-dimensional warehouse terminal and the storage and access equipment data of the three-dimensional warehouse; Step S12: Acquire the empty warehouse position data of the three-dimensional warehouse; Step S13: Perform channel network analysis on the empty warehouse position data and the terminal position data of the three-dimensional warehouse to generate the shortest channel data; Step S14: Based on the shortest channel data, the empty warehouse position data of the stereoscopic warehouse is matched to obtain the efficient operation area data.

[0021] The present invention achieves comprehensive monitoring and data collection of warehouse operations by acquiring the overall data of the stereoscopic warehouse, including electricity consumption data, inbound and outbound order data, storage data and other information, and provides data support for subsequent optimization. The storage data is used to locate the empty warehouse and find the free storage location in the warehouse; the shortest channel data is used to match the empty warehouse position to achieve efficient access operations. This method can minimize the travel distance and time of the access equipment and improve the operation efficiency. The channel network analysis is performed on the empty warehouse position data and the dock position data of the stereoscopic warehouse to generate the shortest channel data. This analysis can help determine the access path, thereby reducing the walking distance and time and optimizing the operation process. The empty warehouse is matched to obtain the efficient operation area data, and the access operation is concentrated in the area closest to the dock and with the shortest channel, further improving the operation efficiency. Combining multiple technical means such as data analysis, space optimization and operation process optimization, it provides an innovative solution for the operation and management of the stereoscopic warehouse, which can improve efficiency and reduce costs, and has certain advancement and practicality in the warehousing industry.

[0022] In the embodiment of the present invention, the power consumption of each area or device of the stereoscopic warehouse is recorded to understand the activity status and energy consumption of the warehouse, and all order information in and out of the warehouse, including the type, quantity, time, etc. of goods, is recorded to understand the logistics demand and flow of the warehouse. A storage pattern diagram of the stereoscopic warehouse is established, and the location, capacity, type of goods and other information of each storage area are recorded. The terminal location of the stereoscopic warehouse, including the entrance and exit, is marked to facilitate the subsequent analysis of the operation path and channel, and the storage and access equipment information inside the warehouse, such as forklifts, conveyor belts, etc., is recorded to understand the equipment distribution and operation efficiency. The storage pattern of the warehouse is analyzed to identify the currently idle storage location, and the identified empty warehouse location is marked and recorded to form the empty warehouse location data of the stereoscopic warehouse. Based on the empty warehouse location data and the terminal location data of the stereoscopic warehouse, the channel network of the stereoscopic warehouse is constructed to determine the channels and connection relationships between the various areas, and the channel network analysis algorithm is used to calculate the shortest path between each empty warehouse location and the terminal location, as well as the distance and time of the path. According to the shortest channel data, the empty warehouse position is matched with the nearest dock position to ensure efficient use of the operating area and improve operating efficiency. The matched empty warehouse position and the corresponding operating channel information are integrated to form efficient operating area data for subsequent operation planning and scheduling.

[0023] Preferably, step S2 comprises the following steps: Step S21: Acquire historical operation data of the three-dimensional warehouse; Step S22: performing event sequence coding on the historical operation data of the three-dimensional warehouse and the in-and-out order data to obtain an event sequence data set; Step S23: performing frequent event sequence mining on the event sequence data set to obtain a frequent event sequence data set; Step S24: performing future pattern matching on the frequent event sequence data set to generate a matching event sequence data set; Step S25: Infer the inbound and outbound demand of the matching event sequence data set to obtain the inbound and outbound demand data for the future time period.

[0024] The present invention utilizes historical operation data and inbound and outbound order data to perform event sequence coding, frequent event sequence mining, and future pattern matching, which can comprehensively consider the past operation of the warehouse and the current logistics demand, thereby more accurately inferring the future inbound and outbound demand. By performing future pattern matching on the frequent event sequence data set, a matching event sequence data set can be generated, thereby more precisely predicting the inbound and outbound demand in the future time period. This refined prediction helps warehouse managers to carry out more effective logistics planning and operation arrangements, and improve the utilization and efficiency of the warehouse. The entire process utilizes a large amount of historical operation data, and analyzes and predicts through technologies such as data mining and pattern matching, providing data-driven decision support for warehouse management. This data-based prediction and planning method can help managers grasp future logistics needs more accurately, prepare in advance, and reduce warehouse operation risks. Through the analysis and pattern matching of historical data, the prediction model can have certain real-time and dynamic characteristics, and can be continuously updated and adjusted over time to adapt to the ever-changing logistics environment and needs. Combining technologies such as data analysis, pattern recognition, and predictive modeling, a new method and idea is provided for predicting the inbound and outbound demand of the stereoscopic warehouse, which helps to improve the efficiency and accuracy of warehouse management.

[0025] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Acquire historical operation data of the three-dimensional warehouse; In an embodiment of the present invention, historical operation data of the stereoscopic warehouse is collected, including but not limited to storage records, outbound records, inventory records, inventory change records, and operation logs, and the collected data is cleaned, including removing duplicate data, processing missing values, processing abnormal values, etc., to ensure the accuracy and completeness of the data. The cleaned data is sorted according to the required format for subsequent analysis and processing. This involves data format conversion, field extraction, and data standardization operations. The sorted data is stored in a suitable data storage medium, such as a database or a data warehouse. During data collection, processing, and storage, it is necessary to ensure the security of the data and take appropriate security measures, such as data encryption, access control, etc., to prevent data leakage and damage.

[0026] Step S22: performing event sequence coding on the historical operation data of the three-dimensional warehouse and the in-and-out order data to obtain an event sequence data set; In an embodiment of the present invention, historical operation data and inbound and outbound order data are preprocessed, including operations such as cleaning, deduplication, and filling missing values ​​to ensure the quality and integrity of the data. The historical operation data and inbound and outbound order data are integrated for subsequent event sequence encoding processing. This involves operations such as field alignment and data association. Define the event types corresponding to operations such as outbound and inbound operations, and determine the events that need to be encoded. Event sequence encoding is performed on the integrated data, and each event sequence is converted into a corresponding numerical sequence or classification sequence. This can be achieved by assigning a unique code to each event type or using one-hot encoding. In order to maintain consistency in sequence length, the event sequences are padded or truncated so that they have the same length. The encoded event sequence data is combined into a data set, and each sample represents an event sequence, including its corresponding target (such as the next event type).

