Intelligent three-dimensional warehouse storage management method and system

Abstract

The invention discloses an intelligent three-dimensional warehouse storage management method and system, and relates to the technical field of warehouse management, and the method comprises the steps: receiving a task instruction from an upper system through a communication network, and distributing specific execution equipment according to an equipment operation state and real-time inventory data; calculating a logistics path based on the equipment position information, the reservoir area environment data and the task demand, and generating a control instruction to drive the execution equipment to execute operation according to the path; according to the goods attributes and the storage rules, goods allocation distribution is optimized through a dynamic programming algorithm, and goods storage and taking operation is completed; the operation state of execution equipment is collected in real time through a sensor, abnormity is recognized based on collected data, an alarm signal is triggered through an upper system, and meanwhile a task execution strategy is adjusted. According to the intelligent three-dimensional warehouse storage management method provided by the invention, comprehensive monitoring of the equipment health state is realized, abnormity can be found in time, an alarm mechanism can be triggered, and the equipment fault risk is remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management, and in particular to an intelligent vertical warehouse storage management method and system. Background Art

[0002] With the rapid development of the logistics industry and warehouse management technology, intelligent warehouse management systems have gradually become an important means to improve warehouse efficiency and optimize resource utilization. Traditional warehouse management systems mainly rely on preset rules and manual scheduling. Equipment scheduling and task allocation are usually based on fixed processes, lacking the ability to dynamically evaluate and adjust the real-time status of equipment. When the warehouse environment changes or the equipment status is abnormal, the existing system is difficult to respond quickly, resulting in a decrease in task execution efficiency or even task interruption.

[0003] Existing technologies are mostly based on static rules in path planning, which makes it difficult to effectively respond to dynamic changes in the warehouse environment, such as the operation trajectory of other equipment or the temporary appearance of obstacles, resulting in poor adaptability of path planning. In addition, in terms of cargo space allocation, traditional methods are usually based on cargo category or area, and cannot dynamically adjust cargo space allocation rules. Resource utilization efficiency is low, and it is easy to waste storage space or overload certain areas. At the same time, the monitoring of equipment operation status is still mainly based on post-failure alarms, and there is a lack of real-time assessment of the health status of the equipment, resulting in poor equipment operation safety and reliability. The current task scheduling mechanism shows great limitations in dealing with emergencies. For example, after equipment failure or path obstruction, the task adjustment strategy is single, dynamic task allocation cannot be achieved, and the overall storage system lacks operational flexibility. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: solving the problems of traditional systems in equipment scheduling, path planning, cargo space allocation, equipment monitoring and task adjustment, and overcoming the limitations of static planning.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent vertical library storage management method, comprising:

[0007] Receive task instructions from the upper system through the communication network, and assign specific execution equipment according to the equipment operation status and real-time inventory data;

[0008] Calculate the logistics path based on equipment location information, warehouse environment data and task requirements, and generate control instructions to drive the execution equipment to perform operations according to the path;

[0009] According to the cargo attributes and storage rules, the dynamic programming algorithm is used to optimize the cargo space allocation and complete the cargo storage and retrieval operations;

[0010] The operating status of the execution equipment is collected in real time through sensors, anomalies are identified based on the collected data, and alarm signals are triggered through the upper system, while the task execution strategy is adjusted.

[0011] As a preferred solution of the intelligent vertical library storage management method of the present invention, wherein: the allocation of specific execution devices includes the upper system sending task instructions to the local management platform through the wireless communication protocol, and the local management platform parsing the task instructions and scheduling;

[0012] The local management platform analyzes the equipment load status based on the real-time equipment operation status data and inventory data, and selects equipment to perform tasks;

[0013] If the initially allocated device is overloaded or fails, the local management platform recalculates and selects other idle devices based on the real-time status of the device.

[0014] As a preferred solution of the intelligent vertical warehouse storage management method of the present invention, wherein: the driving execution device to execute the operation according to the path includes the upper system obtaining the current position of the execution device through real-time positioning technology, and building a warehouse environment model in combination with the warehouse environment data;

[0015] The upper system uses the path planning algorithm to calculate all candidate paths and select the optimal path based on the task requirements;

[0016] The path planning results are transmitted to the execution device control system through control instructions. The execution device performs operations according to the generated path. If the path deviation occurs during the execution process, the execution device calculates the new optimal path and adjusts the operation trajectory.

