An intelligent warehousing operation assistance system and method based on mixed reality

Abstract

The present invention discloses an intelligent warehousing operation assistance system and method based on mixed reality, which relates to the technical field of warehousing management and includes: a storage stack point pre-screening module, an intelligent task planning module, a task execution monitoring module, an abnormal task execution adjustment module, and an optimal handling equipment screening module. The present invention can screen the optimal storage stack points, improve the utilization rate of warehousing space, avoid goods extrusion or space waste caused by space mismatch, improve the transportation efficiency, reduce the energy consumption, can timely detect abnormal conditions, and ensure the reliability of task execution. When an abnormality occurs, it can quickly trigger an alarm and reasonably determine the replacement mode of the handling equipment, ensure the continuity and high efficiency of warehousing operations, and improve the overall operation efficiency, goods storage quality and scientific nature of management decisions of the intelligent warehousing system.

Description

Technical Field

[0001] The present invention relates to the field of warehouse management technology, and in particular to an intelligent warehouse operation assistance system and method based on mixed reality. Background Art

[0002] With the rapid development of the logistics industry and the explosive growth of e-commerce business, the importance of warehouse management in the supply chain system has become increasingly prominent. Traditional warehouse operation methods have problems such as low efficiency, high error rate, and long personnel training cycle. Although the introduction of automated equipment and information systems has improved warehouse efficiency to a certain extent, a large amount of manual operation is still required in the complex and changeable warehouse environment. Therefore, the combination of mixed reality technology and warehouse management, using cutting-edge technologies such as head-mounted display, spatial positioning, and environmental perception, provides operators with intuitive, three-dimensional, and highly interactive operation guidance to improve warehouse efficiency.

[0003] The prior art, such as the invention patent with announcement number: CN112938292B, is an intelligent warehousing device, system and auxiliary decision-making method, including a moving mechanism, a grabbing mechanism, a cargo platform, multiple storage racks, a control mechanism and a positioning mechanism. The equipment is moved in three directions: horizontally left and right, horizontally front and back, and vertically up and down through a highly integrated warehousing system, so as to realize the storage and retrieval operation of the equipment at different cargo positions, transport the required material boxes to the storage position, automatically identify whether to put them into the equipment after putting them into the equipment, and automatically transport them to the storage position after successful placement; the outbound delivery can be screened by the stored model, manufacturer, inspection time, inspection batch, inspection results, etc., and the outbound delivery is automatic after selection, and an auxiliary decision-making system is developed to mine the warehousing data to realize decision-making functions such as preparing procurement plans, checking equipment hidden dangers, and assessing the timeliness of inspection.

[0004] The prior art, such as the invention patent with announcement number: CN112396364B, is a warehouse data push and monitoring method, device, server and storage medium. The warehouse data monitoring method includes: receiving first warehouse data sent by warehouse servers of multiple warehouse systems respectively, the first warehouse data including: an information set of intelligent handling equipment collected in real time from the warehouse system and an identification code of the warehouse system; according to the identification code, determining whether the warehouse information corresponding to the warehouse system has been stored in the cache data; when the warehouse information has been stored in the cache data, storing the information set of intelligent handling equipment collected in real time from the warehouse system in the cache data; and when the warehouse information is not stored in the cache data, returning response information marked as a first operating state to the warehouse system; wherein the first operating state is used to indicate that the warehouse information of the warehouse system is not stored.

[0005] From the above solutions, it can be seen that the current warehouse management system has certain limitations in terms of intelligence and comprehensive management efficiency. It often focuses on specific links or single function realization in the warehousing process. However, in actual warehousing operation scenarios, warehouse management faces many complex and interrelated challenges. The efficiency and rationality of warehouse management not only depends on the utilization of cargo space, but is also closely related to multiple factors such as the environmental conditions of the stacking point and the adaptability of the handling equipment. Therefore, the existing technology based on specific links and single function realization is difficult to achieve the optimal balance between ensuring the continuity and efficiency of operations, which may increase the risk of cargo damage, reduce the efficiency of warehousing operations, and limit the improvement of the overall performance of the warehousing system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an intelligent warehousing operation assistance system and method based on mixed reality, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: In a first aspect, the present invention provides an intelligent warehouse operation assistance system based on mixed reality, comprising:

[0008] The storage stack point pre-screening module is used to obtain the status characteristic information of each idle stack point and the warehouse goods information for processing, and pre-screen each target storage stack point.

[0009] The intelligent task planning module is used to upload each target storage stack point to the cloud processing center and use mixed reality devices to request confirmation operations, thereby determining the optimal storage stack point, synchronously obtaining the initial stack point of the stored goods, and processing to obtain the optimal execution path of the warehousing task.

[0010] The task execution monitoring module is used to decompose the optimal execution path of the warehousing task to obtain each execution branch path, monitor the execution data of the execution branch path task, analyze the execution index of the execution branch path task, and synchronously determine the task execution status label, which includes normal task execution and abnormal task execution.

[0011] The abnormal task execution adjustment module is used to trigger the early warning mechanism and display the early warning information through the mixed reality device when the task execution status label is judged as abnormal task execution, and simultaneously combine the execution branch path task execution indicators to determine the handling equipment replacement mode.

[0012] The optimal handling equipment screening module is used to obtain the characteristic data of each idle handling equipment, process and screen the optimal handling equipment, control the optimal handling equipment to perform auxiliary handover of warehousing and handling tasks, and continue to complete warehousing and handling tasks.

[0013] A second aspect of the present invention provides an intelligent warehousing operation assistance method based on mixed reality, comprising the following steps:

[0014] S1, obtain the status characteristic information of each idle stack point and the storage goods information for processing, and pre-screen to obtain each target storage stack point.

[0015] S2, upload the target storage stack points of the warehouse goods to the cloud processing center, and use the mixed reality device to request confirmation operations, thereby determining the optimal storage stack point, synchronously obtaining the initial stack point of the warehouse goods, and processing to obtain the optimal execution path of the warehousing task.

[0016] S3, decompose the optimal execution path of the warehousing task to obtain each execution branch path, monitor the execution data of the execution branch path task, analyze the execution index of the execution branch path task, and synchronously determine the task execution status label, which includes normal task execution and abnormal task execution.

[0017] S4, when the task execution status label of the optimal execution sub-path of the warehousing task is determined to be an abnormal task execution, the warning mechanism is triggered and the warning information is displayed through the mixed reality device. Combined with the execution index of the branch path task, the handling equipment replacement mode is determined.

[0018] S5, acquiring characteristic data of each idle transport equipment, screening the optimal transport equipment based on the cargo data of the current transport equipment, and controlling the optimal transport equipment to continue the transport task according to the transport equipment replacement mode.