[0027] Step S23: performing frequent event sequence mining on the event sequence data set to obtain a frequent event sequence data set; In an embodiment of the present invention, an encoded event sequence data set is prepared to ensure that the data format is correct and each sample is an event sequence. The support threshold for frequent event sequence mining is determined. The support defines the frequency of a sequence in the data set, usually as a percentage or absolute count. Use an appropriate frequent sequence mining algorithm (such as PrefixSpan, GSP, etc.) to mine the event sequence data set to find frequent event sequences that meet the support threshold. According to the support threshold, frequent event sequences are screened out and those sequences with support below the threshold are removed. The mined frequent event sequences are visualized to more intuitively understand the sequence pattern. The mined frequent event sequence data set is saved for subsequent analysis and application.

[0028] Step S24: performing future pattern matching on the frequent event sequence data set to generate a matching event sequence data set; In an embodiment of the present invention, a frequent event sequence data set is prepared to ensure that each event sequence is a frequent event sequence and contains appropriate time information for future pattern matching. Determine the time window size for future pattern matching, that is, the length of the future time period to be predicted. Select an appropriate pattern matching algorithm, such as a rule-based matching algorithm, a sequence pattern matching algorithm (such as sequence prediction, sequence similarity matching). Perform pattern matching on the frequent event sequences within the future time window to predict the pattern of the future event sequence. Based on the pattern matching results, generate a matching event sequence data set, including information such as the predicted future event sequence and its probability. Understand the results of the pattern matching and analyze the predicted future event sequence pattern and its impact.

[0029] Step S25: Infer the inbound and outbound demand of the matching event sequence data set to obtain the inbound and outbound demand data for the future time period.

[0030] In an embodiment of the present invention, a matching event sequence data set is prepared to ensure that it contains the predicted future event sequence and related information, such as timestamps and event types. Features are extracted from the matching event sequence data set, including but not limited to time features, event type features, frequent pattern features, etc., so as to be used to establish an in-and-out demand inference model. An appropriate modeling method is selected, which can be a rule-based method, a statistical model (such as time series analysis), a machine learning model (such as regression, classification, time series prediction model) or a deep learning model (such as a recurrent neural network, a long short-term memory network). The extracted features and the corresponding in-and-out demand data are used for model training. During the training process, cross-validation and other techniques can be used to evaluate the performance of the model and perform tuning. The trained model is verified using a validation set to evaluate the generalization ability and prediction effect of the model on unseen data. The trained model is used to infer the feature data of the future time period to predict the in-and-out demand. The inference results are evaluated, the difference between the prediction results and the actual situation is compared, and the accuracy and reliability of the model are evaluated. The inference results are understood and the trends and laws of in-and-out demand are analyzed.

[0031] Preferably, step S3 comprises the following steps: Step S31: Perform peak and valley analysis on the electricity consumption data of the three-dimensional warehouse to obtain predicted electricity price time period data; Step S32: dynamically adjusting the power of the storage and access equipment data of the three-dimensional warehouse based on the storage and access demand data of the future time period and the predicted electricity price time period data to obtain the adjusted storage and access equipment data; Step S33: positioning the goods to be shipped out of the three-dimensional warehouse storage data based on the warehouse entry and exit order data to obtain the location data of the goods to be shipped out; allocating the inbound storage location of the three-dimensional warehouse storage data based on the warehouse entry and exit order data to obtain the target storage location data of the goods; Step S34: locating the buffer transfer area for the cargo target storage location data, the cargo location data to be shipped out, and the high-efficiency operation area data, and generating the transport buffer center location data.

[0032] The present invention comprehensively utilizes multi-source data such as electricity consumption data, in-and-out demand data, electricity price period data, storage data and cargo location data of the stereoscopic warehouse, and fully taps the potential value of the data. Through dynamic power adjustment and positioning of goods to be shipped out, intelligent scheduling and management of storage and access equipment and goods are realized, and the operation process inside the warehouse is optimized. Through peak and valley analysis of electricity consumption and dynamic power adjustment, the use period and power of equipment can be reasonably arranged to reduce the electricity cost during peak hours, thereby achieving the purpose of energy conservation and emission reduction. Cargo positioning and buffer transfer area positioning help to optimize the storage and access path of goods in the warehouse, reduce travel time, and improve work efficiency. The generation of transport buffer center point location data can realize intelligent planning and layout of efficient operation areas, further improving the operational efficiency and response speed of the warehouse. Combine data analysis and intelligent scheduling to achieve all-round optimization and improvement of stereoscopic warehouse operations, thereby improving the management level and competitiveness of the warehouse.

[0033] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes: Step S31: Perform peak and valley analysis on the electricity consumption data of the three-dimensional warehouse to obtain predicted electricity price time period data; In an embodiment of the present invention, electricity consumption data of a stereoscopic warehouse is collected, including information such as electricity consumption and timestamp. The collected electricity consumption data is preprocessed, including data cleaning, removing outliers, filling missing values, etc., to ensure the quality and integrity of the data. Peak and valley analysis is performed on the electricity consumption data to identify the peak and valley periods of electricity consumption. Common methods include sliding window method and cluster analysis method. The identified peak and valley periods are associated with the predicted electricity price data to determine the electricity price level in different periods. These electricity price data can be the electricity price table or historical electricity price data of the power supplier obtained in advance. According to the results of the peak and valley analysis and the associated information of the electricity price, the predicted electricity price period data is generated. It usually includes high electricity price period, low electricity price period and ordinary electricity price period. The generated predicted electricity price period data is verified, and the predicted electricity price period is compared to see whether it is consistent with the actual situation, and the analysis algorithm is adjusted and optimized to improve the accuracy. The predicted electricity price period data is output and saved for subsequent electricity consumption planning and scheduling.