[0017] As a preferred solution of the intelligent vertical warehouse storage management method of the present invention, wherein: the completion of the storage and retrieval operation of the goods includes the upper system building a dynamic programming model according to the attributes and storage rules of the goods;

[0018] The upper system uses dynamic programming algorithms to calculate the optimal storage location for goods and optimizes the allocation of cargo space based on the calculation results;

[0019] The calculation results generate storage and retrieval tasks, and the control instructions are sent to the execution equipment through the control platform to guide the execution equipment to complete the goods storage and retrieval operations and update the inventory information in real time.

[0020] As a preferred solution of the intelligent vertical library storage management method of the present invention, wherein: the collecting the running status of the execution device includes installing a plurality of sensors in the execution device to collect the working status data of the execution device;

[0021] During operation, the execution equipment uses sensors to monitor and record parameters in real time, and the data is transmitted to the local management platform via wired or wireless communication networks;

[0022] The local management platform compares the collected data with the preset working parameters of the equipment to determine whether there are any abnormalities in the equipment and generates an operation report for further operation.

[0023] As a preferred solution of the intelligent vertical library storage management method of the present invention, wherein: the triggering of the alarm signal by the upper system includes the upper system performing real-time analysis on the operating status data of the executing device and judging whether the device exceeds the normal working range in combination with the preset device working threshold;

[0024] When sensor data indicates that the equipment is abnormal, the upper system triggers the alarm mechanism, marks the abnormal equipment and notifies the operator;

[0025] The upper system automatically adjusts the task execution strategy according to the preset processing strategy, transfers the task to other available devices, or adjusts the task execution order according to the device status and modifies the task execution plan.

[0026] As a preferred solution of the intelligent vertical library storage management method of the present invention, wherein: the adjustment task execution strategy includes that after the device abnormality is identified, the upper system determines whether the current task can continue to be executed by evaluating the task priority;

[0027] If the device abnormality affects the execution of the current task, the upper system adjusts the task execution strategy according to the task urgency, device status and available resources, and transfers the task to other devices or rearranges the time and order of task execution;

[0028] The adjusted task information is synchronized to all relevant execution devices through the control platform. The execution devices continue to work according to the new task arrangement and update the task execution progress in real time.

[0029] Another object of the present invention is to provide an intelligent vertical warehouse storage management system, which can solve the problems of delayed task allocation, low path planning accuracy, uneven cargo space utilization, and high equipment failure rate in existing intelligent warehouse management methods by constructing an intelligent vertical warehouse storage management system.

[0030] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent vertical warehouse storage management system, comprising: an equipment allocation module, a path planning module, a cargo storage and retrieval module and a scheduling and early warning module; the equipment allocation module is used to receive task instructions from a host system through a communication network, and allocate specific execution equipment according to the equipment operation status and real-time inventory data; the path planning module is used to calculate the logistics path based on equipment location information, warehouse environment data and task requirements, and generate control instructions to drive the execution equipment to perform operations according to the path; the cargo storage and retrieval module is used to optimize the cargo location allocation according to the cargo attributes and storage rules using a dynamic programming algorithm, and complete the cargo storage and retrieval operations; the scheduling and early warning module is used to collect the operation status of the execution equipment in real time through sensors, identify abnormalities based on the collected data, trigger an alarm signal through the host system, and adjust the task execution strategy at the same time.

[0031] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the intelligent vertical library storage management method when executing the computer program.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the intelligent vertical library storage management method as described above.

[0033] Beneficial effects of the present invention: The intelligent vertical warehouse storage management method provided by the present invention collects equipment operating status parameters in real time through multiple sensors, and performs comparative analysis in combination with the rated parameter range of the equipment, thereby realizing comprehensive monitoring of the health status of the equipment, being able to detect anomalies in a timely manner and trigger an alarm mechanism, significantly reducing the risk of equipment failure. During the task execution process, the present invention can flexibly adjust the task execution order or allocate spare equipment according to task priority and equipment status by dynamically adjusting the task scheduling strategy, thereby avoiding task interruptions or delays caused by emergencies. The method of the present invention forms a complete closed-loop management system from equipment scheduling, path planning to cargo space allocation and task adjustment, significantly improving the operating efficiency, adaptability and reliability of the intelligent vertical warehouse. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0035] Figure 1 An overall flow chart of an intelligent vertical library storage management method provided by one embodiment of the present invention.