[0019] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0020] (1) The present invention provides an intelligent warehousing operation assistance system and method based on mixed reality to screen the optimal storage stacking point, improve the utilization rate of storage space, and avoid cargo squeezing or space waste caused by space mismatch. Determining the optimal storage stacking point and execution path improves transportation efficiency and reduces energy consumption. At the same time, through the interactive function of the mixed reality device, operators can participate in decision-making more intuitively and conveniently, thereby enhancing the human-computer collaboration effect. Through comprehensive monitoring and analysis of task execution data, abnormal conditions can be discovered in a timely manner to ensure the reliability of task execution. When an abnormality occurs, it can quickly trigger an early warning and reasonably determine the replacement mode of the handling equipment to ensure the continuity and efficiency of warehousing operations, thereby improving the overall operational efficiency of the intelligent warehousing system, the quality of cargo storage, and the scientific nature of management decisions.

[0021] (2) The present invention obtains the optimal execution path of the warehousing task through processing. The selected path can minimize the travel time and energy consumption of the handling equipment, improve the smoothness of transportation, and enable the goods to reach the target storage stack point quickly and accurately, which helps to further ensure the smooth execution of the warehousing task and improve the intelligence level of the entire warehousing system.

[0022] (3) The present invention helps to realize real-time monitoring and management of warehousing and handling tasks by determining the task execution status label. For normal task execution status, it can ensure that warehousing operations proceed in an orderly manner as planned, maintain an efficient and stable operation rhythm, ensure the continuity of cargo handling, and improve the overall warehousing operation efficiency. For abnormal task execution status, it can reduce the risk of cargo delays and damage caused by equipment failure or abnormal operation, ensure cargo safety, and provide a numerical basis for subsequent targeted adjustment measures.

[0023] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0025] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] See also Figure 1 As shown, an embodiment of the present invention provides an intelligent warehousing operation assistance system based on mixed reality, including:

[0028] The storage stack point pre-screening module is used to obtain the status characteristic information of each idle stack point and the warehouse goods information for processing, and pre-screen each target storage stack point.

[0029] In this embodiment, each target storage stack point is obtained by pre-screening, and the specific acquisition method is as follows:

[0030] The state characteristic information of each idle stack point includes basic data of each idle stack point and environmental data of each idle stack point.

[0031] The basic data of each idle stack point includes the available height, available space volume, maximum load-bearing capacity, and used load-bearing capacity of each idle stack point.

[0032] The environmental data of each idle stack point includes the current temperature, current humidity, current light intensity, adjacent maintenance interval and historical maintenance frequency of each idle stack point.

[0033] It should be noted that the available height, available space volume, maximum load-bearing capacity, adjacent maintenance interval and historical maintenance frequency of each idle stack point are collected from the warehouse management system background, and the used load-bearing capacity is collected through pressure sensors installed at each idle stack point.

[0034] It should also be noted that the current temperature, current humidity, and current light intensity of each idle stack point are collected by a temperature sensor, a humidity sensor, and a light intensity sensor, respectively.

[0035] Warehouse cargo information includes the height, volume and weight of the cargo to be transported.

[0036] It should be noted that the warehouse cargo information can be collected through volume measurement equipment. In a specific embodiment, it can be collected through the Sucetong KS02-M, which integrates an electronic weighing sensor, an industrial code reading camera and a volume measurement module. It can quickly and accurately obtain the length, width and height data of the cargo, and record the weight of the cargo.

[0037] According to the status characteristic information of each idle stack point and the information of stored goods, the storage goods adaptation index of each idle stack point is obtained through analysis and processing.

[0038] The storage cargo adaptation index of each idle storage point is used to characterize the degree of adaptation between the storage cargo and each idle storage point.

[0039] In a specific embodiment, the storage goods adaptation index of each idle stack point is obtained in the following manner:

[0040] Extract the ideal environment data stored in the database, which includes the ideal temperature, ideal humidity, ideal light intensity, reference adjacent maintenance interval duration, and reference historical maintenance frequency of the stack point.

[0041] Analyze the storage cargo adaptation indicators of each idle stack point. The specific method is as follows:

[0042] ,

[0043] in, is the storage cargo adaptation index of the i-th idle stack point, is the available height of the i-th free stack point, is the available space volume of the i-th free stack point, is the maximum load-bearing capacity of the ith free stack point, is the height of the cargo to be transported, is the volume of the goods to be transported, is the weight of the cargo to be transported, is the current temperature of the i-th free stack point, is the current humidity of the i-th free stack point, is the current light intensity of the i-th idle stack point, is the maintenance interval of the i-th idle stack point, is the historical maintenance frequency of the i-th idle stack point, is the ideal temperature of the stack point, The ideal humidity for the stack point, is the ideal light intensity at the stack point, is the reference neighboring maintenance interval of the stack point, is the reference history maintenance frequency of the stack point, i is the number of each idle stack point, , m is the number of free stack points, and e is a natural constant.

[0044] It should be noted that the maximum load-bearing capacity of each idle stack point is obtained by subtracting the used load-bearing capacity of each idle stack point from the maximum load-bearing capacity of each idle stack point.

[0045] It is important to understand that the softplus function is a built-in function in Python. .

[0046] It should also be noted that the storage cargo adaptation index of each idle stacking point is obtained by analyzing and processing the state characteristic information of each idle stacking point and the storage cargo information, taking into account the mutual influence between these parameters. For example, if the height of the cargo is close to or exceeds the available height of the idle stacking point, the cargo may not be fully accommodated during storage, and the top may be squeezed by the top structure of the stacking point, causing the cargo to be deformed and damaged. When the cargo volume is too large and the available space volume of the idle stacking point is limited, the cargo may not be smoothly placed in the stacking point. The weight of the cargo must be less than the remaining value after deducting the used load from the maximum load of the idle stacking point, which is the key to ensuring the safety of the stacking point structure. If the cargo is overweight, the stacking point will be subjected to excessive pressure, which may cause the supporting structure of the stacking point to deform and damage, shortening the service life of the stacking point. When the temperature rises, the saturated water vapor pressure of the air increases. When the water supply is sufficient, the air can accommodate more water vapor, and the humidity may be relatively reduced. For example, in the hot summer, the temperature in the warehouse is high. If the ventilation is good and there is no water supplement, the humidity will be relatively low. Strong light directly brings heat to the warehouse, causing the temperature to rise. Humidity has a certain influence on the propagation of light intensity. In a high humidity environment, water vapor molecules in the air will scatter and absorb light, causing the light intensity to weaken during the transmission process. High temperature and high humidity environments can make the circuit boards of electronic devices (such as temperature sensors) more susceptible to corrosion and short circuits, reducing the life of the equipment and may require more frequent maintenance, shortening the interval between adjacent maintenance and increasing the frequency of historical maintenance.

[0047] Extract the preset warehouse goods adaptation verification threshold in the database.

[0048] It should be noted that the warehouse goods adaptation verification threshold is pre-set in the database and is a critical indicator used to measure whether the degree of adaptation between the warehouse goods and the idle stack points reaches an acceptable standard.

[0049] In a specific embodiment, there are multiple ways to set the storage cargo adaptation verification threshold. First, widely collect historical data of different types of cargo stored in different storage environments, conduct in-depth analysis of these rich and diverse data, and calculate the reasonable range boundary values ​​corresponding to various adaptation indicators of cargo and stacking points under the conditions of ensuring safe storage of cargo, unaffected quality and achieving high storage efficiency through a large number of simulation experiments and actual case analysis. These boundary values ​​are comprehensively analyzed, such as mean processing, to finally determine the storage cargo adaptation verification threshold. The threshold set in this way can provide a scientific and reasonable decision-making basis for warehousing operations and ensure the safety, efficiency and stability of cargo storage.

