Intelligent warehousing equipment collaborative decision-making system and method based on digital twinning

Abstract

The invention discloses an intelligent storage equipment collaborative decision-making system and method based on digital twinning, and relates to the technical field of intelligent storage management. The intelligent warehousing equipment collaborative decision-making system based on digital twinning comprises a digital twinning synchronization module, a data processing module, an equipment collaborative strategy optimization module and a decision-making execution module. According to the method, the real-time data synchronization validity score is obtained by monitoring the synchronization condition between the real-time data in the constructed storage digital twin model in real time, and the real-time synchronization data is processed to obtain the data processing evaluation index; judging whether to adjust a cooperation strategy parameter or not based on the obtained cooperation strategy effect evaluation value, and finally generating a cooperation decision and judging whether to trigger an emergency response mechanism or not, thereby achieving the improvement of the cooperation performance evaluation reliability in the construction and cooperation decision process of the intelligent storage equipment. The problem that in the prior art, cooperative performance evaluation is low in reliability in the intelligent storage equipment construction and cooperative decision-making process is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent warehousing management, and in particular to an intelligent warehousing equipment collaborative decision-making system and method based on digital twins. Background Art

[0002] With the rapid development of e-commerce and modern logistics, intelligent warehousing systems play an increasingly important role in improving warehousing efficiency and reducing operating costs. In the field of intelligent warehousing, digital twin technology can build a virtual warehouse model corresponding to the physical warehouse, which can reflect key information such as cargo distribution, equipment status, and personnel activities in the warehouse in real time. However, traditional warehouse management systems face problems such as low equipment coordination efficiency, insufficient decision-making accuracy, and poor system reliability. Although some studies have attempted to introduce artificial intelligence and Internet of Things technologies to optimize warehouse management, there is still a lack of a comprehensive solution that can fully integrate advanced technologies such as digital twins, swarm intelligence, and graph neural networks.

[0003] Existing technologies collect real-time data of physical entities through sensors and perform preprocessing and cleaning. Based on the preprocessed real-time data, modeling software and physical engines are used to build a virtual model of intelligent warehousing equipment. Then, real-time data exchange between the physical entity layer and the digital twin model layer is achieved through the data interaction and processing layer. Finally, simulation technology is used to achieve collaborative decision-making and scheduling of intelligent warehousing equipment.

[0004] For example, the AGV path optimization method and system based on digital twin intelligent warehousing announced by the invention patent with announcement number: CN118313536B includes: obtaining the dimensional data of the AGV and the dimensional data of the area managed by the intelligent warehousing; establishing a three-dimensional rectangular coordinate system, and obtaining parallel channels and parallel values ​​in the three-dimensional rectangular coordinate system; obtaining the historical action path of the AGV, and marking the parallel channels in parallel based on the historical action path of the AGV and the parallel channels; based on the parallel channels of the AGV marked in parallel, optimizing the AGV path when multiple AGVs are operating simultaneously.

[0005] For example, the invention patent announcement with announcement number: CN117391591B discloses an intelligent warehousing optimization system based on digital twins, including: constructing a digital twin model of the warehousing system, and performing real-time monitoring and predictive analysis on the warehousing system through the digital twin model of the warehousing system; optimizing the scheduling of the warehousing system according to the predictive analysis results of the digital twin model; controlling and adjusting the actual operation of the warehousing system according to the optimized scheduling plan; classifying the associated data to be managed, and obtaining the update timeliness coefficient through update timeliness analysis. If the update timeliness coefficient exceeds the preset update timeliness coefficient threshold, an update timeliness unqualified signal is generated; if the update timeliness coefficient does not exceed the preset update timeliness coefficient threshold, an update timeliness qualified signal is generated.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, the construction of digital twin models relies on the accurate description and simulation of the entities in the room. Since smart warehousing equipment usually undergoes various changes during actual operation, such as equipment upgrades and environmental changes, the update of the digital twin model may not be able to fully capture all details and features, which in turn leads to limited collaboration between collaborative decision-making and scheduling algorithms. There is a problem of low reliability of collaborative performance evaluation in the construction and coordinated decision-making process of smart warehousing equipment. Summary of the invention

[0008] The embodiments of the present application provide a collaborative decision-making system and method for intelligent warehousing equipment based on digital twins, thereby solving the problem of low reliability of collaborative performance evaluation in the process of intelligent warehousing equipment construction and coordinated decision-making in the prior art, and achieving improved reliability of collaborative performance evaluation in the process of intelligent warehousing equipment construction and coordinated decision-making.

[0009] The embodiment of the present application provides a collaborative decision-making system for intelligent warehousing equipment based on digital twins, including: a digital twin synchronization module, a data processing module, an equipment collaboration strategy optimization module, and a decision execution module; wherein the digital twin synchronization module is used to monitor the real-time data synchronization status of the constructed warehouse digital twin model in real time to obtain a real-time data synchronization validity score, and the real-time data synchronization validity score is used to evaluate the real-time synchronization validity of the real-time data between the physical warehouse equipment and the three-dimensional digital model of the warehouse equipment; the data processing module is used to collect the real-time synchronization data of the warehouse digital twin model within a preset time period according to the acquired real-time data synchronization validity score and perform data processing, and at the same time, based on the real The data processing evaluation index is obtained according to the changes of the operating parameters in the data processing process, and the data processing evaluation index is used to evaluate the data processing reliability of the real-time synchronization data within a preset time period; the equipment collaboration strategy optimization module is used to construct a graph structure and simulate the collaboration process of the storage equipment in a preset scenario to obtain a collaboration strategy effect evaluation value, and judge whether to adjust the collaboration strategy parameters based on the acquired collaboration strategy effect evaluation value, and the collaboration strategy effect evaluation value is used to evaluate the collaboration performance of the storage equipment within a preset time period; the decision execution module is used to generate a collaborative decision according to the adjusted collaboration strategy parameters, and monitor the actual execution of the storage equipment under the collaborative decision in real time to determine whether to trigger the emergency response mechanism.

[0010] Furthermore, the specific steps for obtaining the real-time data synchronization effectiveness score are: counting the real-time monitoring data volume of the real-time data in the warehouse digital twin model within a preset time period, and judging whether the real-time monitoring data volume is greater than the preset real-time monitoring data volume; if so, obtaining the real-time status monitoring score in combination with the total data volume of the real-time data; otherwise, prompting the preset personnel to inspect and re-monitor the monitoring equipment corresponding to the real-time data in the warehouse digital twin model. The total data volume of the real-time data is the sum of the number of real-time data in the physical warehouse equipment and the number of real-time data in the three-dimensional digital model. The real-time status monitoring score is used to evaluate the accuracy of the warehouse digital twin model in real-time status monitoring of the warehouse equipment; real-time monitoring of the warehouse digital twin model The synchronization delay duration of the real-time data in the model within a preset time period is combined with the corresponding expected synchronization duration to obtain the storage equipment compliance score, and the storage equipment compliance score is used to evaluate the similarity between the physical storage equipment and the three-dimensional digital model of the storage equipment; the average delay duration of the real-time data within the preset time period is obtained and the total amount of real-time data in the physical storage equipment and the amount of data successfully synchronized with the real-time data in the three-dimensional digital model are counted to obtain the data synchronization efficiency score, and the data synchronization efficiency score is used to evaluate the data synchronization efficiency between the physical storage equipment and the three-dimensional digital model of the storage equipment; the real-time data synchronization validity score is obtained by combining the obtained real-time status monitoring score, the storage equipment compliance score and the data synchronization efficiency score.