[0036] Figure 2 This is an overall structural diagram of an intelligent vertical library storage management system provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Example 1

[0040] Reference Figure 1 , as an embodiment of the present invention, provides an intelligent vertical library storage management method, comprising:

[0041] Receive task instructions from the upper system through the communication network, and assign specific execution equipment according to the equipment operation status and real-time inventory data;

[0042] Calculate the logistics path based on equipment location information, warehouse environment data and task requirements, and generate control instructions to drive the execution equipment to perform operations according to the path;

[0043] According to the cargo attributes and storage rules, the dynamic programming algorithm is used to optimize the cargo space allocation and complete the cargo storage and retrieval operations;

[0044] The operating status of the execution equipment is collected in real time through sensors, anomalies are identified based on the collected data, and alarm signals are triggered through the upper system, while the task execution strategy is adjusted.

[0045] The allocation of specific execution devices includes the upper system sending the task instructions to the local management platform through the wireless communication protocol, and the local management platform parsing the task instructions and scheduling;

[0046] The local management platform analyzes the equipment load status based on the real-time equipment operation status data (such as current, voltage, temperature, etc.) and inventory data (such as inventory quantity, cargo type, storage location, etc.), and selects equipment to perform tasks;

[0047] If the initially allocated device is overloaded or fails, the local management platform recalculates and selects other idle devices based on the real-time status of the device (such as fault code, device health, etc.).

[0048] The upper system sends the task instructions to the local management platform through the wireless communication protocol. The task instructions contain cargo information, target location and equipment requirements. After receiving the task instructions, the local management platform parses the task content into operation objectives (such as warehousing or outbound), cargo type and priority. The task parsing logic uses a field matching algorithm to check the integrity of the task fields, such as whether the target cargo location exists and whether the cargo code is valid, and stores the valid tasks as structured data.

[0049] The local management platform obtains operating status data from equipment sensors through real-time communication interfaces, such as equipment temperature, current, vibration frequency, etc., and reads the current cargo storage status, cargo attributes, and inventory location from the inventory management module. The system matches the operating status with the inventory data by timestamp and generates an integrated real-time task evaluation table. The integration logic ensures data consistency, such as time synchronization correction for lagged sensor data.

[0050] The task evaluation table is used to screen candidate devices suitable for performing tasks. The allocation logic comprehensively considers the current load, operating status and task requirements of the device, and selects the best device through a priority sorting algorithm. For example, when the current load of a device has reached the upper limit, the system automatically removes it from the candidate list and gives priority to backup devices with lower loads. If a device detects an abnormal operation after the task is assigned, the system triggers the reallocation process and marks and archives the status of the abnormal device.

[0051] By dynamically integrating task instruction analysis, equipment operation status and inventory data, combined with priority sorting, the task allocation is accurate and flexible. Especially in the case of equipment failure or overload, the dynamic allocation mechanism effectively reduces the risk of task interruption and improves the overall execution efficiency of the warehousing system.

[0052] The driving execution device to execute the operation according to the path includes the upper system obtaining the current position of the execution device through real-time positioning technology (such as laser radar, ultra-wideband positioning, RFID, etc.), and building a warehouse environment model in combination with warehouse environment data (such as channel width, cargo stacking method, obstacle location, etc.);

[0053] The upper system uses a path planning algorithm (such as A* algorithm, Dijkstra algorithm, etc.) to calculate all candidate paths and select the optimal path based on task requirements (such as priority, equipment load, path length, etc.);

[0054] The path planning results are transmitted to the execution device control system through control instructions. The execution device performs operations according to the generated path. If the path deviation occurs during the execution process, the execution device calculates the new optimal path and adjusts the operation trajectory.

[0055] The upper system obtains the current position of the execution equipment through real-time positioning technology, such as using laser radar to scan the location data of fixed facilities and mobile equipment in the warehouse area. The warehouse environment modeling uses a three-dimensional coordinate system to record static structures (such as shelves and channels) and dynamic obstacles (such as other running equipment). During the modeling process, an update cycle is set for dynamic data to reduce computing overhead. For example, the dynamic position is updated every 3 seconds to reflect environmental changes.