[0050] If the storage cargo adaptation index of a certain idle stack point is greater than the storage cargo adaptation verification threshold, the idle stack point is extracted and recorded as the target storage stack point, and statistics are counted for each target storage stack point.

[0051] It should be noted that if the storage cargo adaptation index of the idle stack is greater than the storage cargo adaptation verification threshold, it means that the idle stack has reached a high level of adaptation to the cargo to be transported in terms of space availability, load-bearing capacity, and environmental conditions, and can provide relatively ideal storage conditions for the cargo. This means that when the cargo is stored at the stack, its height can be reasonably placed within the available height range of the stack, and its volume will not exceed the available space volume of the stack, and its weight will not exceed the remaining carrying capacity of the stack. At the same time, the current environmental factors such as temperature, humidity, and light intensity of the stack are relatively close to the ideal environmental conditions required by the cargo, and the stack also has good stability in terms of maintenance status, which can effectively reduce the risks in the cargo storage process, such as extrusion deformation due to insufficient space, damage to the stack caused by overweight, and deterioration of cargo caused by environmental discomfort, which helps to ensure the integrity and quality of the cargo and improve the safety and efficiency of warehousing operations. Therefore, it is extracted and recorded as the target storage stack as one of the preferred choices for cargo storage.

[0052] The intelligent task planning module is used to upload each target storage stack point to the cloud processing center and use mixed reality devices to request confirmation operations, thereby determining the optimal storage stack point, synchronously obtaining the initial stack point of the stored goods, and processing to obtain the optimal execution path of the warehousing task.

[0053] It should be noted that the cloud processing center is used to upload warehouse information, realize centralized management of warehouse data, efficient calculation, and real-time sharing and collaborative processing of information.

[0054] Mixed reality equipment refers to advanced technical equipment that can deeply integrate virtual information with real-world scenes and realize natural human-computer interaction. Through precise spatial calculation and image recognition technology, virtual content can be accurately superimposed on the real world. For example, in the intelligent warehousing scene, the mixed reality headset worn by the operator can display the real layout of the warehouse in real time, and highlight virtual guidance information such as the target storage stack point and the optimal transportation path in the field of view, so that the operator can intuitively obtain key information of the task and improve the accuracy and efficiency of the operation. At the same time, the mixed reality device is equipped with a variety of interaction methods, such as voice recognition, gesture control, eye tracking, etc. Operators can query cargo information and request equipment assistance through natural voice commands, or use gestures to operate the virtual interface to confirm task steps, adjust transportation plans, etc., to achieve convenient interaction with virtual information and intelligent warehousing systems, greatly improving the intelligent experience and work efficiency of warehousing operations, breaking the limitations of traditional human-computer interaction, and providing strong technical support for the efficient operation of intelligent warehousing.

[0055] In a specific embodiment, the optimal storage stack point is determined, and the specific process is as follows:

[0056] Each target storage stack point is displayed through a mixed reality device, and a confirmation operation is requested. If no confirmation operation is detected, the target storage stack point corresponding to the maximum value of the warehouse goods adaptation index is recorded as the optimal storage stack point. If a confirmation operation is detected, the target storage stack point corresponding to the confirmation operation is recorded as the optimal storage stack point.

[0057] It should be noted that the display arrangement order of each target storage stack point is determined by its corresponding warehouse goods adaptation index. The larger the warehouse goods adaptation index, the higher the display arrangement order of the corresponding target storage stack point. For example, when the warehouse goods adaptation index of a target storage stack point is the largest, the display arrangement order of this target storage stack point is the first.

[0058] In this embodiment, the optimal execution path of the storage task is obtained by processing, and the specific process is as follows:

[0059] Obtain the transportable paths between the initial stacking point and the optimal storage stacking point of the stored goods, record them as executable paths, and collect the path state parameters of each executable path.

[0060] The path state parameters of each executable path include path length, number of path curves, path road surface smoothness, and number of path obstacles.

[0061] It should be noted that the specific method for obtaining the path status parameters of each executable path is as follows:

[0062] Obtain warehouse topographic survey maps and input them into the cloud processing center to locate the transportable paths between the initial storage point and the optimal storage point of the warehouse goods, and extract the path length and number of path bends of each executable path;

[0063] The path pavement smoothness can be obtained by using a laser smoothness meter.

[0064] The number of path obstacles can be collected by warehouse cameras.

[0065] Based on the path state parameters of each executable path, the transportation priority value of each executable path is obtained through analysis and processing, and the transportation priority value of each executable path is used to characterize the transportation priority degree of each executable path.

[0066] In a specific embodiment, the transport priority value of each executable path is obtained in the following manner:

[0067] ,

[0068] in, is the transport priority value of the jth executable path, is the path length of the jth executable path, is the number of path turns of the jth executable path, is the path surface smoothness of the jth executable path, is the number of path obstacles of the jth executable path, is the correction factor corresponding to the unit path length, is the correction factor corresponding to the number of curves per unit path, is the correction factor corresponding to the unit path road surface flatness, is the correction factor corresponding to the number of obstacles per unit path, j is the number of each executable path, , n is the number of executable paths, and e is a natural constant.

[0069] It should be noted that the correction factor corresponding to the unit path length, the correction factor corresponding to the unit path curve number, the correction factor corresponding to the unit path road surface smoothness, and the correction factor corresponding to the unit path obstacle number respectively represent the numerical values ​​of the influence of the path length, the number of path curves, the path road surface smoothness, and the number of path obstacles on the transportation priority value of the executable path. When used, the preset values ​​can be directly extracted from the database. The database stores a path state parameter extraction mapping set. The mapping set forms a mapping relationship between the correction factor corresponding to the unit path length, the correction factor corresponding to the unit path curve number, the correction factor corresponding to the unit path road surface smoothness, and the correction factor corresponding to the unit path obstacle number and the path length, the number of path curves, the path road surface smoothness, and the number of path obstacles. When used, the real-time path length, the number of path curves, the path road surface smoothness, and the number of path obstacles are input into the mapping set, so as to extract the correction factor corresponding to the unit path length, the correction factor corresponding to the unit path curve number, the correction factor corresponding to the unit path road surface smoothness, and the correction factor corresponding to the unit path obstacle number.

[0070] It should also be noted that the transport priority value of each executable path is obtained by analyzing and processing the path state parameters of each executable path, taking into account the interrelationship between these parameters. For example, a longer path may have more opportunities for turning. Because in a large warehouse environment, a longer path is more likely to pass through different areas and shelf layouts, thereby encountering more channel turns and direction changes. Longer paths are more susceptible to the ground conditions in different areas, and the probability of uneven road surfaces is relatively high. Long paths cover a wide area and pass through more work areas and storage areas, so they are more likely to encounter obstacles. At turns, the road surface is more prone to wear and deformation, resulting in a decrease in flatness. Uneven roads may cause goods to shift or scatter during transportation, and these scattered goods themselves become obstacles on the path.