[0011] Furthermore, the specific limiting expression of the real-time data synchronization effectiveness score is:

[0012] ;

[0013] Where t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, It represents the real-time data synchronization effectiveness score of the real-time data in the warehouse digital twin model within the t-th preset time period. It represents the real-time status monitoring score of the real-time data in the warehouse digital twin model in the t-th preset time period. It represents the amount of real-time monitoring data in the warehouse digital twin model within the t-th preset time period. Indicates the preset real-time monitoring data volume, It represents the compliance score of the real-time data in the warehouse digital twin model within the t-th preset time period. Represents the data synchronization efficiency score of the real-time data in the warehouse digital twin model within the tth preset time period.

[0014] Furthermore, the specific acquisition process of the data processing evaluation index is as follows: the amount of successfully collected data of the real-time synchronization data within a preset time period and the total data collection time corresponding to the collection of these data amounts are counted, and the data collection efficiency score is obtained in combination with the total data amount of the real-time synchronization data within the preset time period; the data processing process of the real-time synchronization data within the preset time period is monitored in real time to obtain the data transmission score, the data cleaning score and the data storage score, and the data processing evaluation index is obtained by combining the obtained data collection efficiency score and the data storage rate; the data collection efficiency score is the product of the ratio of the amount of successfully collected data in the real-time synchronization data within the preset time period to the total data collection time and the data collection accuracy rate; the data collection accuracy rate is the ratio of the amount of successfully collected data in the real-time synchronization data to the total data amount of the real-time synchronization data to be collected; the data transmission score is the ratio of the amount of successfully transmitted data within the preset time period to the amount of successfully collected data to be transmitted; the data cleaning score is the ratio of the amount of successfully cleaned data within the preset time period to the amount of successfully transmitted data to be cleaned; the data storage score is the product of the ratio of the amount of successfully stored data within the preset time period to the total capacity of the storage device and the inverse of the total data storage time; the data storage rate is the ratio of the amount of successfully stored data to the amount of successfully collected data.

[0015] Furthermore, the collaborative strategy effectiveness evaluation value is obtained by the following method: counting the number of collaborative work times and the corresponding collaborative work duration between the storage equipment within a preset time period, determining whether the collaborative work duration is not greater than the preset collaborative work duration, and if so, obtaining the collaborative work number score and the collaborative work duration score; counting the actual collaborative work volume of the storage equipment within the preset time period and obtaining the collaborative work rate score in combination with the amount of collaborative tasks to be completed, and at the same time combining the obtained data processing evaluation index, the collaborative work number score and the collaborative work duration score to obtain the collaborative strategy effectiveness evaluation value; the collaborative work number score is the ratio of the absolute value of the difference between the collaborative work number and the reference collaborative work number to the reference collaborative work number; the collaborative work duration score is the ratio of the difference between the preset collaborative work duration and the collaborative work duration to the preset collaborative work duration; the collaborative work rate score is the ratio of the actual collaborative work volume to the amount of collaborative tasks to be completed.

[0016] Furthermore, the specific process of real-time monitoring of the actual execution of storage equipment under collaborative decision-making is: real-time monitoring of the actual collaborative order of storage equipment within a preset time period, and at the same time real-time monitoring of the scheduling errors of storage equipment within the preset time period to obtain a scheduling error score, and the scheduling error score is the ratio of the number of scheduling errors to the total number of scheduling errors; real-time monitoring of the scheduling delays of storage equipment within the preset time period to obtain the actual scheduling delay duration, and the actual scheduling delay duration is the total duration of the scheduling operation performed by the storage equipment within the preset time period.

[0017] The embodiment of the present application provides a collaborative decision-making method for intelligent warehousing equipment based on digital twins, comprising the following steps: Step 1, real-time monitoring of the real-time data synchronization in the constructed warehousing digital twin model to obtain a real-time data synchronization validity score, wherein the real-time data synchronization validity score is used to evaluate the real-time synchronization validity of the real-time data between the physical warehousing equipment and the three-dimensional digital model of the warehousing equipment; Step 2, collecting the real-time synchronization data of the warehousing digital twin model within a preset time period according to the obtained real-time data synchronization validity score and performing data processing, and obtaining a data processing evaluation index based on the change of real-time operating parameters during the data processing process, wherein the data processing evaluation index is used to evaluate the data processing reliability of the real-time synchronization data within a preset time period; Step 3, constructing a graph structure and simulating the collaborative process of the warehousing equipment in a preset scenario to obtain a collaborative strategy effect evaluation value, and judging whether to adjust the collaborative strategy parameters based on the obtained collaborative strategy effect evaluation value, wherein the collaborative strategy effect evaluation value is used to evaluate the collaborative performance of the warehousing equipment within a preset time period; Step 4, generating a collaborative decision according to the adjusted collaborative strategy parameters, and monitoring the actual execution of the warehousing equipment under the collaborative decision in real time to determine whether to trigger the emergency response mechanism.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] 1. By real-time monitoring of the real-time data synchronization in the constructed warehouse digital twin model to obtain the real-time data synchronization effectiveness score, and at the same time performing data processing on the real-time synchronization data to obtain the data processing evaluation index, then judging whether to adjust the collaboration strategy parameters based on the obtained collaboration strategy effect evaluation value, and finally generating a collaborative decision based on the adjusted collaboration strategy parameters and determining whether to trigger the emergency response mechanism, a more accurate evaluation of the real-time data synchronization in the warehouse digital twin model is achieved, thereby achieving an improvement in the reliability of collaborative performance evaluation in the process of intelligent warehouse equipment construction and coordinated decision-making, and effectively solving the problem of low reliability of collaborative performance evaluation in the process of intelligent warehouse equipment construction and coordinated decision-making in the prior art.

[0020] 2. The storage equipment compliance score is obtained by obtaining the real-time status monitoring score and monitoring the synchronization delay duration and expected synchronization duration of the real-time data in the warehouse digital twin model within the preset time period. At the same time, the real-time data synchronization effectiveness score is obtained based on the obtained data synchronization efficiency score and combined with the obtained real-time status monitoring score, thereby improving the accuracy and reliability of the real-time data synchronization effectiveness score, and further realizing a more accurate evaluation of the synchronization performance between real-time data.