[0056] Path planning is based on a heuristic search method, combining the current location of the device and the target location to search for all candidate paths in the environment model. The path cost function considers the path length, passability, and the impact of obstacles at the same time. For example, obstacle nodes are assigned a higher cost value to avoid the path passing through. The system scores each candidate path and selects the path with the lowest score as the optimal path.

[0057] After the path is generated, the system converts the path node information into device motion instructions, including straight-line movement, turning, and speed adjustment. During the path execution process, if the device detects path deviation or obstacle blocking, the system calculates the new path from the current position to the target position through the repositioning module and issues correction instructions in real time. The calculation of the corrected path gives priority to the task time limit to ensure the timeliness of the path adjustment.

[0058] Combining three-dimensional environmental modeling with dynamic path correction, real-time adaptability and execution accuracy of path planning are achieved, mission failures due to dynamic environmental changes or equipment offsets are avoided, and the completion rate of logistics tasks is improved.

[0059] The completion of the storage and access operation of the goods includes the upper system building a dynamic programming model according to the attributes of the goods (such as size, weight, storage conditions, etc.) and storage rules (such as priority access, load balancing, temperature and humidity requirements, etc.);

[0060] The upper system uses dynamic programming algorithms (such as K-means clustering, genetic algorithms, etc.) to calculate the optimal storage location of the goods, optimizes the allocation of cargo space based on the calculation results, improves storage efficiency and meets storage rules;

[0061] The calculation results generate storage and retrieval tasks, and the control instructions are sent to the execution equipment through the control platform to guide the execution equipment to complete the goods storage and retrieval operations and update the inventory information in real time.

[0062] After receiving the storage and retrieval task instructions, the upper system first screens all cargo locations in the warehouse area according to the cargo attributes (such as size, weight, and storage conditions) included in the task. The screening process comprehensively considers the compatibility of the cargo and the cargo location. For example, when the cargo needs to be refrigerated, only cargo locations with refrigeration functions are screened; when the cargo is heavy, cargo locations with low load-bearing capacity are excluded. The adaptability of the cargo location is determined by the preset rules and the real-time warehouse environmental parameters. For example, if the current temperature of a cargo location exceeds the allowable range of the cargo, the cargo location will be excluded from the candidate pool.

[0063] Based on the initial screening of cargo locations, the system dynamically plans the candidate cargo locations that meet the conditions. The goal of dynamic planning is to select the cargo location with the highest comprehensive score. The scoring dimensions include cargo location distance, current occupancy rate, and cargo location adaptability weight. For example, for cargo locations that are far away but vacant for a long time, the system will give priority to the cargo location by appropriately increasing the vacancy weight value to balance resource utilization. The dynamic planning process is based on step-by-step iterative optimization. By dynamically adjusting the scoring rules, it ensures that the cargo location selected each time can meet the current task requirements.

[0064] After the cargo location is allocated, the upper system generates a storage and retrieval task instruction, which includes the coordinate information of the cargo location and the target cargo location, as well as the execution device number involved in the task. The instruction is sent to the execution device through the local management platform. The execution device calls the path planning module according to the instruction to calculate the optimal path, and completes the storage or retrieval operation of the cargo according to the generated path.

[0065] After the task is completed, the system will update the status of the cargo space in real time, including whether the cargo space is occupied, the current storage of goods and changes in the cargo space environment (such as temperature and humidity). The system compares and analyzes the time cost involved in the task execution process with the cargo space score and dynamically optimizes the scoring rules, such as reducing the scoring priority of cargo spaces that have been assigned multiple times and caused task delays.

[0066] Optimizing cargo space allocation through dynamic planning not only improves the efficiency of cargo storage and retrieval, but also greatly improves the utilization rate of storage space. Especially in multi-task parallel scenarios, cargo space resource allocation is more balanced, effectively reducing the resource conflict rate.