[0071] The preset transport priority threshold in the database is extracted, and each executable path corresponding to a transport priority value greater than the transport priority threshold is extracted and recorded as each target execution path.

[0072] It should be noted that the transport priority threshold is a critical indicator pre-set in the database and used to measure whether the transport priority of each executable path is sufficient to meet the warehousing and transportation needs.

[0073] In a specific embodiment, the setting method of the transport priority threshold involves many aspects of consideration and analysis. First, the historical transportation data of each executable path under different warehouse layouts, cargo flow, equipment performance, and working time periods are comprehensively collected, covering data information on path travel time, energy consumption, equipment loss, and cargo transportation success rate. In-depth and meticulous research is carried out on these complex data. Through a large number of simulated transportation experiments based on actual scenarios and a review and analysis of many actual transportation cases in the past, the reasonable range boundary values ​​of the transportation priority values ​​corresponding to each executable path based on its various state parameters are calculated to ensure smooth warehousing operations, timely and accurate distribution of goods, and reasonable use of equipment. These boundary values ​​are comprehensively analyzed using methods such as weighted average and principal component analysis, fully considering the weight of the influence of different factors on the degree of transportation priority, and finally determining the transportation priority threshold. The threshold set in this way can provide accurate and effective decision-making references for warehousing task planning, ensure the efficiency, economy and reliability of warehousing and transportation operations, and enable goods to be transported quickly, safely and at low cost under the optimal path planning.

[0074] When the transport priority value is greater than the transport priority threshold, it means that the executable path has reached a high level in terms of overall transport conditions and performance. It may have a relatively short path length, which means that the handling equipment can spend less time to complete the transportation task when driving on the path, thereby improving the turnover efficiency of the goods. The path has a good surface flatness, which can reduce the bumps and vibrations of the handling equipment during driving, reduce the equipment failure rate, and ensure the stability of the goods, avoiding damage to the goods due to uneven road surface. The number of turns is small, which can keep the handling equipment in a relatively smooth driving state, reduce the speed loss and energy consumption caused by frequent turning, and improve the timeliness of transportation. The number of obstacles on the path is small, which reduces the risk of collision of the handling equipment, reduces the additional operations and time delays caused by avoiding obstacles, and helps the handling equipment pass through the path more quickly and safely. In general, such a path can better meet the requirements of warehousing operations for efficiency, safety and low loss, so it is marked as the target execution path and is used as a priority to perform warehousing and handling tasks to improve the operational efficiency of the entire warehousing system.

[0075] Upload each target execution path to the cloud processing center, use mixed reality devices to request confirmation operations, and determine the optimal execution path for the warehousing task based on the confirmation operation results.

[0076] In a specific embodiment, the optimal execution path of the storage task is determined, and the specific process is as follows:

[0077] Each target execution path is displayed through a mixed reality device, and a confirmation operation is requested. If no confirmation operation is detected, the target execution path corresponding to the maximum transport priority value is recorded as the optimal execution path of the warehousing task. If a confirmation operation is detected, the target execution path corresponding to the confirmation operation is recorded as the optimal execution path of the warehousing task.

[0078] It should be noted that the display order of each target execution path is determined by its corresponding transportation priority value. The larger the transportation priority value, the higher the display order of the corresponding target execution path. For example, when the transportation priority value of a target execution path is the largest, the display order of this target execution path is the first.

[0079] In a specific embodiment, the optimal execution path of the warehousing task is obtained through processing. The selected path can minimize the driving time and energy consumption of the handling equipment, improve the smoothness of transportation, and enable the goods to reach the target storage stack point quickly and accurately, which helps to further ensure the smooth execution of the warehousing task and improve the intelligence level of the entire warehousing system.

[0080] The task execution monitoring module is used to decompose the optimal execution path of the warehousing task to obtain each execution branch path, monitor the execution data of the execution branch path task, analyze the execution index of the execution branch path task, and synchronously determine the task execution status label, which includes normal task execution and abnormal task execution.

[0081] It should be noted that the optimal execution path of the warehousing task is decomposed to obtain each execution branch path, which can be divided according to the key operation nodes and logical processes of the handling equipment in the process of executing the task. For example, the transfer of handling equipment between different functional areas in the warehouse, the shuttle between shelves, the loading and unloading of goods, etc. are used as the main division basis. When the handling equipment starts from the initial position and enters the main channel to the cargo storage area, this stage can be divided into an execution branch path; after arriving at the cargo storage area, the process of moving between different shelf rows to find the target cargo can be regarded as another branch path; when the cargo is found and loaded, the loading operation and related preparation work stages constitute a new branch path; after loading is completed, the handling equipment carries the cargo to the target storage stack point, and the driving process through different channels and areas on the way can be divided into independent branch paths. By decomposing the optimal execution path of warehousing tasks, it is possible to more accurately monitor the task execution data of each stage, deeply analyze the execution indicators of each stage, and timely and accurately determine the task execution status label of each branch path, thereby achieving refined management and efficient monitoring of the entire warehousing task execution process, ensuring the smooth completion of the task, and also facilitating the rapid location of the problem and taking corresponding adjustment measures when abnormal situations occur.

[0082] In this embodiment, the task execution data includes real-time operation data and historical impact data, wherein the real-time operation data includes the real-time speed, real-time acceleration, real-time power fluctuation value and real-time power consumption rate of the transport equipment in the execution branch path.

[0083] The historical impact data includes the historical cumulative running time of the handling equipment in the execution branch path, the historical maintenance frequency, the total number of historical failures and the historical average trouble-free running time.

[0084] It should be noted that the speed, acceleration and power can be collected by speed sensors, acceleration sensors and power meters respectively, the power can be directly extracted from the battery management system of the handling equipment itself, and the historical impact data can be directly extracted from the database.

[0085] It should also be noted that the real-time power fluctuation value refers to the power of the transport equipment at the current moment minus the rated power of the transport equipment.

[0086] In this embodiment, the execution index of the branch path task is analyzed and executed, and the specific process is as follows:

[0087] According to the real-time operation data, the execution branch path transport state index is obtained by analyzing and processing, and the execution branch path transport state index is used to characterize the transport state of the transport equipment in the execution branch path.

[0088] In a specific embodiment, the branch path transport status indicator is executed, and the specific acquisition process is as follows:

[0089] The ideal operation data of the handling equipment stored in the database is extracted, including the ideal speed, the ideal acceleration, the reference power fluctuation value and the reference power consumption rate.

[0090] ,

[0091] in, To execute the branch path transport status indicator, The real-time speed of the handling equipment in executing the branch path. The real-time acceleration of the handling equipment in the execution branch path. It is the real-time power fluctuation value of the handling equipment in the execution branch path. The real-time power consumption rate of the transport equipment in the execution branch path. To achieve the ideal speed for handling equipment, For the ideal acceleration of handling equipment, Reference power fluctuation value for handling equipment, The reference power consumption rate of the handling equipment is is a natural constant.