[0021] 3. By counting the collaborative work time that is no more than the preset collaborative work time, the collaborative work frequency score and the collaborative work time score are obtained. At the same time, the actual collaborative work volume of the storage equipment in the preset time period is counted and combined with the amount of collaborative tasks to be completed to obtain the collaborative work rate score. The obtained data processing evaluation index, collaborative work frequency score and collaborative work time score are combined to obtain the collaborative strategy effect evaluation value, thereby achieving improved accuracy and reliability in obtaining the collaborative strategy effect evaluation value, and further achieving a more accurate evaluation of the collaborative performance between storage equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the structure of a collaborative decision-making system for intelligent warehousing equipment based on digital twins provided in an embodiment of the present application;

[0023] Figure 2 A flowchart of a collaborative decision-making method for intelligent warehousing equipment based on digital twins provided in an embodiment of the present application;

[0024] Figure 3 A specific generation flow chart of collaborative decision-making provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The embodiment of the present application solves the problem of low reliability of collaborative performance evaluation in the construction and coordinated decision-making process of intelligent warehousing equipment in the prior art by providing a collaborative decision-making system and method for intelligent warehousing equipment based on digital twins. The digital twin synchronization module monitors the synchronization status between real-time data in the constructed warehousing digital twin model in real time to obtain the real-time data synchronization validity score. At the same time, the data processing module collects the real-time synchronization data of the warehousing digital twin model within a preset time period and performs data processing according to the obtained real-time data synchronization validity score. At the same time, the data processing evaluation index is obtained based on the change of real-time operating parameters during the data processing process. Then, the equipment collaborative strategy optimization module constructs a graph structure and simulates the collaborative process of warehousing equipment in a preset scenario to obtain a collaborative strategy effect evaluation value. Based on the obtained collaborative strategy effect evaluation value, it is determined whether to adjust the collaborative strategy parameters. Finally, the decision execution module generates a collaborative decision according to the adjusted collaborative strategy parameters. At the same time, the actual execution of the warehousing equipment under the collaborative decision is monitored in real time to determine whether to trigger the emergency response mechanism, thereby improving the reliability of collaborative performance evaluation in the construction and coordinated decision-making process of intelligent warehousing equipment.

[0026] The technical solution in the embodiment of the present application is to solve the problem of low reliability of collaborative performance evaluation in the process of building and coordinating decision-making of the above-mentioned intelligent storage equipment. The overall idea is as follows:

[0027] By real-time monitoring of the real-time data synchronization in the constructed warehouse digital twin model to obtain the real-time data synchronization effectiveness score, and at the same time performing data processing on the real-time synchronized data to obtain the data processing evaluation index, and then judging whether to adjust the collaboration strategy parameters based on the obtained collaboration strategy effect evaluation value, finally generating a collaborative decision based on the adjusted collaboration strategy parameters and determining whether to trigger the emergency response mechanism, the effect of improving the reliability of collaborative performance evaluation in the process of intelligent warehousing equipment construction and coordinated decision-making is achieved.

[0028] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0029] like Figure 1As shown, it is a structural schematic diagram of a collaborative decision-making system for intelligent warehousing equipment based on digital twins provided in an embodiment of the present application. The collaborative decision-making system for intelligent warehousing equipment based on digital twins provided in an embodiment of the present application includes: a digital twin synchronization module, a data processing module, an equipment collaboration strategy optimization module, and a decision execution module; wherein the digital twin synchronization module is used to map the physical warehousing equipment with the three-dimensional digital model of the warehousing equipment to construct a warehousing digital twin model, and monitor the real-time data synchronization in the constructed warehousing digital twin model in real time to obtain a real-time data synchronization validity score. The warehousing digital twin model is used to visualize the real-time data mapping relationship between the physical warehousing equipment and the three-dimensional digital model of the warehousing equipment. The real-time data synchronization validity score is used to evaluate the real-time synchronization validity of the real-time data between the physical warehousing equipment and the three-dimensional digital model of the warehousing equipment; the data processing module is used to collect the real-time synchronization data of the warehousing digital twin model within a preset time period according to the acquired real-time data synchronization validity score and perform data processing, and at the same time, based on the real-time operating parameters in the data processing process The data processing evaluation index is obtained according to the changes in the real-time synchronization data. The real-time synchronization data is the real-time data successfully synchronized in the warehouse digital twin model. The data processing evaluation index is used to evaluate the data processing reliability of the real-time synchronization data within a preset time period; the equipment collaboration strategy optimization module is used to construct a graph structure according to the particle swarm optimization algorithm in the swarm intelligence algorithm and simulate the collaboration process of the storage equipment in the preset scenario to obtain the collaboration strategy effect evaluation value, and judge whether to adjust the collaboration strategy parameters based on the acquired collaboration strategy effect evaluation value. The graph structure is used to visualize the connection relationship, dependency relationship and communication path between the storage equipment. The collaboration strategy effect evaluation value is used to evaluate the collaborative performance of the storage equipment within a preset time period. The collaboration strategy parameters include working mode and communication protocol; the decision execution module is used to generate collaborative decisions according to the adjusted collaboration strategy parameters, and monitor the actual execution of the storage equipment under the collaborative decision in real time to determine whether to trigger the emergency response mechanism. The collaborative decision includes the scheduling of the storage equipment and the collaboration sequence between the storage equipment. The emergency response mechanism includes suspending operation, switching to spare storage equipment and adjusting the collaborative decision.

[0030] In this embodiment, the specific steps for constructing the warehouse digital twin model are: based on the physical warehouse equipment data (usually including the type, specification, location and operating status of the warehouse equipment, obtained through on-site inspection by sensors) and the warehouse environment data (usually including the floor plan of the warehouse workshop or warehouse, shelf configuration, warehouse in and out process and operation data, transmitted to the controller through sensors for display), and using three-dimensional modeling tools (such as Solid Works) to build a three-dimensional digital model of the warehouse equipment (used to visualize the geometric shape, size and texture of the warehouse equipment), while using sensors and controllers deployed in the physical warehouse equipment to collect the operating status and environmental data of the warehouse equipment in real time (these data are transmitted to the existing digital twin model through communication protocols such as OPC UA), so as to obtain a warehouse digital twin model (used to visualize the synchronization of geometric position information, motion state synchronization and energy consumption information between physical warehouse equipment and warehouse equipment).

[0031] Specifically, the specific steps of constructing the graph structure are as follows: determine the type and number of equipment participating in the collaboration according to the actual situation of the storage equipment; create a corresponding node for each device in the graph structure and initialize its attributes (such as position, status); add edges between device nodes in the graph structure according to the collaboration needs between storage equipment, such as data transmission needs and material delivery needs, and define the attributes of the edges (such as transmission direction and transmission rate); regard each device node as a particle, and define the position vector and velocity vector of the particle according to the attributes and collaboration relationship of the device; update the position and velocity of the particle through iteration, and use the information sharing mechanism between particles to update the local optimal position (Pbest) and global optimal position (Gbest) of each particle during the iteration process; determine the optimal collaboration strategy between devices according to the results of the particle swarm optimization algorithm. The reliability of collaborative performance evaluation in the construction and coordination decision-making process of intelligent storage equipment is improved.