[0034] Step S32: dynamically adjusting the power of the storage and access equipment data of the three-dimensional warehouse based on the storage and access demand data of the future time period and the predicted electricity price time period data to obtain the adjusted storage and access equipment data; In the embodiment of the present invention, the in-and-out demand data and the predicted electricity price period data of the future time period are collected, and necessary preprocessing is performed, such as data cleaning and missing value filling. Features are extracted from the original data. For example, the in-and-out demand data can extract the features of the out-and-out quantity and the in-and-out quantity in each time period; the electricity price period data can extract the features of the electricity price in each time period. These features are encoded and converted into a data format suitable for model input. The feature-encoded data are combined to form data point combination data. A suitable asynchronous frequency modulation task scheduling algorithm is selected, and a suitable scheduling strategy is determined according to the data point combination data and the access device. According to the selected algorithm, the tasks of the stereoscopic warehouse access device are scheduled, the tasks performed by each device in different time periods are determined, and the asynchronous frequency modulation task queue data is generated. A variable clock speed control scheme is designed, and the change law of the clock speed is determined according to the characteristics of the asynchronous frequency modulation task queue data and the access device. According to the designed scheme, the clock speed of the asynchronous frequency modulation task queue data is controlled to generate variable clock speed data, that is, the working speed or beat of the device in each time period is determined. Design a dynamic frequency modulation strategy to adjust the device's operating frequency in real time according to the variable clock speed data and the status of the access device to meet the requirements of task scheduling. According to the designed strategy, dynamically modulate the access device to adjust the device's operating frequency to adapt to the requirements of the asynchronous frequency modulation task queue.

[0035] Step S33: positioning the goods to be shipped out of the three-dimensional warehouse storage data based on the warehouse entry and exit order data to obtain the location data of the goods to be shipped out; allocating the inbound storage location of the three-dimensional warehouse storage data based on the warehouse entry and exit order data to obtain the target storage location data of the goods; In an embodiment of the present invention, the in-and-out order data and the stereoscopic warehouse storage data are collected. The in-and-out order data include the order information of the goods to be shipped, and the stereoscopic warehouse storage data include the location and status information of the currently stored goods. The order information of the goods to be shipped is matched with the stereoscopic warehouse storage data to determine the storage location and status of the goods to be shipped. According to the matching results, the specific location information of the goods to be shipped is determined, including the shelf, level and storage location, etc. The incoming order information is matched with the stereoscopic warehouse storage data to determine the information of the goods to be shipped. According to the matching results and the storage rules of the stereoscopic warehouse, a target storage location is allocated to each incoming goods to ensure efficient use of warehouse space and meet the storage and access requirements of goods. The stereoscopic warehouse storage data is updated, including the location status of the goods to be shipped and the target storage location data, to ensure the accuracy and real-time nature of the data. According to the location data of the goods to be shipped and the target storage location data, the out-and-in operation is performed to ensure that the goods are shipped out and shipped on time. The in-and-out situation and storage location of the goods are continuously monitored, and the operation plan and storage strategy are adjusted in time to meet actual needs and improve operation efficiency.

[0036] Step S34: locating the buffer transfer area for the cargo target storage location data, the cargo location data to be shipped out, and the high-efficiency operation area data, and generating the transport buffer center location data.

[0037] In an embodiment of the present invention, the target storage location data and the efficient operation area data of the goods are prepared, and the target storage location data and the efficient operation area data of the goods are simulated by multi-channel transportation using simulation software or algorithm. This can be based on the simulation algorithm to simulate the storage path of the goods, considering different channels, paths and operation areas to optimize the storage process. According to the simulation results, a data set containing multi-channel storage path information is generated. These data sets may include information such as the path, length, and time of the storage channel for subsequent path matching and optimization. The location data and efficient operation area data of the goods to be shipped are prepared, and the location data and the efficient operation area data of the goods to be shipped are simulated by multi-channel transportation using simulation software or algorithm. This can be based on the simulation algorithm to simulate the storage path of the goods, considering different channels, paths and operation areas to optimize the storage process, and according to the simulation results, a data set containing multi-channel storage path information is generated. These data sets may include information such as the path, length, and time of the storage channel for subsequent path matching and optimization. Path matching is performed on the storage channel data set and the storage channel data set to find the shared path between them. This can be an algorithm- or model-based path matching process, the purpose of which is to find a shared path between the inbound and outbound channels to achieve optimal utilization and sharing of the path. Based on the matching results, a data set containing information about the extreme shared channels is generated. These data sets may include information such as the path, length, and time of the shared channel for subsequent path optimization and management. The extreme shared channel data is analyzed to determine the location of the separation point. The separation point refers to the location on the extreme shared channel where the inbound and outbound paths are separated. It is usually a location set up to achieve smooth entry and exit of goods. Based on the separation point location, a data set containing the location information of the transport buffer center point is generated. These data sets may include information such as the coordinates of the buffer center point and path relationships for subsequent path management and optimization.

[0038] Preferably, step S32 includes the following steps: Step S321: performing data feature encoding on the future time period in-and-out demand data and the predicted electricity price period data to obtain data point combination data; Step S322: performing asynchronous frequency modulation task scheduling on the storage and access device data of the stereoscopic warehouse based on the data point combination data to obtain asynchronous frequency modulation task queue data; Step S323: performing variable clock speed control on the asynchronous frequency modulation task queue data to generate variable clock speed data; Step S324: dynamically modulate the storage and access device data of the stereoscopic warehouse based on the variable clock speed data to obtain adjusted storage and access device data.

[0039] The present invention realizes comprehensive consideration of the in-and-out demand and electricity price period by encoding the data feature of the in-and-out demand data and the predicted electricity price period data in the future time period. In this way, the demand situation of warehouse operation can be more comprehensively understood, which is conducive to subsequent scheduling decisions. The asynchronous frequency modulation task scheduling of the stereoscopic warehouse access equipment is performed using the data point combination data, which means that the tasks can be executed independently, and the frequency is dynamically adjusted according to the actual situation to maximize the utilization and efficiency of the equipment. Variable clock speed control introduces flexibility and adaptability to the entire system. By generating variable clock speed data according to the asynchronous frequency modulation task queue data, the operating speed of the warehouse equipment can be dynamically adjusted according to the current task situation, so as to better meet the actual needs and improve the responsiveness and flexibility of the system. The stereoscopic warehouse access equipment is dynamically frequency modulated based on the variable clock speed data, which means that the system can dynamically adjust the equipment according to the actual situation to achieve more efficient warehouse operation. This dynamic frequency modulation optimization can help reduce energy consumption and improve equipment utilization, thereby reducing costs and improving operational efficiency. Scheduling decisions are made by comprehensively considering multiple factors, introducing innovative methods such as asynchronous frequency modulation, variable clock speed control and dynamic frequency modulation optimization, thereby improving the efficiency and flexibility of warehouse operations.