[0092] It should be noted that the execution branch path handling status index is obtained based on the analysis and processing of real-time operation data, taking into account the mutual influence between these parameters. For example, when the handling equipment needs to accelerate, the acceleration is positive and the speed will gradually increase; when it needs to decelerate, the acceleration is negative and the speed will decrease. When the handling equipment accelerates, greater force is required. When the speed is constant, the power demand will increase, resulting in an increase in the power fluctuation value. The greater the power, the faster the power consumption. When the handling equipment accelerates or runs in a high-power state, the power demand is high and the power consumption rate will increase significantly.

[0093] According to the historical impact data, the handling capacity correction coefficient of the handling equipment is obtained by analysis and processing, and the handling capacity correction coefficient of the handling equipment is used to characterize the impact degree of the historical use status of the handling equipment.

[0094] In this embodiment, the handling capacity correction coefficient of the handling equipment is obtained through analysis and processing. The specific analysis process is as follows:

[0095] The reference historical impact data stored in the database is extracted, including the reference historical cumulative operating time of the handling equipment, the reference historical maintenance frequency, the reference historical total number of faults, and the reference historical average trouble-free operating time.

[0096] According to the historical impact data and the reference historical impact data, the historical impact characteristic index of the handling equipment is obtained through analysis and processing, and the historical impact characteristic index of the handling equipment is used to characterize the degree of influence of the historical state of the handling equipment on the handling capacity.

[0097] In a specific embodiment, the handling equipment history impact characteristic index, the specific acquisition process is as follows:

[0098] ,

[0099] in, To handle the historical impact characteristic indicators of equipment, It is the historical accumulated running time of the handling equipment in the execution branch path. The historical maintenance frequency of the handling equipment in the execution branch path. is the total number of historical failures of the handling equipment in the execution branch path, The historical mean time between failures of the handling equipment in executing the branch path is The reference history accumulated running time of the handling equipment, Reference historical maintenance frequency for handling equipment, The total number of reference historical failures of the handling equipment. is the reference historical mean trouble-free operation time of the handling equipment, and e is a natural constant.

[0100] It should be noted that Functions and Function is a built-in function in Python. .

[0101] It should also be noted that the historical impact characteristic indicators of handling equipment obtained by analyzing and processing historical impact data take into account the mutual influence between these parameters. For example, the longer the historical cumulative operating time, the higher the degree of wear of the equipment parts, which will increase the probability of equipment failure, thereby increasing the total number of historical failures. The level of historical maintenance frequency will have an impact on the historical cumulative operating time and the total number of historical failures. If the maintenance frequency is reasonable and the maintenance quality is high, and potential problems of the equipment can be discovered and handled in a timely manner, then the historical cumulative operating time of the equipment can be effectively extended and the number of failures can be reduced. The total number of historical failures will also affect the historical cumulative operating time and historical maintenance frequency. More failures will increase the equipment downtime for maintenance, thereby reducing the actual historical cumulative operating time. Long historical cumulative operating time, low historical maintenance frequency, and a large total number of historical failures often lead to a shortened historical average trouble-free operating time.

[0102] The handling capacity correction coefficient corresponding to each historical influencing characteristic index interval preset in the database is extracted, and the handling capacity correction coefficient corresponding to the interval where the historical influencing characteristic index of the handling equipment is located is mapped and extracted, and recorded as the handling capacity correction coefficient of the handling equipment.

[0103] Based on the branch path transport state index and the transport capacity correction coefficient of the transport equipment, the branch path task execution index is analyzed and processed to obtain the branch path task execution index, which is used to characterize the corrected transport state of the transport equipment on the branch path.

[0104] It should be understood that the reason why the handling capacity correction coefficient of the handling equipment is obtained through the handling equipment historical impact characteristic index is that the handling equipment historical impact characteristic index comprehensively reflects the past operating conditions of the equipment. These factors are interrelated and jointly affect the current actual handling capacity of the equipment. By executing the branch path handling status index and the handling equipment handling capacity correction coefficient, the branch path task execution index is obtained through comprehensive analysis and processing. It can more accurately reflect the actual ability of the equipment when executing tasks on the current path. It not only takes into account the current instantaneous status of the equipment, but also combines the impact of historical factors on equipment performance. It can more comprehensively and accurately evaluate the handling status of the equipment on the branch path, which provides a more reliable basis for timely discovering potential problems, reasonably arranging tasks, optimizing handling strategies, and ensuring the efficiency and stability of the entire warehousing operation.

[0105] In a specific embodiment, the branch path task execution index is obtained by the following method:

[0106] ,

[0107] in, To execute branch path task execution indicators, To execute the branch path transport status indicator, is the correction coefficient of the handling capacity of the handling equipment, and e is a natural constant.

[0108] It is important to understand that the softsign function is a built-in function in Python. .

[0109] In this embodiment, the task execution status label is determined, and the specific process is as follows:

[0110] Extract the preset task execution indicator thresholds in the database.

[0111] It should be noted that the task execution index threshold is pre-set in the database and is used to measure the judgment limit of whether the handling equipment is abnormal in the execution branch path of the handling equipment.

[0112] In a specific embodiment, the setting method of the task execution index threshold involves many considerations and rigorous analysis processes. First, the historical task execution data of different handling equipment running on different execution branch paths in various warehousing task scenarios are comprehensively collected, and the task execution index is obtained by comprehensive calculation. Through a large number of simulated task execution analyses based on actual cases and a review and analysis of the task execution status in many actual warehousing operations in the past, the reasonable range boundary values ​​of the normal and abnormal states corresponding to the task execution index are calculated while ensuring the stable operation of the handling equipment, the efficient completion of the task and the reasonable range of equipment loss. These boundary values ​​are comprehensively analyzed using methods such as weighted summation and statistical interval estimation, and the weights of the influence of different factors on the judgment of the task execution status are fully considered to finally determine the task execution index threshold. The threshold set in this way can provide accurate and effective decision-making references for status monitoring and abnormal diagnosis during the execution of warehousing tasks, ensure the reliability, efficiency and long-term stable operation of warehousing and handling operations, and can promptly discover and handle abnormal situations during task execution, and optimize the overall operation process.

[0113] If the task execution index of the execution branch path is greater than or equal to the task execution index threshold, the task execution status label of the current branch path is marked as abnormal task execution. If the task execution index of the execution branch path is less than the task execution index threshold, the task execution status label of the current branch path is marked as normal task execution.

[0114] If the execution index of the branch path task is greater than or equal to the task execution index threshold, it means that the actual operating status of the handling equipment on the current branch path deviates from the normal expected range, and there may be multiple adverse situations that need to be dealt with in time to avoid more serious consequences. If the execution index of the branch path task is less than the task execution index threshold, it means that the operating status of the handling equipment on the current branch path is basically in line with expectations, and various operating parameters and historical influencing factors are within a reasonable range. The handling operation can continue according to the predetermined plan.