[0032] Furthermore, the specific steps for obtaining the real-time data synchronization effectiveness score are as follows: the real-time monitoring data volume of the real-time data in the warehouse digital twin model within the preset time period is counted, and whether the real-time monitoring data volume is greater than the preset real-time monitoring data volume is determined. If so, the real-time status monitoring score is obtained in combination with the total data volume of the real-time data (i.e., the real-time data synchronization effectiveness score in the restriction expression) ), otherwise the preset personnel are prompted to repair and re-monitor the monitoring equipment corresponding to the real-time data in the warehouse digital twin model. The total amount of real-time data is the sum of the number of real-time data in the physical warehouse equipment and the number of real-time data in the three-dimensional digital model. The real-time status monitoring score is used to evaluate the accuracy of the warehouse digital twin model in real-time status monitoring of the warehouse equipment; the real-time data in the warehouse digital twin model is monitored in real time. The synchronization delay time within the preset time period is combined with the corresponding expected synchronization time to obtain the warehouse equipment compliance score (that is, the real-time data synchronization validity score in the restriction expression ), the storage equipment compliance score is used to evaluate the similarity between the physical storage equipment and the three-dimensional digital model of the storage equipment; the average delay time of the real-time data in the preset time period is obtained and the total amount of real-time data in the physical storage equipment and the amount of real-time data successfully synchronized in the three-dimensional digital model are counted to obtain the data synchronization efficiency score (that is, the real-time data synchronization effectiveness score in the restriction expression ), the data synchronization efficiency score is used to evaluate the data synchronization efficiency between the physical storage equipment and the three-dimensional digital model of the storage equipment; the real-time data synchronization effectiveness score is obtained by combining the acquired real-time status monitoring score, storage equipment compliance score and data synchronization efficiency score.

[0033] In this embodiment, the preset real-time monitoring data volume (set by the preset personnel) is represented by the sum and average of the historical real-time data monitoring quantities in the historical time period in the preset database; the expected synchronization duration is represented by the sum and average of the minimum values ​​of the historical synchronization delay durations of the historical real-time data in the preset database within the historical time period; when real-time data is monitored in the warehouse digital twin model, it indicates that the corresponding real-time data is successfully synchronized, thereby achieving more accurate acquisition of the real-time data synchronization effectiveness score.

[0034] The specific limiting expression of the real-time data synchronization effectiveness score is:

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] Where t is the number of the preset time period, , T is the total number of preset time periods, i is the number of real-time data, , Y is the total amount of real-time data, e is a natural constant, It represents the real-time data synchronization effectiveness score of the real-time data in the warehouse digital twin model within the t-th preset time period. It represents the real-time status monitoring score of the real-time data in the warehouse digital twin model in the t-th preset time period. It represents the amount of real-time monitoring data in the warehouse digital twin model within the t-th preset time period. It represents the total amount of real-time data in the warehouse digital twin model within the t-th preset time period. Indicates the preset real-time monitoring data volume, It represents the compliance score of the real-time data in the warehouse digital twin model within the t-th preset time period. represents the actual value of the i-th real-time data in the warehouse digital twin model within the t-th preset time period, Represents the reference value of the i-th real-time data in the warehouse digital twin model (represented by the preset personnel summing and averaging the historical actual values ​​of the historical real-time data in the warehouse digital twin model in the preset database), It represents the synchronization delay of the real-time data in the warehouse digital twin model in the tth preset time period. Indicates the expected duration of synchronization. It represents the data synchronization efficiency score of the real-time data in the warehouse digital twin model in the t-th preset time period. It represents the amount of real-time data in the warehouse digital twin model that is successfully synchronized within the t-th preset time period. It represents the total amount of real-time data in the warehouse digital twin model in the t-th preset time period. It represents the average delay duration of the real-time data in the warehouse digital twin model in the tth preset time period.

[0040] In this embodiment, assuming that the total amount of real-time data in a preset time period is 100, the preset amount of real-time monitoring data is usually recorded as 60, and the statistical table of changes in the real-time data synchronization effectiveness score is shown in Table 1:

[0041] Table 1 Statistics of changes in real-time data synchronization effectiveness scores

[0042]

[0043] It should be understood that, as shown in Table 1, the real-time data synchronization effectiveness score increases with the increase of the real-time status monitoring score, the storage equipment compliance score and the data synchronization efficiency score. Among them, the real-time status monitoring score also indirectly affects the values ​​of the storage equipment compliance score and the data synchronization efficiency score. When the real-time status monitoring score decreases, it usually means that the real-time status monitoring of the storage equipment by the storage digital twin model is inaccurate (such as failure to monitor the abnormal load-bearing of a certain shelf). This change causes the digital twin model to delay the reflection of real-time data, thereby increasing the synchronization delay time. Secondly, since the storage digital twin model fails to accurately reflect the real-time status of the physical storage equipment, the status difference between the three-dimensional digital model and the physical storage equipment increases (that is, the storage equipment compliance score decreases). This indirect influence mechanism further emphasizes the importance of real-time status monitoring in the construction and coordination decision-making process of intelligent storage equipment, thereby improving the accuracy of collaborative performance evaluation, and then realizing the improvement of the reliability of collaborative performance evaluation in the construction and coordination decision-making process of intelligent storage equipment, and effectively solving the problem of low reliability of collaborative performance evaluation in the construction and coordination decision-making process of intelligent storage equipment in the prior art.

[0044] Furthermore, a real-time data synchronization validity score is obtained, and then the function of the data processing module is determined based on the obtained real-time data synchronization validity score. The specific judgment process is: determine whether the obtained real-time data synchronization validity score is within the real-time data synchronization threshold range: if the obtained real-time data synchronization validity score is within the real-time data synchronization threshold range (including the case where it is equal to the maximum and minimum values ​​of the historical real-time data synchronization validity scores), the function of the data processing module is executed; if the obtained real-time data synchronization validity score is not within the real-time data synchronization threshold range, the function of the data processing module is not executed and the function of the digital twin synchronization module is re-executed.

[0045] In this embodiment, the real-time data synchronization threshold range represents the range corresponding to the maximum and minimum values ​​of the historical real-time data synchronization validity score of the historical real-time data in the preset database within the historical time period. When the real-time data synchronization validity score is within the threshold range, the execution data processing module can perform analysis and decision-making based on accurate data, thereby improving the accuracy of the processing results.