[0067] The collecting of the running status of the execution device includes installing a plurality of sensors (such as a temperature sensor, an acceleration sensor, a current sensor, etc.) in the execution device to collect the working status data of the execution device;

[0068] During operation, the execution equipment uses sensors to monitor and record parameters such as current, voltage, temperature, vibration, etc. in real time, and the data is transmitted to the local management platform through wired or wireless communication networks;

[0069] The local management platform compares the collected data with the preset working parameters of the equipment to determine whether there are any abnormalities in the equipment and generates an operation report for further operation.

[0070] Various types of sensors are installed in the execution equipment, including thermocouple sensors for monitoring temperature, accelerometers for monitoring vibration, and Hall sensors for monitoring current. These sensors are arranged in a distributed architecture to ensure that key components of equipment operation (such as motors and bearings) are within the monitoring range. The data acquisition module collects sensor data at a fixed frequency (such as 10 times per second) and uses a filtering algorithm to clean up the collected noise signals.

[0071] The data collected by the sensors are transmitted to the local management platform in real time through the communication interface of the equipment. During the data transmission process, the system uses an error checking mechanism to ensure data integrity. The management platform integrates the transmitted data. For example, the temperature, vibration and current data provided by multiple sensors are integrated into comprehensive parameters of the equipment health status for subsequent status evaluation.

[0072] The management platform compares the integrated parameters with the preset working thresholds of the equipment, such as whether the temperature exceeds the safety upper limit of 50°C of the equipment, and whether the current is lower than the rated operating current of the equipment. The comparison results generate a device health score, which gradually decreases based on the degree of deviation of the health status parameters. When the score is lower than the set threshold (such as 70 points), the system automatically marks the device as abnormal and notifies the task scheduling module to suspend the task allocation of the device.

[0073] After the health score and abnormal mark are generated, the management platform automatically generates an operation log, which includes the collected abnormal parameters, deviation range and timestamp. The log file is archived through the local storage module and uploaded to the upper system for subsequent equipment maintenance and task analysis.

[0074] Through multi-sensor fusion and real-time data comparison, the present invention can not only comprehensively monitor the operating status of the equipment, but also detect potential faults in advance, thereby reducing the probability of equipment damage and improving the safety and reliability of task execution.

[0075] The triggering of the alarm signal by the upper system includes the upper system performing real-time analysis on the operating status data of the executing device and judging whether the device exceeds the normal operating range in combination with the preset device operating threshold (such as device load, temperature, operating time, etc.);

[0076] When sensor data indicates that the equipment is abnormal (such as over-temperature, abnormal current, vibration exceeding the limit, etc.), the upper system triggers the alarm mechanism, marks the abnormal equipment and notifies the operator;

[0077] The upper system automatically adjusts the task execution strategy according to the preset processing strategy, transfers the task to other available devices, or adjusts the task execution order according to the device status and modifies the task execution plan.

[0078] When the management platform detects that the sensor data exceeds the safety threshold (for example, the temperature exceeds 60°C or the vibration frequency exceeds 2 times the normal range), the system will immediately trigger an abnormal alarm signal. The alarm information is synchronously notified to the operator through the sound and light equipment and the wireless communication terminal. The system marks the device status according to the abnormality type, for example, updating the operating status of the device to "warning" or "fault".

[0079] After the status of the abnormal device is updated, the system suspends its current task and triggers the task scheduling module to reallocate tasks. The scheduling module selects an adjustment strategy based on the urgency of the task. For example, for low-priority tasks, the system temporarily delays task execution; for high-priority tasks, it prioritizes calling backup devices and replans the task path in real time.

[0080] Equipment marked as "faulty" will be automatically isolated by the system to prevent it from continuing to participate in task allocation. The system generates a maintenance plan based on the abnormal report of the equipment, such as arranging equipment inspection and maintenance during non-task peak periods, and pushes the maintenance plan to the upper system for coordinated scheduling.

[0081] After the task adjustment is completed, the system will archive the key data of the adjustment process (such as equipment switching time and delay duration). The system optimizes the scheduling strategy based on the recorded data, such as setting a higher priority for the backup equipment, to further improve the efficiency of task adjustment.

[0082] Through real-time abnormal classification and rapid task adjustment, the present invention effectively reduces the impact of abnormal equipment on task execution, improves the flexibility of multi-task scheduling and the fault tolerance of the system.