[0040] In the embodiment of the present invention, the in-and-out demand data and the predicted electricity price period data of the future time period are collected, and necessary preprocessing is performed, such as data cleaning and missing value filling. Features are extracted from the original data. For example, the in-and-out demand data can extract the features of the out-and-out quantity and the in-and-out quantity in each time period; the electricity price period data can extract the features of the electricity price in each time period. These features are encoded and converted into a data format suitable for model input. The feature-encoded data are combined to form data point combination data. A suitable asynchronous frequency modulation task scheduling algorithm is selected, and a suitable scheduling strategy is determined according to the data point combination data and the access device. According to the selected algorithm, the tasks of the stereoscopic warehouse access device are scheduled, the tasks performed by each device in different time periods are determined, and the asynchronous frequency modulation task queue data is generated. A variable clock speed control scheme is designed, and the change law of the clock speed is determined according to the characteristics of the asynchronous frequency modulation task queue data and the access device. According to the designed scheme, the clock speed of the asynchronous frequency modulation task queue data is controlled to generate variable clock speed data, that is, the working speed or beat of the device in each time period is determined. Design a dynamic frequency modulation strategy to adjust the device's operating frequency in real time according to the variable clock speed data and the status of the access device to meet the requirements of task scheduling. According to the designed strategy, dynamically modulate the access device to adjust the device's operating frequency to adapt to the requirements of the asynchronous frequency modulation task queue.

[0041] Preferably, step S34 includes the following steps: Step S341: Perform multi-channel transportation simulation on the cargo target storage location data and the efficient operation area data to generate a warehousing channel data set; Step S342: Perform multi-channel transportation simulation on the location data of the goods to be shipped and the data of the efficient operation area to generate a shipping channel data set; Step S343: performing extreme sharing path matching on the inbound channel data set and the outbound channel data set to obtain extreme sharing channel data; Step S344: locate the separation point of the extreme shared channel data to obtain the transport buffer center point position data.

[0042] The present invention uses a multi-channel transport simulation method to better simulate the warehousing and outbound process of goods. By considering multi-channel transport, the path selection of the flow of goods inside the warehouse can be optimized, and the efficiency of goods handling and operation can be improved. The data set of the inbound channel and the data set of the outbound channel are matched with the extreme shared path, which means that the system will consider the path that meets the requirements of both inbound and outbound. This matching method can optimize the utilization of the channel to the greatest extent, reduce path conflicts, and improve the overall operation efficiency of the warehouse. The separation point positioning can determine the location data of the center point of the transport buffer. The determination of these center points can help optimize the storage and transportation paths of goods, reduce the moving distance and time of goods inside the warehouse, improve operation efficiency and reduce energy consumption. The whole process combines the data simulation, path matching and center point positioning of the inbound and outbound channels to achieve a comprehensive optimization of the flow of goods inside the warehouse. This optimization method that comprehensively considers multiple links helps to improve the efficiency of warehouse operations, reduce costs and improve customer service levels. Innovative methods such as multi-channel transport simulation, extreme shared path matching and transportation buffer center point positioning are adopted to comprehensively optimize the flow of goods inside the warehouse, improve operation efficiency and customer satisfaction.

[0043] In an embodiment of the present invention, the target storage location data and the efficient operation area data of the goods are prepared, and the target storage location data and the efficient operation area data of the goods are simulated by multi-channel transportation using simulation software or algorithm. This can be based on the simulation algorithm to simulate the storage path of the goods, considering different channels, paths and operation areas to optimize the storage process. According to the simulation results, a data set containing multi-channel storage path information is generated. These data sets may include information such as the path, length, and time of the storage channel for subsequent path matching and optimization. The location data and efficient operation area data of the goods to be shipped are prepared, and the location data and the efficient operation area data of the goods to be shipped are simulated by multi-channel transportation using simulation software or algorithm. This can be based on the simulation algorithm to simulate the storage path of the goods, considering different channels, paths and operation areas to optimize the storage process, and according to the simulation results, a data set containing multi-channel storage path information is generated. These data sets may include information such as the path, length, and time of the storage channel for subsequent path matching and optimization. Path matching is performed on the storage channel data set and the storage channel data set to find the shared path between them. This can be an algorithm- or model-based path matching process, the purpose of which is to find a shared path between the inbound and outbound channels to achieve optimal utilization and sharing of the path. Based on the matching results, a data set containing information about the extreme shared channels is generated. These data sets may include information such as the path, length, and time of the shared channel for subsequent path optimization and management. The extreme shared channel data is analyzed to determine the location of the separation point. The separation point refers to the location on the extreme shared channel where the inbound and outbound paths are separated. It is usually a location set up to achieve smooth entry and exit of goods. Based on the separation point location, a data set containing the location information of the transport buffer center point is generated. These data sets may include information such as the coordinates of the buffer center point and path relationships for subsequent path management and optimization.

[0044] Preferably, step S343 includes the following steps: Step S3431: Perform frequent item set mining on the inbound channel data set and the outbound channel data set to obtain frequent path pattern data; Step S3432: performing maximum similarity edit distance matching on the inbound channel data set and the outbound channel data set based on the frequent path pattern data to obtain matching path pattern data; Step S3433: performing longitudinal node calculation on the matching path pattern data to obtain longitudinal node data of each path; Step S3434: Perform maximum longitudinal distance matching on the matching path pattern data based on the longitudinal node data of each path to generate extreme shared channel data.