[0046] Furthermore, the specific process of obtaining the data processing evaluation index is as follows: when the obtained real-time data synchronization effectiveness score is within the real-time data synchronization threshold range, the amount of successfully collected data of the real-time synchronization data within the preset time period and the total data collection time corresponding to the collection of these data amounts are counted, and the data collection efficiency score (i.e., the value of the real-time synchronization efficiency score in the restricted expression of the data processing evaluation index) is obtained by combining the total amount of real-time synchronization data within the preset time period. ); Real-time monitoring of the data processing process of real-time synchronization data within a preset time period to obtain the data transmission score (i.e., the value in the restriction expression of the data processing evaluation index). ), data cleaning score (i.e., the restricted expression of the data processing evaluation index ) and the data storage score (i.e., the limiting expression of the data processing evaluation index ), while combining the obtained data acquisition efficiency score and data storage rate (i.e., the limiting expression of the data processing evaluation index ) to obtain the data processing evaluation index.

[0047] Among them, data processing includes data transmission, data cleaning and data storage; the data collection efficiency score is the product of the ratio of the amount of successfully collected data in the real-time synchronization data within the preset time period to the total data collection time and the data collection accuracy rate; the data collection accuracy rate is the ratio of the amount of successfully collected data in the real-time synchronization data to the total amount of real-time synchronization data to be collected; the data transmission score is the ratio of the amount of successfully transmitted data within the preset time period (that is, the amount of real-time synchronization data in the amount of successfully transmitted successfully collected data) to the amount of successfully collected data to be transmitted; the data cleaning score is the ratio of the amount of successfully cleaned data within the preset time period (that is, the amount of real-time synchronization data in the amount of successfully transmitted data after successfully removing noise and anomalies) to the amount of successfully transmitted data to be cleaned; the data storage score is the product of the ratio of the amount of successfully stored data within the preset time period (that is, the amount of real-time synchronization data in the amount of successfully stored successfully cleaned data) to the total capacity of the storage device and the inverse of the total data storage time; the data storage rate is the ratio of the amount of successfully stored data (that is, the amount of real-time synchronization data completed by data storage) to the amount of successfully collected data.

[0048] In this embodiment, assuming that the real-time data synchronization threshold range is between 2 and 3, the specific limiting expression of the data processing evaluation index is:

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] ;

[0054] ;

[0055] Where t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, It represents the data processing evaluation index of the real-time synchronization data in the warehouse digital twin model within the t-th preset time period, It represents the real-time data synchronization effectiveness score of the real-time data in the warehouse digital twin model within the t-th preset time period. Indicates the real-time data synchronization threshold range. It represents the data collection efficiency score of the real-time synchronization data in the warehouse digital twin model in the t-th preset time period, It represents the amount of successfully collected data in the real-time synchronization data in the warehouse digital twin model within the t-th preset time period. Indicates the total duration of data collection. It represents the total amount of real-time synchronized data in the warehouse digital twin model to be collected in the t-th preset time period. represents the data transmission score of the real-time synchronization data in the warehouse digital twin model within the t-th preset time period, It represents the amount of successfully transmitted data of the real-time synchronization data in the warehouse digital twin model within the t-th preset time period, It represents the data cleaning score of the real-time synchronization data in the warehouse digital twin model in the t-th preset time period, It represents the amount of successfully cleaned data of the real-time synchronization data in the warehouse digital twin model within the t-th preset time period, It represents the data storage fraction of the real-time synchronization data in the warehouse digital twin model within the t-th preset time period, It represents the amount of successfully stored data of the real-time synchronization data in the warehouse digital twin model within the t-th preset time period, represents the total capacity of the storage device in the warehouse digital twin model in the t-th preset time period, Indicates the total data storage duration. It represents the data storage rate of the real-time synchronization data in the warehouse digital twin model within the t-th preset time period.

[0056] It should be understood that the data processing evaluation index increases with the increase of the real-time data synchronization effectiveness score, data collection efficiency score, data transmission score, data cleaning score, data storage score and data storage rate. Among them, (1) the data cleaning score and data storage score also indirectly affect the value of the data storage rate. Suppose there is a sales data table that contains erroneous data and duplicate data. Through data cleaning, the erroneous data can be corrected (such as correcting the wrong sales amount to the correct amount) and the duplicate data can be removed (such as deleting the same sales data that has been recorded multiple times). In this way, the sales data table after data cleaning will be more accurate and reliable, and redundant data will be reduced. When these real-time data are stored in the database, the data storage rate will be improved due to the improvement of data quality and the reduction of redundant data.

[0057] (2) The real-time data synchronization effectiveness score also indirectly affects the values ​​of the data collection efficiency score and the data transmission score. When the real-time data synchronization effectiveness score increases, the real-time synchronization data collected by the sensor can be quickly and accurately transmitted to the warehouse digital twin model, which improves the data collection efficiency and data transmission efficiency. It can significantly reduce the data delay and loss, so that the real-time synchronization data can be captured and recorded more accurately, thereby achieving the improvement of the reliability of collaborative performance evaluation in the process of intelligent warehousing equipment construction and coordinated decision-making, and effectively solving the problem of low reliability of collaborative performance evaluation in the process of intelligent warehousing equipment construction and coordinated decision-making in the existing technology.

[0058] Furthermore, the collaborative strategy effect evaluation value is obtained by the following method: when the data processing evaluation index is within the data processing evaluation threshold range (including the case where it is equal to the maximum and minimum values ​​of the historical data processing evaluation index), the number of collaborative work times between the storage devices within the preset time period and the corresponding collaborative work duration are counted to determine whether the collaborative work duration is not greater than the preset collaborative work duration. If so, it indicates that the collaborative work efficiency between the storage devices meets the expected demand and obtains the collaborative work times score (that is, the number of collaborative work times in the restriction expression of the collaborative strategy effect evaluation value). ) and the collaborative work duration score (i.e., the value in the restricted expression of the collaborative strategy effect evaluation value ); Count the actual collaborative workload of storage equipment within the preset time period and combine it with the amount of collaborative tasks to be completed to obtain the collaborative work rate score (i.e., the value in the restricted expression of the collaborative strategy effect evaluation value). ), and combined with the obtained data processing evaluation index (i.e., the restriction expression of the collaborative strategy effect evaluation value ), the collaborative work frequency score and the collaborative work duration score to obtain the collaborative strategy effectiveness evaluation value.

[0059] Among them, the collaborative work frequency score is the ratio of the absolute value of the difference between the collaborative work frequency and the reference collaborative work frequency to the reference collaborative work frequency; the collaborative work time score is the ratio of the difference between the preset collaborative work time and the collaborative work time to the preset collaborative work time; the collaborative work rate score is the ratio of the actual collaborative work volume to the amount of collaborative tasks to be done; the data processing evaluation threshold range represents the range corresponding to the maximum and minimum values ​​of the historical data processing evaluation index of the historical real-time synchronization data in the preset database within the historical time period; the preset collaborative work time is represented by the sum and average of the historical collaborative work time of the historical real-time synchronization data in the preset database within the historical time period.