[0083] The adjustment task execution strategy includes, after the device abnormality is identified, the upper system determines whether the current task can continue to be executed by evaluating the task priority;

[0084] If the device abnormality affects the execution of the current task, the upper system adjusts the task execution strategy according to the task urgency, device status and available resources, and transfers the task to other devices or rearranges the time and order of task execution;

[0085] The adjusted task information is synchronized to all relevant execution devices through the control platform. The execution devices continue to work according to the new task arrangement and update the task execution progress in real time.

[0086] Dynamic adjustment of task priority assessment

[0087] When an abnormality occurs in a device, the system will immediately re-evaluate the priority of all tasks. The evaluation logic takes into account the urgency of the task, the impact on inventory turnover, and the availability of spare equipment. For example, for high-priority tasks that need to be completed within a specific time period, the system will prioritize spare equipment for execution; for low-priority tasks, they will be delayed.

[0088] After the task plan is adjusted, the system synchronizes the updated plan to the relevant devices in real time through the task management module. The synchronization process is implemented through a low-latency communication protocol to ensure that the devices can respond quickly to the plan adjustment. The updated plan information includes the results of the task reallocation, the new path planning instructions, and the execution status of the device.

[0089] After the backup device takes over the task, the system monitors the task execution process in real time through sensors and path planning modules. The monitoring content includes the running trajectory of the device, the degree of deviation between the task completion time and the path planning. If new anomalies are found during the task execution, the system will trigger the adjustment mechanism again to ensure that the task can be completed smoothly.

[0090] After the task is completed, the system will record the execution efficiency during the adjustment process (such as the time to switch equipment and the task delay time) in the log file, and optimize the evaluation weight of the task priority through the data analysis module. For example, the priority weight of the task with a longer equipment switching time will be reduced, thereby optimizing future task adjustment decisions.

[0091] Through dynamic priority adjustment and real-time task synchronization, the present invention maximizes resource utilization efficiency in multi-task scheduling scenarios, effectively reduces task delays caused by equipment abnormalities, and improves the stability of task execution and the overall performance of the warehousing system.

[0092] Example 2

[0093] Reference Figure 2 , as an embodiment of the present invention, provides an intelligent vertical library storage management system, including:

[0094] Equipment allocation module 100, route planning module 200, cargo storage and access module 300 and dispatch warning module 400;

[0095] The equipment allocation module 100 is used to receive task instructions from the upper system through the communication network, and allocate specific execution equipment according to the equipment operation status and real-time inventory data;

[0096] The path planning module 200 is used to calculate the logistics path based on the equipment location information, the warehouse environment data and the task requirements, and generate control instructions to drive the execution equipment to perform operations according to the path;

[0097] The cargo storage and retrieval module 300 is used to optimize cargo space allocation and complete cargo storage and retrieval operations based on cargo attributes and storage rules using a dynamic programming algorithm;

[0098] The scheduling warning module 400 is used to collect the operating status of the execution equipment in real time through sensors, identify abnormalities based on the collected data, trigger an alarm signal through the upper system, and adjust the task execution strategy.

[0099] Example 3

[0100] An embodiment of the present invention is different from the first two embodiments in that:

[0101] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0103] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0104] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0105] Example 4

[0106] As an embodiment of the present invention, a smart vertical library storage management method is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0107] The experiment simulates a typical intelligent vertical warehouse scenario, which contains 500 cargo spaces and 10 devices in the warehouse area, with three types of cargo (ordinary cargo, refrigerated cargo, and fragile cargo). The system receives a total of 100 tasks, including warehousing, outbound cargo, and cargo space adjustment. The equipment operation status is randomly set, including normal load, overload, and abnormal status. The warehouse environment dynamically generates obstacles to simulate changes in actual usage scenarios. The test compares five core indicators: task allocation time, path planning error, cargo storage and retrieval efficiency, equipment failure rate, and task adjustment response time.

[0108] Traditional methods use static rules to allocate equipment, and task allocation is based on the task queue preset by the equipment, without considering the equipment's operating status in real time. Path planning is based on a static shortest path algorithm, and obstacles are considered fixed, so the path cannot be adjusted dynamically. Cargo allocation is mainly based on fixed rules, such as allocating to specific areas according to the type of goods, without considering real-time resource utilization. Equipment status monitoring is based on periodic manual inspections or equipment failures that trigger alarms, and lacks real-time monitoring and early warning functions. Task adjustment relies on manual intervention, and task reallocation after equipment failure takes a long time.