[0045] The present invention mines frequent item sets on the inbound channel data set and the outbound channel data set, and the system can find frequently occurring path patterns. This mining method helps to understand the common patterns and trends of channel data, and provides a basis for subsequent path matching. Using the maximum similarity edit distance matching method, the system can find the most similar matching path pattern in the frequent path pattern data. Compared with strict path matching, this method is more flexible, can handle noise and changes in the data, and improve the accuracy and robustness of matching. The longitudinal node data of each path can be obtained by performing longitudinal node calculation on the matching path pattern data. These data can reflect the layout and relationship of the path in the vertical direction, which helps to better understand the path structure and optimize the design of the channel. Based on the longitudinal node data of each path, the maximum longitudinal distance matching is performed, and the system can generate extreme shared channel data. This matching method takes into account the distance relationship of the channel in the vertical direction, can more comprehensively optimize the sharing and utilization of the channel, and improve the operation efficiency of the warehouse. Innovative methods such as frequent path pattern mining, maximum similarity edit distance matching, longitudinal node calculation and maximum longitudinal distance matching are adopted to optimize the generation process of extreme shared channel data in a more flexible, accurate and comprehensive manner, thereby improving the operation efficiency and resource utilization of the warehouse.

[0046] In an embodiment of the present invention, the inbound channel data set and the outbound channel data set are cleaned and prepared to ensure the quality and consistency of the data. A frequent item set mining algorithm (such as an Apriori algorithm or an FP-Growth algorithm) is used to mine the inbound channel data set and the outbound channel data set to find out the frequently occurring path patterns, and the frequent path pattern data obtained by mining is saved for use in subsequent steps. The frequent path pattern data is matched with the paths in the inbound channel data set and the outbound channel data set, and the edit distance between them is calculated. For each path, the path pattern with the largest edit distance is found in the frequent path pattern data as the matching path pattern data. For each path in the matching path pattern data, its longitudinal node data is extracted, that is, the position information of each node in the path in the vertical direction. According to the longitudinal node data of each path, the maximum longitudinal distance matching is performed to find a group of paths with the maximum longitudinal distance. The channel shared between this group of paths is the extreme shared channel data.

[0047] Preferably, step S4 comprises the following steps: Step S41: when the access device data is adjusted to high-load access device data, full-power storage and retrieval operation deployment is performed on the high-load access device data to improve storage and retrieval efficiency; Step S42: when the access device data is adjusted to low-load access device data, a Y-shaped transportation strategy is formulated for the cargo target storage location data, the cargo location data to be shipped out, and the high-efficiency operation area data based on the transportation buffer center location data, and a storage location transformation Y-shaped transportation strategy is generated; Step S43: Based on the storage location transformation Y-shaped transportation strategy, low-power storage location transformation operations are deployed for low-load access equipment data to achieve the purpose of carbon reduction and peak regulation.

[0048] The present invention has the ability to dynamically adjust the entry and exit operation strategy according to the load of the access equipment. When the access equipment load is high, full-power entry and exit operations are deployed to improve efficiency; when the load is low, low-power storage location transformation is achieved through Y-shaped transportation strategy and storage location transformation strategy to balance energy consumption and entry and exit efficiency. By formulating a Y-shaped transportation strategy for each data based on the location data of the transportation buffer center point, the location advantage of the transportation buffer center point is fully utilized to achieve efficient management and resource utilization of access equipment data, thereby improving overall operation efficiency. The storage location transformation strategy is applied to low-load access equipment data to achieve the purpose of carbon reduction and peak regulation. By executing low-power storage location transformation operation deployment during low-load periods, energy consumption can be effectively reduced, and more storage location transformation capabilities can be released during high-load periods, thereby achieving intelligent management and optimization of energy. Dynamicity, comprehensive resource utilization, and consideration of environmental protection and energy conservation have brought innovative strategies and methods to the field of warehousing and logistics management.

[0049] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes: Step S41: when the access device data is adjusted to high-load access device data, full-power storage and retrieval operation deployment is performed on the high-load access device data to improve storage and retrieval efficiency; In an embodiment of the present invention, the status of the high-load access device is evaluated, including aspects such as device performance, workload, and availability. This can be accomplished through a device monitoring system or by checking the device status on site. When the access device data is adjusted to high-load access device data, the in-and-out warehouse operations are optimized and scheduled in full-power mode. This includes optimizing path planning, operation sequence, operation time, etc. to ensure that the high-load access device can efficiently handle a large number of in-and-out warehouse tasks. During the deployment of full-power in-and-out warehouse operations, it may be necessary to increase the support of related resources, such as increasing manpower, optimizing material flow, and providing technical support, to ensure that the in-and-out warehouse operations in full-power mode can proceed smoothly. After the full-power in-and-out warehouse operations are deployed, it is necessary to continuously monitor the equipment operating status and operating efficiency, and adjust and optimize according to actual conditions to achieve the best in-and-out warehouse efficiency.

[0050] Step S42: when the access device data is adjusted to low-load access device data, a Y-shaped transportation strategy is formulated for the cargo target storage location data, the cargo location data to be shipped out, and the high-efficiency operation area data based on the transportation buffer center location data, and a storage location transformation Y-shaped transportation strategy is generated; In an embodiment of the present invention, when the access device data is adjusted to low-load access device data, the transport buffer center point location data, the cargo target storage location data, the cargo location data to be shipped out and the efficient operation area data are collected. These data can be obtained through a warehouse management system, a real-time monitoring system or manual collection. The collected data is analyzed, with special attention paid to the distribution of the cargo target storage location, the cargo location to be shipped out and the efficient operation area. The distance and correlation between different areas are understood. Based on the transport buffer center point location data, one or more center point locations are determined in the warehouse, and a Y-shaped transportation path is planned according to the center point location and the distribution of the cargo target storage location, the cargo location to be shipped out and the efficient operation area, that is, starting from the center point location, the cargo is transported in two directions to form a Y-shaped path. According to the formulated Y-shaped transportation strategy, a storage location transformation plan is generated. This includes determining the details such as the order of picking up the cargo, the transportation path, and the placement position to achieve efficient cargo storage and operation process. The generated storage location transformation Y-shaped transportation strategy is applied to actual warehouse operations. This may involve operations such as adjusting the operation path of the access device and updating the operation plan in the warehouse management system.