[0060] Specifically, the specific restricted expression of the collaborative strategy effect evaluation value is:

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] Where t is the number of the preset time period, , T is the total number of preset time periods, It represents the data processing evaluation index of the real-time synchronization data in the warehouse digital twin model within the t-th preset time period, Indicates the data processing evaluation threshold range, It represents the evaluation value of the collaborative strategy effect of the storage equipment in the tth preset time period, It represents the score of the number of collaborative operations of storage equipment in the tth preset time period, It represents the number of collaborative operations of storage equipment in the tth preset time period, Indicates the reference number of collaborative work (represented by the sum and average of the historical collaborative work times of the historical storage equipment in the preset database within the historical time period). It represents the collaborative working time score of the storage equipment in the tth preset time period, Indicates the preset collaborative working time. It indicates the collaborative working time of storage equipment in the tth preset time period. It represents the collaborative working rate score of the storage equipment in the tth preset time period, It represents the actual collaborative workload of the storage equipment in the tth preset time period, Indicates the amount of tasks to be coordinated within the preset time period.

[0066] Specifically, assuming that the data processing evaluation threshold range is between 3 and 4.25, and the preset collaborative working time is 10 seconds, the change statistics of the collaborative strategy effect evaluation value are shown in Table 2:

[0067] Table 2 Statistics of changes in collaborative strategy effect evaluation values

[0068]

[0069] It should be understood that, as shown in Table 2, the evaluation value of the collaborative strategy effect decreases with the increase of the collaborative work time score, and increases with the increase of the collaborative work times score, the collaborative work rate score and the data processing evaluation index. Among them, the collaborative work time score decreases with the increase of the collaborative work time, and the collaborative work times score increases with the collaborative work times deviation (i.e. ) increases, and the collaborative work rate score increases with the increase of the actual collaborative work volume.

[0070] It should be noted that (1) the collaborative work duration score and the collaborative work frequency score also indirectly affect the value of the collaborative work rate score. When the collaborative work duration increases, it means that there is a delay in the collaborative work between storage equipment, which in turn reduces the speed of completing the actual collaborative work (that is, the collaborative work rate score decreases).

[0071] (2) The data processing evaluation index also indirectly affects the value of the collaborative work rate score. In the process of intelligent warehousing equipment construction and coordinated decision-making, when the data processing evaluation index increases, the collaborative work rate of the warehousing equipment is improved, so that collaborative decisions can be made more quickly. Conversely, the collaborative work rate will be reduced. By considering the above indirect influence mechanism, it is helpful to improve the evaluation efficiency and accuracy of the collaborative ability between warehousing equipment, thereby achieving the improvement of the reliability of collaborative performance evaluation in the process of intelligent warehousing equipment construction and coordinated decision-making, and effectively solving the problem of low reliability of collaborative performance evaluation in the process of intelligent warehousing equipment construction and coordinated decision-making in the existing technology.

[0072] Furthermore, the specific process of judging whether to adjust the collaboration strategy parameters based on the obtained collaboration strategy effect evaluation value is as follows: judging whether the obtained collaboration strategy effect evaluation value is within the collaboration strategy effect evaluation threshold range: if the obtained collaboration strategy effect evaluation value is within the collaboration strategy effect evaluation threshold range (including the case where it is equal to the maximum and minimum values ​​of the historical collaboration strategy effect evaluation values), then it indicates that the collaboration performance of the storage equipment within the preset time period meets the expected requirements, and the preset personnel is not prompted to adjust the collaboration strategy parameters; if the obtained collaboration strategy effect evaluation value is not within the collaboration strategy effect evaluation threshold range, then it indicates that the collaboration performance of the storage equipment within the preset time period meets the expected requirements and the preset personnel is prompted to adjust the collaboration strategy parameters.

[0073] In this embodiment, the collaborative strategy effect evaluation threshold range represents the range corresponding to the maximum and minimum values ​​of the historical collaborative strategy effect evaluation values ​​of the historical storage equipment in the historical time period in the preset database. The collaborative strategy parameters generally include equipment layout parameters and logistics path parameters. When the acquired collaborative strategy effect evaluation value is not within the collaborative strategy effect evaluation threshold range, the preset personnel adjust the collaborative strategy parameters by changing the equipment layout parameters and logistics path parameters on the control panel of the controller in the storage equipment. Each time an adjustment is made, it is necessary to re-acquire the collaborative strategy effect evaluation value until the acquired collaborative strategy effect evaluation value is within the collaborative strategy effect evaluation threshold range, thereby improving the accuracy and reliability of the collaborative performance evaluation between storage equipment.

[0074] It should be understood that the specific process of real-time monitoring of the actual execution of storage equipment under collaborative decision-making is: real-time monitoring of the actual collaborative order of storage equipment within a preset time period, and at the same time real-time monitoring of the scheduling errors of storage equipment within the preset time period to obtain a scheduling error score, where the scheduling error score is the ratio of the number of scheduling errors to the total number of scheduling errors; real-time monitoring of the scheduling delays of storage equipment within the preset time period to obtain the actual scheduling delay duration, where the actual scheduling delay duration is the total duration of the scheduling operation performed by the storage equipment within the preset time period.

[0075] Furthermore, the specific process of real-time monitoring of the actual execution of the storage equipment under the collaborative decision-making to determine whether to trigger the emergency response mechanism is as follows: determine whether the actual collaborative order is the same as the collaborative order in the collaborative decision. If so, continue to monitor the actual execution of the storage equipment under the collaborative decision, otherwise trigger the suspension of operation program; determine whether the scheduling error score is not greater than the preset scheduling error score. If so, continue to monitor the actual execution of the storage equipment under the collaborative decision, otherwise trigger the suspension of operation program and prompt the preset personnel to perform maintenance; determine whether the actual scheduling delay time is less than the maximum allowable scheduling delay time (including the maximum and minimum values ​​of the historical scheduling delay time in the historical scheduling process). If so, continue to monitor the actual execution of the storage equipment under the collaborative decision, otherwise trigger the suspension of operation program and switch to the backup storage equipment; when the actual collaborative order is different from the collaborative order in the collaborative decision and the scheduling error score is greater than the preset scheduling error score and the actual scheduling delay time is not less than the maximum allowable scheduling delay time, it indicates that the collaborative decision is unqualified and the collaborative decision is readjusted.

[0076] In this embodiment, the preset scheduling error score is represented by the sum and average of the historical scheduling error scores of the storage equipment in the preset database in the historical scheduling process, and the maximum allowable scheduling delay time is the range corresponding to the maximum and minimum values ​​of the historical scheduling delay time of the storage equipment in the preset database in the historical scheduling process; this example introduces real-time monitoring and emergency response mechanisms, which can instantly detect abnormal situations of storage equipment in the collaborative execution process, such as incorrect collaboration sequence (that is, the actual collaboration sequence is different from the collaboration sequence in the collaborative decision), excessive scheduling error scores (that is, the scheduling error score is greater than the preset scheduling error score) or too long scheduling delay (that is, the actual scheduling delay time is not less than the maximum allowable scheduling delay time), thereby quickly triggering the emergency response mechanism, such as suspending the running program or switching to backup equipment, effectively preventing the occurrence of potential failures and ensuring the safety and continuity of warehousing operations.