[0115] In a specific embodiment, by determining the task execution status label, it is helpful to achieve real-time monitoring and management of warehousing and handling tasks. For normal task execution status, it can ensure that warehousing operations proceed in an orderly manner as planned, maintain an efficient and stable operating rhythm, ensure the continuity of cargo handling, and improve the overall warehousing operation efficiency. For abnormal task execution status, it can reduce the risk of cargo delays and damage caused by equipment failure or abnormal operation, ensure cargo safety, and provide a numerical basis for subsequent targeted adjustment measures.

[0116] The abnormal task execution adjustment module is used to trigger the early warning mechanism and display the early warning information through the mixed reality device when the task execution status label is judged as abnormal task execution, and simultaneously combine the execution branch path task execution indicators to determine the handling equipment replacement mode.

[0117] In a specific embodiment, the warning information may be detailed prompt content presented to the operator in a visual manner through a mixed reality device, such as "an abnormality occurred in the transport equipment when executing a branch path, please replace the transport equipment, etc."

[0118] In this embodiment, the handling equipment replacement mode is determined, and the specific determination process is as follows:

[0119] Get the current execution branch path task execution index, recorded as the abnormal task execution index.

[0120] The equipment replacement mode determination factor corresponding to each task execution index interval stored in the database is extracted, and the equipment replacement mode determination factor corresponding to the interval where the abnormal task execution index is located is mapped and extracted, and marked as the handling equipment replacement mode determination factor.

[0121] The remaining path state parameters of the currently executed branch path are obtained, and combined with the handling equipment replacement mode determination factor, the handling equipment replacement mode determination indicator parameters are analyzed to obtain.

[0122] In a specific embodiment, the determination indicator parameters of the handling equipment replacement mode are obtained by analysis, and the specific analysis process is as follows:

[0123] According to the remaining path state parameters of the currently executed branch path, the transportation priority value of the remaining path of the currently executed branch path is analyzed and obtained. The transportation priority value of the remaining path of the currently executed branch path is used to characterize the transportation difficulty of the remaining path of the currently executed branch path.

[0124] The remaining path state parameters of the currently executed branch path include the path length of the remaining path, the number of path curves, the path road surface flatness, and the number of path obstacles.

[0125] ,

[0126] in, is the transportation priority value of the remaining paths of the currently executed branch path. is the path length of the remaining path of the current execution branch path, is the number of path bends of the remaining path of the currently executed branch path, is the path surface smoothness of the remaining path of the currently executed branch path, is the number of path obstacles of the remaining path of the currently executed branch path, is the correction factor corresponding to the unit path length, is the correction factor corresponding to the number of curves per unit path, is the correction factor corresponding to the unit path road surface flatness, is the correction factor corresponding to the number of obstacles per unit path, and e is a natural constant.

[0127] According to the transport priority value of the remaining path of the currently executed branch path and the handling equipment replacement mode determination factor, the handling equipment replacement mode determination indicator parameter is analyzed and processed to obtain the handling equipment replacement mode determination indicator parameter, which is used to characterize the degree of failure of the handling equipment.

[0128] ,

[0129] in, To determine the indicator parameters for the replacement mode of handling equipment, is the transportation priority value of the remaining paths of the currently executed branch path. is the determination factor of the replacement mode of handling equipment, and e is a natural constant.

[0130] It should be understood that the reason why the equipment replacement mode determination factor is obtained through the abnormal task execution index is that the abnormal task execution index directly reflects the degree to which the operating state of the handling equipment deviates from the normal state when the branch path task is currently executed. The equipment replacement mode determination factor is obtained according to the abnormal degree of the actual operation of the equipment, which can more accurately determine what replacement mode the equipment should adopt under the current situation, avoiding the situation of blindly replacing equipment or continuing to use equipment with serious hidden dangers. By analyzing and processing the transportation priority value of the remaining path of the current branch path and the handling equipment replacement mode determination factor, the handling equipment replacement mode determination indicator parameter can be obtained, which can comprehensively consider the current abnormal state of the equipment and the difficulty of the subsequent path. The remaining path state parameter affects the difficulty of the equipment to complete the remaining tasks. If the equipment is currently abnormal but the remaining path is simple, it may continue to run to the next node and then replace it; if the remaining path is complex, even if the current abnormality does not seem serious, it may need to be replaced immediately. In this way, the actual situation of the equipment and the requirements of subsequent tasks can be weighed more scientifically, and the most reasonable equipment replacement decision can be made, which can not only ensure the smooth progress of the task, but also maximize the efficiency of warehousing operations and reduce delays and losses caused by equipment problems.

[0131] Extract the preset replacement mode determination indicator parameter thresholds in the database.

[0132] It should be noted that the threshold value of the indicator parameter for determining the replacement mode is pre-set in the database and is used to measure the critical indicator for determining the replacement mode of the handling equipment.

[0133] In a specific embodiment, the setting process of the threshold value of the replacement mode determination indicator parameter is rigorous and complex. First, the operation data of different handling equipment under various task scenarios and different remaining execution path conditions, as well as the corresponding handling equipment replacement related data, are widely collected, and these massive and complex data are deeply analyzed, and the reasonable range boundary values ​​corresponding to the handling equipment replacement mode determination indicator parameters are calculated while ensuring the continuity of warehousing operations, safe transportation of goods, and controllable overall operating costs. These boundary values ​​are comprehensively and comprehensively evaluated using scientific methods such as multivariate regression analysis and hierarchical analysis, and the influence weights of different factors on replacement decisions are fully considered to finally determine the threshold value of the replacement mode determination indicator parameter. The threshold value set in this way can provide an accurate and effective judgment basis for the handling equipment replacement decision, and ensure that the warehousing and handling operations can scientifically and reasonably choose the best response strategy when facing equipment abnormalities, thereby ensuring the efficiency and reliability of warehousing operations.

[0134] If the handling equipment replacement mode determination indicator parameter is greater than or equal to the replacement mode determination indicator parameter threshold, the handling equipment replacement mode is defined as the first replacement mode, and the current handling equipment handling task is stopped, and the handling equipment replacement is directly determined.

[0135] If the indicator parameter for determining the replacement mode of the handling equipment is greater than or equal to the threshold value of the indicator parameter for determining the replacement mode, it means that the abnormal condition of the current handling equipment on the execution branch path is so serious that it cannot continue to operate effectively in the current task. Continuing to use the equipment may bring extremely high risks and serious consequences. At this time, it is necessary to immediately stop the handling task of the current handling equipment and let it wait for the replacement equipment in place to avoid further expansion of potential risks and ensure the safety and continuity of the entire storage and handling operation.

[0136] If the transport equipment replacement mode determination indicator parameter is less than the replacement mode determination indicator parameter threshold, the transport equipment replacement mode is defined as the second replacement mode, and the current transport equipment is controlled to continue transporting to the next execution path initial node for transport equipment replacement.