[0051] Step S43: Based on the storage location transformation Y-shaped transportation strategy, low-power storage location transformation operations are deployed for low-load access equipment data to achieve the purpose of carbon reduction and peak regulation.

[0052] In an embodiment of the present invention, an analysis of energy consumption is performed, the current energy utilization is evaluated, and the target carbon reduction and peak-shaving targets are determined. Based on the previously formulated Y-shaped transportation strategy for storage position transformation, it is optimized with the goal of minimizing energy consumption. This involves adjusting the transportation route, optimizing the use of transportation equipment, and adjusting the settings and parameters of low-load storage and access equipment according to the optimized transportation strategy to reduce energy consumption and power requirements. This includes adjusting the operating speed, load size, and dwell time of the equipment. According to the adjusted transportation strategy and equipment settings, a new operation plan is formulated to ensure that the operation deployment can be smoothly implemented. Gradually apply the new low-power storage position transformation operation deployment plan to actual operations to ensure a robust transition, continuously monitor the effects of the operation deployment, including indicators such as energy consumption, carbon emissions, and operation efficiency, and timely adjust and optimize the deployment plan.

[0053] Preferably, the storage location conversion Y-shaped transportation strategy described in step S42 specifically includes: Based on the low-load access device data, the access device is concurrently called to generate and issue inbound and outbound storage mode data, wherein the concurrently issued inbound and outbound storage mode data includes outbound storage mode data and inbound storage mode data, wherein the outbound storage mode data is for outbound transportation of the goods to be outbound based on the position data of the goods to be outbound and the position data of the transportation buffer center point, so that the position data of the goods to be outbound is equivalent to the position data of the transportation buffer center point; wherein the inbound storage mode data is for inbound transportation of the goods to be inbound based on the target storage position data of the goods, the position data of the transportation buffer center point and the high-efficiency operation area data; when the inbound machine returns to the transportation buffer center point position, the inbound machine transports the goods to be outbound in the transportation buffer center point position to the high-efficiency operation area.

[0054] The present invention can perform outbound and inbound operations simultaneously in the same time period by concurrently calling the outbound machine and the inbound machine, thereby improving the operation efficiency and storage space utilization rate. The outbound machine and the inbound machine accurately calculate the cargo transportation path based on the location data of the cargo to be outbound, the cargo target storage location data and the transportation buffer center location data, thereby minimizing the operation time and energy consumption. When the inbound machine returns to the transportation buffer center location, it transports the cargo to be outbound to the efficient operation area, realizes the ability to dynamically adjust the operation process, flexibly allocates the operation tasks according to the actual situation, and improves the operation efficiency and flexibility. By optimizing the operation path and transportation strategy, the idling and energy waste of the storage and retrieval equipment are reduced, thereby reducing energy consumption and carbon emissions, and meeting the goal of environmental protection and energy saving. The strategy seamlessly connects the outbound and inbound operations, avoids the idle time of the operation, maximizes the utilization rate of the storage and retrieval equipment, thereby improving the overall operation efficiency and the productivity of the warehousing and logistics system. Combining the characteristics of concurrent calling, location data analysis and dynamic adjustment of the operation process, the operation efficiency is innovatively improved, the energy consumption is reduced, and it is of great significance for optimizing the operation management of the warehousing and logistics system.

[0055] In the embodiment of the present invention, according to the low-load access device data, the system performs concurrent calls to obtain the outbound machine and the inbound machine that are available at the same time, ensuring that outbound and inbound operations can be performed simultaneously in the same time period. The outbound machine determines the outbound path according to the location data of the goods to be outbound and the location data of the transportation buffer center point. The outbound machine transports the goods to be outbound to the location of the transportation buffer center point, so that the location data of the goods to be outbound is equal to the location data of the transportation buffer center point, ensuring that the outbound goods arrive at the buffer zone smoothly. The inbound machine determines the inbound path according to the target storage location data of the goods, the location data of the transportation buffer center point and the data of the efficient operation area. The inbound machine transports the goods from the transportation buffer center point position to the target storage location, ensuring that the goods are accurately placed in the designated storage area. When the inbound machine returns to the location of the transportation buffer center point, the system will transport the goods to be outbound in the location to the efficient operation area for further processing or sorting.

[0056] In this specification, a low-carbon and high-efficiency intelligent stereoscopic warehouse system is provided, which is used to execute the low-carbon and high-efficiency intelligent stereoscopic warehouse operation method as described above, including: The high-efficiency zone positioning module is used to obtain the overall data of the three-dimensional warehouse, where the overall data of the three-dimensional warehouse includes the electricity consumption data of the three-dimensional warehouse, the order data of inbound and outbound warehouses, the storage data of the three-dimensional warehouse, the location data of the three-dimensional warehouse terminal and the storage and access equipment data of the three-dimensional warehouse; obtain the empty warehouse location data of the three-dimensional warehouse; perform channel network analysis on the empty warehouse location data and the three-dimensional warehouse terminal location data of the three-dimensional warehouse to obtain the high-efficiency operation area data; The terminal status acquisition module is used to obtain the historical operation data of the three-dimensional warehouse; conduct frequent event sequence mining on the historical operation data of the three-dimensional warehouse and the inbound and outbound order data to obtain the frequent event sequence data set; perform inbound and outbound demand inference on the frequent event sequence data set to obtain the inbound and outbound demand data for the future time period; The buffer positioning module is used to dynamically adjust the power of the three-dimensional warehouse access equipment data based on the future time period inbound and outbound demand data and the three-dimensional warehouse power consumption data to obtain the adjusted access equipment data; locate the goods to be shipped out of the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the location data of the goods to be shipped out; locate the buffer transfer area of ​​the goods target storage location data, the location data of the goods to be shipped out and the efficient operation area data to generate the transportation buffer center point location data; The mode switching module is used to deploy full-power in and out-of-warehouse operations for the high-load access device data when the access device data is adjusted to high-load access device data, so as to improve the efficiency of in and out-of-warehouse operations; when the access device data is adjusted to low-load access device data, low-power storage position transformation operations are deployed for the low-load access device data, so as to achieve the purpose of carbon reduction and peak regulation.