[0077] like Figure 2As shown, it is a flow chart of a collaborative decision-making method for intelligent warehousing equipment based on digital twins provided in an embodiment of the present application. The collaborative decision-making method for intelligent warehousing equipment based on digital twins provided in an embodiment of the present application includes the following steps: Step 1, mapping the physical warehousing equipment and the three-dimensional digital model of the warehousing equipment to construct a warehousing digital twin model, and monitoring the real-time data synchronization in the constructed warehousing digital twin model in real time to obtain a real-time data synchronization effectiveness score. The warehousing digital twin model is used to visualize the real-time data mapping relationship between the physical warehousing equipment and the three-dimensional digital model of the warehousing equipment. The real-time data synchronization effectiveness score is used to evaluate the real-time synchronization effectiveness of the real-time data between the physical warehousing equipment and the three-dimensional digital model of the warehousing equipment; Step 2, according to the obtained real-time data synchronization effectiveness score, the real-time synchronization data of the warehousing digital twin model within a preset time period is collected and data processing is performed, and at the same time, a data processing evaluation index is obtained based on the changes in real-time operating parameters during the data processing process. The real-time synchronization data is the warehousing digital twin data. The real-time data successfully synchronized in the word twin model, the data processing evaluation index is used to evaluate the data processing reliability of the real-time synchronized data within the preset time period; step three, build a graph structure according to the particle swarm optimization algorithm in the swarm intelligence algorithm and simulate the collaboration process of the storage equipment in the preset scenario (usually including warehousing, outbound, inventory and sorting) to obtain the collaboration strategy effect evaluation value, and judge whether to adjust the collaboration strategy parameters based on the acquired collaboration strategy effect evaluation value. The graph structure is used to visualize the connection relationship, dependency relationship and communication path between the storage equipment. The collaboration strategy effect evaluation value is used to evaluate the collaborative performance of the storage equipment within the preset time period. The collaboration strategy parameters include working mode and communication protocol; step four, generate collaborative decisions according to the adjusted collaboration strategy parameters, monitor the actual execution of the storage equipment under the collaborative decision in real time to determine whether to trigger the emergency response mechanism. The collaborative decision includes the scheduling of the storage equipment and the collaboration sequence between the storage equipment. The emergency response mechanism includes suspending operation, switching to spare storage equipment and adjusting the collaborative decision.

[0078] Among them, in actual applications, real-time data usually includes: the real-time inventory of the storage workshop; whether the conveyor and stacker in the storage equipment are running (if the control panel in the controller displays 1, it is running, and if it displays 0, it is stopped), the time, quantity and location of the goods entering or leaving the warehouse, storage operation data (such as the sorting volume, packaging volume and loading volume within a preset time period), the preset number of personnel in the storage workshop, and the movement trajectory (monitored in real time by the camera in the storage workshop). It should be noted that the above real-time data is consistent with the unit of the corresponding reference value; Figure 3 As shown, it is a specific generation flow chart of collaborative decision-making provided in the embodiment of the present application, through Figure 3The synergy in the collaborative process of warehousing equipment has realized the intelligent management and optimized scheduling of warehousing equipment, and improved the overall efficiency of warehousing operations. At the same time, through the introduction of real-time data synchronization, data processing evaluation, collaborative strategy optimization and emergency response mechanism, it has improved resource utilization efficiency, thereby achieving the improvement of the reliability of collaborative performance evaluation in the process of intelligent warehousing equipment construction and coordinated decision-making.

[0079] To summarize, the embodiment of the present application monitors the real-time data synchronization status in the constructed warehouse digital twin model in real time to obtain the real-time data synchronization effectiveness score, and processes the real-time synchronized data to obtain the data processing evaluation index, and then determines whether to adjust the collaboration strategy parameters based on the obtained collaboration strategy effect evaluation value, and finally generates a collaborative decision based on the adjusted collaboration strategy parameters and determines whether to trigger the emergency response mechanism, thereby achieving a more accurate evaluation of the real-time data synchronization status in the warehouse digital twin model, and then achieving an improvement in the reliability of collaborative performance evaluation in the process of building and coordinating decision-making of intelligent warehousing equipment, effectively solving the problem of low reliability of collaborative performance evaluation in the process of building and coordinating decision-making of intelligent warehousing equipment in the prior art.

[0080] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0084] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0085] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A collaborative decision-making system for intelligent warehousing equipment based on digital twins, characterized in that: include: Digital twin synchronization module, data processing module, equipment collaboration strategy optimization module, and decision execution module; The digital twin synchronization module is used to monitor the real-time data synchronization status in the constructed warehouse digital twin model in real time to obtain a real-time data synchronization effectiveness score, and the real-time data synchronization effectiveness score is used to evaluate the real-time synchronization effectiveness of real-time data between the physical warehouse equipment and the three-dimensional digital model of the warehouse equipment; The data processing module is used to collect the real-time synchronization data of the warehouse digital twin model within a preset time period and perform data processing according to the obtained real-time data synchronization effectiveness score, and at the same time obtain a data processing evaluation index based on the changes of real-time operation parameters during the data processing process. The data processing evaluation index is used to evaluate the data processing reliability of the real-time synchronization data within the preset time period; The equipment collaboration strategy optimization module is used to construct a graph structure and simulate the collaboration process of storage equipment in a preset scenario to obtain a collaboration strategy effect evaluation value, and determine whether to adjust the collaboration strategy parameters based on the acquired collaboration strategy effect evaluation value. The collaboration strategy effect evaluation value is used to evaluate the collaborative performance of the storage equipment within a preset time period; The decision execution module is used to generate collaborative decisions according to the adjusted collaborative strategy parameters, and monitor the actual execution of storage equipment under the collaborative decision in real time to determine whether to trigger the emergency response mechanism.

2. The intelligent warehousing equipment collaborative decision-making system based on digital twins as claimed in claim 1, characterized in that: The specific steps for obtaining the real-time data synchronization effectiveness score are as follows: Count the amount of real-time monitoring data in the warehouse digital twin model within a preset time period, and determine whether the amount of real-time monitoring data is greater than the preset amount of real-time monitoring data. If so, obtain the real-time status monitoring score in combination with the total amount of real-time data. Otherwise, prompt the preset personnel to inspect and re-monitor the monitoring equipment corresponding to the real-time data in the warehouse digital twin model. The total amount of real-time data is the sum of the amount of real-time data in the physical warehouse equipment and the amount of real-time data in the three-dimensional digital model. The real-time status monitoring score is used to evaluate the accuracy of the warehouse digital twin model in real-time status monitoring of the warehouse equipment; Real-time monitoring of the synchronization delay of real-time data in the warehouse digital twin model within a preset time period and combining the corresponding expected synchronization duration to obtain the warehouse equipment compliance score, which is used to evaluate the similarity between the physical warehouse equipment and the three-dimensional digital model of the warehouse equipment; Obtaining the average delay time of real-time data within a preset time period and counting the total amount of real-time data in the physical storage equipment and the amount of data successfully synchronized with the real-time data in the three-dimensional digital model to obtain a data synchronization efficiency score, which is used to evaluate the data synchronization efficiency between the physical storage equipment and the three-dimensional digital model of the storage equipment; The real-time data synchronization effectiveness score is obtained by combining the obtained real-time status monitoring score, storage equipment compliance score and data synchronization efficiency score.