[0137] If the indicator parameter for determining the replacement mode of the handling equipment is less than the threshold value for determining the indicator parameter for the replacement mode, it means that although the current handling equipment has an abnormality in the execution branch path, the equipment still has a certain ability to continue to operate and can complete part of the remaining tasks within a limited range and transport the goods to the initial node of the next execution path. At this time, if the equipment is stopped immediately and waits for replacement, the goods may be stranded at the current location. Allowing the equipment to continue to run for a short distance to reach a more suitable replacement node can reduce the time cost of these additional operations, and at the same time, the equipment replacement work can be carried out in a more orderly manner, making the entire storage and handling operation process smoother and reducing the impact of equipment replacement on overall operation efficiency.

[0138] The optimal handling equipment screening module is used to obtain the characteristic data of each idle handling equipment, process and screen the optimal handling equipment, control the optimal handling equipment to perform auxiliary handover of warehousing and handling tasks, and continue to complete warehousing and handling tasks.

[0139] In this embodiment, the optimal transport equipment is screened, and the specific screening process is as follows:

[0140] Obtain characteristic data of each idle handling equipment, including the remaining power, maximum load-bearing capacity, historical average trouble-free time and accumulated operating time of each idle handling equipment.

[0141] Synchronously extract the cargo weight of the current handling equipment.

[0142] According to the characteristic data of each idle transport equipment and the cargo weight of the current transport equipment, the transport matching characteristic value of each idle transport equipment is obtained through analysis and processing, and the transport matching characteristic value of each idle transport equipment is used to characterize the adaptability of each idle transport equipment in transporting cargo.

[0143] In a specific embodiment, each idle transport equipment transports matching feature values, and the specific acquisition method is as follows:

[0144] The reference handling equipment characteristic data stored in the database is obtained, including the reference remaining power of the handling equipment, the reference historical mean time between failures, and the reference accumulated operating time.

[0145] ,

[0146] in, For the Idle handling equipment handling matching feature values, For the The remaining power of an idle handling device, For the The maximum load that can be carried by an idle handling device, For the The historical MTBF of idle handling equipment, For the The accumulated running time of idle handling equipment, The reference remaining power of the handling equipment. The current weight of the cargo being handled by the equipment, The reference historical MTBF of handling equipment. The reference cumulative operating time of the handling equipment. is the number of each idle handling equipment, , is the number of idle handling equipment, and e is a natural constant.

[0147] It should be noted that, according to the characteristic data of each idle handling equipment and the cargo weight of the current handling equipment, the handling matching characteristic values ​​of each idle handling equipment are analyzed and processed, taking into account the mutual influence between these parameters. For example, the greater the load-bearing capacity of the handling equipment, the more power may be consumed when carrying heavier cargo. If the maximum load-bearing capacity of the handling equipment is high, when carrying cargo that is close to or reaches its maximum load-bearing capacity, the power components such as the motor need to output a larger torque, which will lead to faster power consumption, thereby reducing the remaining power faster. The greater the maximum load-bearing capacity of the handling equipment, the greater the pressure and load its structure and components usually need to withstand. In the process of carrying heavier cargo for a long time, the key components of the equipment (such as the chassis, lifting mechanism, drive wheel, etc.) are more likely to wear, which will lead to a shortened historical cumulative operating time and an increased probability of failure, thereby reducing the historical average trouble-free operation time. Handling equipment with a longer historical average trouble-free operation time usually indicates that its overall design, manufacturing quality and maintenance management level are higher. In this case, the equipment can work more stably during the historical cumulative operating time, reducing the downtime due to failures, so that the operating time can be used more effectively and the historical cumulative operating time can be extended.

[0148] The idle transport equipment corresponding to the maximum value of the idle transport equipment matching feature is extracted and recorded as the optimal transport equipment.

[0149] In another specific embodiment, the optimal transport equipment may also be obtained by extracting a transport matching verification index preset in a database.

[0150] The idle transport devices corresponding to the transport matching feature values ​​greater than the transport matching verification index are extracted and marked as the adapted transport devices.

[0151] Upload each compatible handling equipment to the cloud processing center, use the mixed reality device to request confirmation, and determine the optimal handling equipment based on the confirmation operation results.

[0152] See also Figure 2 As shown, an embodiment of the present invention provides an intelligent warehousing operation assistance method based on mixed reality, comprising the following steps:

[0153] S1, obtain the status characteristic information of each idle stack point and the storage goods information for processing, and pre-screen to obtain each target storage stack point.

[0154] S2, upload the target storage stack points of the warehouse goods to the cloud processing center, and use the mixed reality device to request confirmation operations, thereby determining the optimal storage stack point, synchronously obtaining the initial stack point of the warehouse goods, and processing to obtain the optimal execution path of the warehousing task.

[0155] S3, decompose the optimal execution path of the warehousing task to obtain each execution branch path, monitor the execution data of the execution branch path task, analyze the execution index of the execution branch path task, and synchronously determine the task execution status label, which includes normal task execution and abnormal task execution.

[0156] S4, when the task execution status label of the optimal execution sub-path of the warehousing task is determined to be an abnormal task execution, the early warning mechanism is triggered and the early warning information is displayed through the mixed reality device. Combined with the execution index of the branch path task, the handling equipment replacement mode is determined.

[0157] S5, acquiring characteristic data of each idle transport equipment, screening the optimal transport equipment based on the cargo data of the current transport equipment, and controlling the optimal transport equipment to continue the transport task according to the transport equipment replacement mode.

[0158] In a specific embodiment, by providing an intelligent warehousing operation assistance system and method based on mixed reality, the optimal storage stacking point is screened, the utilization rate of the storage space is improved, and the squeezing of goods or waste of space caused by space mismatch is avoided. The optimal storage stacking point and execution path are determined, the transportation efficiency is improved, and energy consumption is reduced. At the same time, through the interactive function of the mixed reality device, the operator can participate in the decision-making more intuitively and conveniently, and the human-computer collaboration effect is enhanced. Through comprehensive monitoring and analysis of task execution data, abnormal conditions can be discovered in time to ensure the reliability of task execution. When an abnormality occurs, it can quickly trigger an early warning and reasonably determine the replacement mode of the handling equipment to ensure the continuity and efficiency of warehousing operations, thereby improving the overall operational efficiency of the intelligent warehousing system, the quality of cargo storage and the scientific nature of management decisions.