[0109] The present invention adopts a dynamic equipment allocation mechanism to collect the equipment operation status and load conditions in real time, and allocates equipment by priority sorting in combination with task requirements. Path planning is based on dynamic environment modeling, and the path is dynamically adjusted in combination with real-time positioning technology. The obstacle position is continuously updated to optimize the path execution accuracy. The cargo space allocation is optimized by a dynamic programming algorithm, and the storage location is dynamically adjusted in combination with the cargo attributes and real-time inventory status. Equipment status monitoring relies on multi-sensor data, monitors equipment operating parameters in real time and generates a health score, identifies potential faults in advance and triggers early warnings. Task adjustment is based on priority evaluation and dynamic allocation strategies. When the equipment is abnormal, it can quickly switch to backup equipment and adjust the task execution order. The experimental results are shown in Table 1.

[0110] Table 1 Comparison of experimental results

[0111] index Traditional methods Method of the present invention Task allocation time (seconds) 15 8 Path planning error (meters) 1.5 0.5 Cargo storage and retrieval efficiency (pieces / hour) 120 180 Equipment failure rate (times / 100 hours) 5 1 Task adjustment response time (seconds) 20 5

[0112] The present invention adopts dynamic device status monitoring and priority sorting technology in task allocation, which solves the problem of task allocation delay caused by fixed rules in traditional methods. By evaluating the operating load and health status of the equipment in real time, the present invention can quickly identify the equipment suitable for executing the task and adjust the allocation strategy in real time when the task changes. Compared with the fixed queue allocation of the traditional method, the present invention achieves higher allocation efficiency and lower allocation failure rate.

[0113] In terms of path planning, the present invention uses dynamic environment modeling technology to adjust path planning in real time according to dynamic changes in the storage area (moving obstacles or other equipment position changes). Compared with traditional static path planning methods, the present invention significantly improves the execution accuracy of the path in complex dynamic environments and effectively avoids task interruptions caused by environmental changes. This improvement is due to the introduction of dynamic programming algorithms and the application of real-time positioning technology, which enables the equipment to adjust its operating trajectory according to the actual environment.

[0114] The present invention combines the cargo attributes and real-time inventory status, and uses a dynamic programming algorithm to adjust the cargo space, thereby achieving balanced utilization of resources. Compared with the traditional cargo space allocation method based on fixed rules, the present invention effectively avoids the situation where some cargo spaces are overused or idle, and significantly improves the overall efficiency of cargo storage and retrieval.

[0115] The present invention continuously monitors the operating status of the equipment and combines it with a preset threshold judgment technology to identify potential equipment anomalies in advance and trigger early warnings. Compared with the traditional method that relies on post-fault processing, the present invention significantly reduces the downtime and damage caused by abnormal operation of the equipment, thereby improving the reliability of equipment operation.

[0116] In terms of task adjustment, the present invention can quickly respond to emergencies, reallocate tasks or adjust the order of tasks through task priority evaluation and dynamic allocation strategies. Compared with the traditional adjustment method that relies on manual intervention, the automatic adjustment logic of the present invention significantly improves the system's response efficiency in abnormal situations and ensures the system stability in a multi-task environment.

[0117] The present invention significantly improves the operating efficiency and reliability of intelligent vertical warehouses in dynamic scenarios through innovative dynamic equipment allocation, path optimization, cargo location management and task adjustment strategies. The improvement of the technical solution enables the present invention to better adapt to complex environmental changes in practical applications, showing stronger adaptability and execution capabilities than traditional methods, and providing important support for the technological upgrading of the intelligent warehousing field.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent vertical library storage management method, characterized in that: include: Receive task instructions from the upper system through the communication network, and assign specific execution equipment according to the equipment operation status and real-time inventory data; Calculate the logistics path based on equipment location information, warehouse environment data and task requirements, and generate control instructions to drive the execution equipment to perform operations according to the path; According to the cargo attributes and storage rules, the dynamic programming algorithm is used to optimize the cargo space allocation and complete the cargo storage and retrieval operations; The operating status of the execution equipment is collected in real time through sensors, anomalies are identified based on the collected data, and alarm signals are triggered through the upper system, while the task execution strategy is adjusted.