[0057] The benefit of the present invention lies in that comprehensive analysis is performed by acquiring the overall data of the stereoscopic warehouse, including power consumption data, inbound and outbound order data, storage data, terminal location data and access equipment data. In this way, the operation of the stereoscopic warehouse can be fully understood, providing basic data for subsequent optimization and decision-making. By locating the empty warehouse of the stereoscopic warehouse storage data, the empty warehouse location data of the stereoscopic warehouse is obtained. In this way, it is possible to accurately know which positions in the warehouse are idle, which provides convenience for the subsequent storage and retrieval of goods. By performing channel network analysis on the empty warehouse location data and the terminal location data, efficient operation area data is obtained. In this way, the most suitable area for cargo storage and retrieval in the stereoscopic warehouse can be determined, which improves operation efficiency and reduces cargo transportation time. By mining the frequent event sequence of the stereoscopic warehouse historical operation data and inbound and outbound order data, a frequent event sequence data set is obtained. In this way, frequently occurring operation events can be discovered and predicted, providing a basis for subsequent planning and scheduling. By inferring the inbound and outbound demand of the frequent event sequence data set, the inbound and outbound demand data of the future time period is obtained. In this way, the inbound and outbound demand of the warehouse can be predicted in advance, providing a basis for the dynamic power adjustment and operation deployment of the access equipment. Based on the in-and-out demand data and power consumption data of the future time period, the data of the access equipment is dynamically adjusted for power, and the operation is deployed according to the adjusted data. In this way, the power and operation mode of the access equipment can be flexibly adjusted according to the actual demand and energy consumption, and the energy utilization efficiency and operation efficiency can be improved. By locating the buffer transfer area of ​​the target storage location data of the goods, the location data of the goods to be shipped out, and the data of the efficient operation area, the transportation buffer center point location data is generated. In this way, the transportation path and process of the goods can be optimized, and the transportation time and energy consumption can be reduced. According to the adjusted access equipment data, it is divided into high-load and low-load access equipment data, and full-power in-and-out operation deployment and low-power storage position conversion operation deployment are respectively carried out. In this way, flexible scheduling can be carried out according to the actual situation, improving the efficiency of in-and-out storage and saving energy consumption. Combining technologies such as data analysis, predictive inference and dynamic scheduling, through comprehensive analysis and optimization decision-making, the goals of efficient operation of intelligent stereoscopic warehouses, reducing energy consumption and improving operation efficiency can be achieved, thus providing new ideas and solutions for the intelligent management of stereoscopic warehouses.

[0058] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0059] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A low-carbon and high-efficiency intelligent stereoscopic warehouse operation method, characterized in that: The following steps are involved: Step S1: Acquire the overall data of the three-dimensional warehouse, wherein the overall data of the three-dimensional warehouse includes the electricity consumption data of the three-dimensional warehouse, the inbound and outbound order data, the storage data of the three-dimensional warehouse, the location data of the three-dimensional warehouse terminal and the storage and access equipment data of the three-dimensional warehouse; Obtain the empty warehouse position data of the stereoscopic warehouse; Conduct channel network analysis on the empty warehouse location data and the terminal location data of the three-dimensional warehouse to obtain the efficient operation area data; Step S2: Acquire the historical operation data of the three-dimensional warehouse; perform frequent event sequence mining on the historical operation data of the three-dimensional warehouse and the in-and-out order data to obtain a frequent event sequence data set; perform in-and-out demand inference on the frequent event sequence data set to obtain in-and-out demand data for future time periods; Step S3: dynamically adjusting the power of the storage and access equipment data of the three-dimensional warehouse based on the storage and access demand data in the future time period and the power consumption data of the three-dimensional warehouse to obtain the adjusted storage and access equipment data; Based on the inbound and outbound order data, the warehouse storage data is used to locate the goods to be shipped out, and the location data of the goods to be shipped out is obtained; Based on the inbound and outbound order data, the storage location of the three-dimensional warehouse storage data is allocated to obtain the target storage location data of the goods; Locate the buffer transfer area based on the cargo target storage location data, cargo location data to be shipped out, and efficient operation area data, and generate transportation buffer center location data; Step S4: When the access device data is adjusted to high-load access device data, full-power in and out-of-warehouse operations are deployed for the high-load access device data to improve the efficiency of in and out-of-warehouse operations; when the access device data is adjusted to low-load access device data, low-power storage position transformation operations are deployed for the low-load access device data to achieve the purpose of carbon reduction and peak regulation.

2. The low-carbon and high-efficiency intelligent stereoscopic warehouse operation method according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire the overall data of the three-dimensional warehouse, wherein the overall data of the three-dimensional warehouse includes the electricity consumption data of the three-dimensional warehouse, the inbound and outbound order data, the storage data of the three-dimensional warehouse, the location data of the three-dimensional warehouse terminal and the storage and access equipment data of the three-dimensional warehouse; Step S12: Acquire the empty warehouse position data of the three-dimensional warehouse; Step S13: Perform channel network analysis on the empty warehouse position data and the terminal position data of the three-dimensional warehouse to generate the shortest channel data; Step S14: Based on the shortest channel data, the empty warehouse position data of the stereoscopic warehouse is matched to obtain the efficient operation area data.

3. The low-carbon and high-efficiency intelligent stereoscopic warehouse operation method according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Acquire historical operation data of the three-dimensional warehouse; Step S22: performing event sequence coding on the historical operation data of the three-dimensional warehouse and the in-and-out order data to obtain an event sequence data set; Step S23: performing frequent event sequence mining on the event sequence data set to obtain a frequent event sequence data set; Step S24: performing future pattern matching on the frequent event sequence data set to generate a matching event sequence data set; Step S25: Infer the inbound and outbound demand of the matching event sequence data set to obtain the inbound and outbound demand data for the future time period.

4. The low-carbon and high-efficiency intelligent stereoscopic warehouse operation method according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Perform peak and valley analysis on the electricity consumption data of the three-dimensional warehouse to obtain predicted electricity price time period data; Step S32: dynamically adjusting the power of the storage and access equipment data of the three-dimensional warehouse based on the storage and access demand data of the future time period and the predicted electricity price time period data to obtain the adjusted storage and access equipment data; Step S33: positioning the goods to be shipped out of the three-dimensional warehouse storage data based on the warehouse entry and exit order data to obtain the location data of the goods to be shipped out; allocating the inbound storage location of the three-dimensional warehouse storage data based on the warehouse entry and exit order data to obtain the target storage location data of the goods; Step S34: locating the buffer transfer area for the cargo target storage location data, the cargo location data to be shipped out, and the high-efficiency operation area data, and generating the transport buffer center location data.