[0159] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0160] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. An intelligent warehouse operation assistance system based on mixed reality, characterized by: include: The storage point pre-screening module is used to obtain the status characteristic information of each idle point and the storage goods information for processing, and pre-screen each target storage point; The intelligent task planning module is used to upload each target storage stack point to the cloud processing center and use the mixed reality device to request confirmation operations, thereby determining the optimal storage stack point, synchronously obtaining the initial stack point of the stored goods, and processing to obtain the optimal execution path of the storage task; The task execution monitoring module is used to decompose the optimal execution path of the warehousing task to obtain each execution branch path, monitor the execution data of the execution branch path task, analyze the execution index of the execution branch path task, and synchronously determine the task execution status label, which includes normal task execution and abnormal task execution; The abnormal task execution adjustment module is used to trigger the early warning mechanism and display the early warning information through the mixed reality device when the task execution status label determines that the task is abnormally executed, and simultaneously combine the execution index of the branch path task to determine the replacement mode of the handling equipment; The optimal handling equipment screening module is used to obtain the characteristic data of each idle handling equipment, process and screen the optimal handling equipment, control the optimal handling equipment to perform the auxiliary handover of storage and handling tasks, and continue to complete the storage and handling tasks; The pre-screening obtains each target storage stack point, and the specific acquisition method is as follows: According to the status characteristic information of each idle stack point and the storage cargo information, the storage cargo adaptation index of each idle stack point is obtained by analysis and processing; The storage cargo adaptation index of each idle storage point is used to characterize the degree of adaptation between the storage cargo and each idle storage point; Extract the storage goods adaptation verification threshold preset in the database; If the storage cargo adaptation index of a certain idle stack point is greater than the storage cargo adaptation verification threshold, the idle stack point is extracted and recorded as the target storage stack point, and each target storage stack point is counted; The processing obtains the optimal execution path of the warehousing task, and the specific process is as follows: Obtain the transportable paths between the initial stacking point and the optimal storage stacking point of the stored goods, record them as executable paths, and collect path state parameters of each executable path; Based on the path state parameters of each executable path, analyzing and processing to obtain the transportation priority value of each executable path, wherein the transportation priority value of each executable path is used to characterize the transportation priority degree of each executable path; Extract the preset transport priority threshold in the database, and extract the executable paths corresponding to the transport priority value greater than the transport priority threshold, and record them as the target execution paths; Upload each target execution path to the cloud processing center, use mixed reality devices to request confirmation operations, and determine the optimal execution path for the warehousing task based on the confirmation operation results; The analysis and execution of branch path task execution indicators is as follows: Based on the transport state index of the execution branch path and the transport capacity correction coefficient of the transport equipment, the execution branch path task execution index is obtained by analyzing and processing, and the execution branch path task execution index is used to characterize the corrected transport state of the transport equipment on the execution branch path; The specific determination process of determining the replacement mode of the handling equipment is as follows: Get the current branch path task execution index, recorded as the abnormal task execution index; Extract the equipment replacement mode determination factor corresponding to each task execution index interval stored in the database, and map the equipment replacement mode determination factor corresponding to the interval where the abnormal task execution index is located, and mark it as the handling equipment replacement mode determination factor; Obtain the remaining path state parameters of the currently executed branch path, and combine them with the handling equipment replacement mode determination factor to analyze and obtain the handling equipment replacement mode determination indicator parameter; Extracting the threshold value of the parameter for determining the indication of the preset replacement mode in the database; If the handling equipment replacement mode determination indicator parameter is greater than or equal to the replacement mode determination indicator parameter threshold, the handling equipment replacement mode is defined as the first replacement mode, and the current handling equipment handling task is stopped, and the handling equipment replacement is directly determined; If the transport equipment replacement mode determination indicator parameter is less than the replacement mode determination indicator parameter threshold, the transport equipment replacement mode is defined as the second replacement mode, and the current transport equipment is controlled to continue transporting to the next execution path initial node for transport equipment replacement.

2. The intelligent warehouse operation assistance system based on mixed reality according to claim 1, characterized in that: The pre-screening obtains each target storage stack point, and the specific acquisition method also includes: The state characteristic information of each idle stack point includes basic data of each idle stack point and environmental data of each idle stack point; The basic data of each free stack point includes the available height, available space volume, maximum load-bearing capacity, and used load-bearing capacity of each free stack point; The environmental data of each idle stack point includes the current temperature, current humidity, current light intensity, adjacent maintenance interval and historical maintenance frequency of each idle stack point; Warehouse cargo information includes the height, volume and weight of the cargo to be transported.

3. The intelligent warehouse operation assistance system based on mixed reality according to claim 1, characterized in that: The task execution data includes real-time operation data and historical impact data, wherein the real-time operation data includes the real-time speed, real-time acceleration, real-time power fluctuation value and real-time power consumption rate of the transport equipment in the execution branch path; The historical impact data includes the historical cumulative running time of the handling equipment in the execution branch path, the historical maintenance frequency, the total number of historical failures and the historical average trouble-free running time.

4. The intelligent warehouse operation assistance system based on mixed reality according to claim 3 is characterized in that: The specific process of analyzing and executing branch path task execution indicators also includes: According to the real-time operation data, the execution branch path transport state indicator is obtained by analyzing and processing, and the execution branch path transport state indicator is used to characterize the transport state of the transport equipment in the execution branch path; According to the historical impact data, the handling capacity correction coefficient of the handling equipment is obtained by analysis and processing, and the handling capacity correction coefficient of the handling equipment is used to characterize the impact degree of the historical use status of the handling equipment.

5. The intelligent warehouse operation assistance system based on mixed reality according to claim 1, characterized in that: The specific process of determining the task execution status label is as follows: Extract the task execution indicator thresholds preset in the database; If the task execution index of the execution branch path is greater than or equal to the task execution index threshold, the task execution status label of the current branch path is marked as abnormal task execution. If the task execution index of the execution branch path is less than the task execution index threshold, the task execution status label of the current branch path is marked as normal task execution.

6. The intelligent warehouse operation assistance system based on mixed reality according to claim 1, characterized in that: The optimal screening handling equipment, the specific screening process is as follows: Obtain characteristic data of each idle handling equipment, including remaining power, maximum load-bearing capacity, historical mean time between failures, and accumulated operating time of each idle handling equipment; Synchronously extract the cargo weight of the current handling equipment; According to the characteristic data of each idle transport equipment and the cargo data of the current transport equipment, analyzing and processing to obtain the transport matching characteristic value of each idle transport equipment, wherein the transport matching characteristic value of each idle transport equipment is used to characterize the adaptability of each idle transport equipment in transporting the cargo; The idle transport equipment corresponding to the maximum value of the idle transport equipment matching feature is extracted and recorded as the optimal transport equipment.

7. A method for a mixed reality-based intelligent warehouse operation assistance system as claimed in any one of claims 1 to 6, characterized in that: The following steps are involved: S1, obtaining the status characteristic information of each idle stack point and the storage goods information for processing, and pre-screening to obtain each target storage stack point; S2, upload the target storage stacking points of the warehouse goods to the cloud processing center, and use the mixed reality device to request confirmation operation, thereby determining the optimal storage stacking point, synchronously obtaining the initial stacking point of the warehouse goods, and processing to obtain the optimal execution path of the warehousing task; S3, decomposing the optimal execution path of the warehousing task to obtain each execution branch path, monitoring the execution data of the execution branch path task, analyzing the execution index of the execution branch path task, and synchronously determining the task execution status label, wherein the task execution status label includes normal task execution and abnormal task execution; S4, when the task execution status label of the optimal execution sub-path of the warehousing task is determined to be abnormal task execution, the warning mechanism is triggered and the warning information is displayed through the mixed reality device, and the handling equipment replacement mode is determined in combination with the execution index of the branch path task; S5, acquiring characteristic data of each idle transport equipment, screening the optimal transport equipment based on the cargo data of the current transport equipment, and controlling the optimal transport equipment to continue the transport task according to the transport equipment replacement mode.

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