2. The intelligent vertical library storage management method according to claim 1, characterized in that: The allocation of specific execution devices includes the upper system sending the task instructions to the local management platform through the wireless communication protocol, and the local management platform parsing the task instructions and scheduling; The local management platform analyzes the equipment load status based on the real-time equipment operation status data and inventory data, and selects equipment to perform tasks; If the initially allocated device is overloaded or fails, the local management platform recalculates and selects other idle devices based on the real-time status of the device.

3. The intelligent vertical library storage management method according to claim 2, characterized in that: The driving execution device to execute the operation according to the path includes the upper system obtaining the current position of the execution device through real-time positioning technology, and building a reservoir area environment model in combination with the reservoir area environment data; The upper system uses the path planning algorithm to calculate all candidate paths and select the optimal path based on task requirements; The path planning results are transmitted to the execution device control system through control instructions. The execution device performs operations according to the generated path. If the path deviation occurs during the execution process, the execution device calculates the new optimal path and adjusts the operation trajectory.

4. The intelligent vertical library storage management method according to claim 3, characterized in that: The completion of the storage and retrieval operation of the goods includes the upper system constructing a dynamic programming model according to the attributes and storage rules of the goods; The upper system uses dynamic programming algorithms to calculate the optimal storage location for goods and optimizes the allocation of cargo space based on the calculation results; The calculation results generate storage and retrieval tasks, and the control instructions are sent to the execution equipment through the control platform to guide the execution equipment to complete the goods storage and retrieval operations and update the inventory information in real time.

5. The intelligent vertical library storage management method according to claim 4, characterized in that: The collecting of the operating status of the execution device comprises installing a plurality of sensors in the execution device for collecting the working status data of the execution device; During operation, the execution equipment uses sensors to monitor and record parameters in real time, and the data is transmitted to the local management platform via wired or wireless communication networks; The local management platform compares the collected data with the preset working parameters of the equipment to determine whether there are any abnormalities in the equipment and generates an operation report for further operation.

6. The intelligent vertical library storage management method according to claim 5, characterized in that: The triggering of the alarm signal by the upper system includes the upper system performing real-time analysis on the running status data of the executing device and judging whether the device exceeds the normal working range in combination with the preset device working threshold; When sensor data indicates that the equipment is abnormal, the upper system triggers the alarm mechanism, marks the abnormal equipment and notifies the operator; The upper system automatically adjusts the task execution strategy according to the preset processing strategy, transfers the task to other available devices, or adjusts the task execution order according to the device status and modifies the task execution plan.

7. The intelligent vertical library storage management method according to claim 6, characterized in that: The adjustment task execution strategy includes, after the device abnormality is identified, the upper system determines whether the current task can continue to be executed by evaluating the task priority; If the device abnormality affects the execution of the current task, the upper system adjusts the task execution strategy according to the task urgency, device status and available resources, and transfers the task to other devices or rearranges the time and order of task execution; The adjusted task information is synchronized to all relevant execution devices through the control platform. The execution devices continue to work according to the new task arrangement and update the task execution progress in real time.

8. A system using the intelligent vertical library storage management method according to any one of claims 1 to 7, characterized in that: include: Equipment allocation module (100), route planning module (200), cargo storage and retrieval module (300) and dispatch warning module (400); The equipment allocation module (100) is used to receive task instructions from the upper system through a communication network, and allocate specific execution equipment according to the equipment operation status and real-time inventory data; The path planning module (200) is used to calculate the logistics path based on the equipment location information, the warehouse environment data and the task requirements, and generate control instructions to drive the execution equipment to perform operations according to the path; The cargo storage and retrieval module (300) is used to optimize cargo location allocation and complete cargo storage and retrieval operations based on cargo attributes and storage rules using a dynamic programming algorithm; The scheduling warning module (400) is used to collect the operating status of the execution equipment in real time through sensors, identify abnormalities based on the collected data, trigger an alarm signal through the upper system, and adjust the task execution strategy at the same time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent vertical library storage management method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the intelligent vertical library storage management method described in any one of claims 1 to 7 are implemented.

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