5. The low-carbon and high-efficiency intelligent stereoscopic warehouse operation method according to claim 4 is characterized in that: Step S32 includes the following steps: Step S321: performing data feature encoding on the future time period in-and-out demand data and the predicted electricity price period data to obtain data point combination data; Step S322: performing asynchronous frequency modulation task scheduling on the storage and access device data of the stereoscopic warehouse based on the data point combination data to obtain asynchronous frequency modulation task queue data; Step S323: performing variable clock speed control on the asynchronous frequency modulation task queue data to generate variable clock speed data; Step S324: dynamically modulate the storage and access device data of the stereoscopic warehouse based on the variable clock speed data to obtain adjusted storage and access device data.

6. The low-carbon and high-efficiency intelligent stereoscopic warehouse operation method according to claim 4 is characterized in that: Step S34 includes the following steps: Step S341: Perform multi-channel transportation simulation on the cargo target storage location data and the efficient operation area data to generate a warehousing channel data set; Step S342: Perform multi-channel transportation simulation on the location data of the goods to be shipped and the data of the efficient operation area to generate a shipping channel data set; Step S343: performing extreme sharing path matching on the inbound channel data set and the outbound channel data set to obtain extreme sharing channel data; Step S344: locate the separation point of the extreme shared channel data to obtain the transport buffer center point position data.

7. The low-carbon and high-efficiency intelligent stereoscopic warehouse operation method according to claim 6 is characterized in that: Step S343 includes the following steps: Step S3431: Perform frequent item set mining on the inbound channel data set and the outbound channel data set to obtain frequent path pattern data; Step S3432: performing maximum similarity edit distance matching on the inbound channel data set and the outbound channel data set based on the frequent path pattern data to obtain matching path pattern data; Step S3433: performing longitudinal node calculation on the matching path pattern data to obtain longitudinal node data of each path; Step S3434: Perform maximum longitudinal distance matching on the matching path pattern data based on the longitudinal node data of each path to generate extreme shared channel data.

8. The low-carbon and high-efficiency intelligent stereoscopic warehouse operation method according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: when the access device data is adjusted to high-load access device data, full-power storage and retrieval operation deployment is performed on the high-load access device data to improve storage and retrieval efficiency; Step S42: when the access device data is adjusted to low-load access device data, a Y-shaped transportation strategy is formulated for the cargo target storage location data, the cargo location data to be shipped out, and the high-efficiency operation area data based on the transportation buffer center location data, and a storage location transformation Y-shaped transportation strategy is generated; Step S43: Based on the storage location transformation Y-shaped transportation strategy, low-power storage location transformation operations are deployed for low-load access equipment data to achieve the purpose of carbon reduction and peak regulation.

9. The low-carbon and high-efficiency intelligent stereoscopic warehouse operation method according to claim 8 is characterized in that: The storage location conversion Y-shaped transportation strategy described in step S42 specifically includes: Based on the low-load access device data, the access device is concurrently called to generate and issue inbound and outbound storage mode data, wherein the concurrently issued inbound and outbound storage mode data includes outbound storage mode data and inbound storage mode data, wherein the outbound storage mode data is for outbound transportation of the goods to be outbound based on the position data of the goods to be outbound and the position data of the transportation buffer center point, so that the position data of the goods to be outbound is equivalent to the position data of the transportation buffer center point; wherein the inbound storage mode data is for inbound transportation of the goods to be inbound based on the target storage position data of the goods, the position data of the transportation buffer center point and the high-efficiency operation area data; when the inbound machine returns to the transportation buffer center point position, the inbound machine transports the goods to be outbound in the transportation buffer center point position to the high-efficiency operation area.

10. A low-carbon and high-efficiency intelligent stereoscopic warehouse system, characterized in that: Used to execute the low-carbon and high-efficiency intelligent stereoscopic warehouse operation method as claimed in claim 1, the low-carbon and high-efficiency intelligent stereoscopic warehouse system comprises: The high-efficiency zone positioning module is used to obtain the overall data of the three-dimensional warehouse, where the overall data of the three-dimensional warehouse includes the electricity consumption data of the three-dimensional warehouse, the order data of inbound and outbound warehouses, the storage data of the three-dimensional warehouse, the location data of the three-dimensional warehouse terminal and the storage and access equipment data of the three-dimensional warehouse; obtain the empty warehouse location data of the three-dimensional warehouse; perform channel network analysis on the empty warehouse location data and the three-dimensional warehouse terminal location data of the three-dimensional warehouse to obtain the high-efficiency operation area data; The terminal status acquisition module is used to obtain the historical operation data of the three-dimensional warehouse; conduct frequent event sequence mining on the historical operation data of the three-dimensional warehouse and the inbound and outbound order data to obtain the frequent event sequence data set; perform inbound and outbound demand inference on the frequent event sequence data set to obtain the inbound and outbound demand data for the future time period; The buffer positioning module is used to dynamically adjust the power of the three-dimensional warehouse access equipment data based on the future time period inbound and outbound demand data and the three-dimensional warehouse power consumption data to obtain the adjusted access equipment data; locate the goods to be shipped out of the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the location data of the goods to be shipped out; allocate the inbound storage location to the three-dimensional warehouse storage data based on the inbound and outbound order data to obtain the target storage location data of the goods; locate the buffer transfer area for the target storage location data of the goods, the location data of the goods to be shipped out and the efficient operation area data to generate the transportation buffer center point location data; The mode switching module is used to deploy full-power in and out-of-warehouse operations for the high-load access device data when the access device data is adjusted to high-load access device data, so as to improve the efficiency of in and out-of-warehouse operations; when the access device data is adjusted to low-load access device data, low-power storage position transformation operations are deployed for the low-load access device data, so as to achieve the purpose of carbon reduction and peak regulation.

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