3. The intelligent warehousing equipment collaborative decision-making system based on digital twins as claimed in claim 2, characterized in that: The specific limiting expression of the real-time data synchronization effectiveness score is: ； Where t is the number of the preset time period, , T is the total number of preset time periods, e is a natural constant, It represents the real-time data synchronization effectiveness score of the real-time data in the warehouse digital twin model within the t-th preset time period. It represents the real-time status monitoring score of the real-time data in the warehouse digital twin model in the t-th preset time period. It represents the amount of real-time monitoring data in the warehouse digital twin model within the t-th preset time period. Indicates the preset real-time monitoring data volume, It represents the compliance score of the real-time data in the warehouse digital twin model within the t-th preset time period. Represents the data synchronization efficiency score of the real-time data in the warehouse digital twin model within the tth preset time period.

4. The intelligent warehousing equipment collaborative decision-making system based on digital twins as claimed in claim 1, characterized in that: The step of obtaining the real-time data synchronization validity score further includes determining whether to execute the function of the data processing module based on the obtained real-time data synchronization validity score. The specific determination process is as follows: Determine whether the obtained real-time data synchronization validity score is within the real-time data synchronization threshold range: If the obtained real-time data synchronization validity score is within the real-time data synchronization threshold range, the function of the data processing module is executed; If the obtained real-time data synchronization validity score is not within the real-time data synchronization threshold range, the function of the data processing module is not executed and the function of the digital twin synchronization module is re-executed.

5. The intelligent warehousing equipment collaborative decision-making system based on digital twins as claimed in claim 1, characterized in that: The specific process of obtaining the data processing evaluation index is as follows: Count the amount of successfully collected data of real-time synchronization data within a preset time period and the total data collection time corresponding to the collection of these data amounts, and obtain a data collection efficiency score based on the total amount of real-time synchronization data within the preset time period; Real-time monitoring of the data processing process of real-time synchronization data within a preset time period to obtain data transmission scores, data cleaning scores and data storage scores, and at the same time combining the obtained data collection efficiency scores and data storage rates to obtain a data processing evaluation index; The data collection efficiency score is the product of the ratio of the amount of successfully collected data in the real-time synchronization data within a preset time period to the total data collection time and the data collection accuracy rate; The data collection accuracy is the ratio of the amount of successfully collected data in the real-time synchronization data to the total amount of real-time synchronization data to be collected; The data transmission score is the ratio of the amount of successfully transmitted data to the amount of successfully collected data to be transmitted within a preset time period; The data cleaning score is the ratio of the amount of successfully cleaned data to the amount of successfully transmitted data to be cleaned within a preset time period; The data storage score is the product of the ratio of the amount of successfully stored data to the total capacity of the storage device within a preset time period and the inverse of the total data storage duration; The data storage rate is the ratio of the amount of successfully stored data to the amount of successfully collected data.

6. The intelligent warehousing equipment collaborative decision-making system based on digital twins as claimed in claim 1, characterized in that: The collaborative strategy effect evaluation value is obtained by the following method: Count the number of collaborative work between storage devices within a preset time period and the corresponding collaborative work duration, and determine whether the collaborative work duration is not greater than the preset collaborative work duration. If so, obtain the collaborative work number score and the collaborative work duration score; The actual collaborative workload of the storage equipment within the preset time period is counted and combined with the amount of collaborative tasks to be coordinated to obtain the collaborative work rate score. At the same time, the obtained data processing evaluation index, collaborative work frequency score and collaborative work duration score are combined to obtain the collaborative strategy effect evaluation value; The collaborative work frequency score is the ratio of the absolute value of the difference between the collaborative work frequency and the reference collaborative work frequency to the reference collaborative work frequency; The collaborative working time score is the ratio of the difference between the preset collaborative working time and the collaborative working time to the preset collaborative working time; The collaborative work rate score is the ratio of the actual collaborative work volume to the amount of tasks to be collaboratively performed.

7. The intelligent warehousing equipment collaborative decision-making system based on digital twins as claimed in claim 1, characterized in that: The specific process of judging whether to adjust the collaborative strategy parameters based on the acquired collaborative strategy effect evaluation value is as follows: Determine whether the obtained collaborative strategy effect evaluation value is within the collaborative strategy effect evaluation threshold range: If the obtained collaborative strategy effect evaluation value is within the collaborative strategy effect evaluation threshold range, the preset personnel will not be prompted to adjust the collaborative strategy parameters; If the obtained collaboration strategy effect evaluation value is not within the collaboration strategy effect evaluation threshold range, the preset personnel will be prompted to adjust the collaboration strategy parameters.

8. The intelligent warehousing equipment collaborative decision-making system based on digital twins as claimed in claim 1, characterized in that: The specific process of real-time monitoring of the actual execution of storage equipment under collaborative decision-making is as follows: Real-time monitoring of the actual coordination order of the storage equipment within a preset time period, and real-time monitoring of the scheduling errors of the storage equipment within the preset time period to obtain a scheduling error score, where the scheduling error score is the ratio of the number of scheduling errors to the total number of scheduling errors; The scheduling delay of the storage equipment within the preset time period is monitored in real time to obtain the actual scheduling delay duration, where the actual scheduling delay duration is the total duration of the scheduling operation performed by the storage equipment within the preset time period.

9. The intelligent warehousing equipment collaborative decision-making system based on digital twins as claimed in claim 1, characterized in that: The specific process of real-time monitoring of the actual execution of storage equipment under collaborative decision-making to determine whether to trigger the emergency response mechanism is as follows: Determine whether the actual coordination order is the same as the coordination order in the collaborative decision. If so, continue to monitor the actual execution of the storage equipment under the collaborative decision. Otherwise, trigger the suspension program. Determine whether the scheduling error score is not greater than the preset scheduling error score. If so, continue to monitor the actual execution of the storage equipment under the collaborative decision-making. Otherwise, trigger the suspension program and prompt the preset personnel to perform maintenance. Determine whether the actual scheduling delay is less than the maximum allowed scheduling delay. If so, continue to monitor the actual execution of the storage equipment under the collaborative decision-making. Otherwise, trigger the suspension of the running program and switch to the backup storage equipment. When the actual coordination order is different from the coordination order in the coordinated decision and the scheduling error score is greater than the preset scheduling error score and the actual scheduling delay duration is not less than the maximum allowable scheduling delay duration, it indicates that the coordinated decision is unqualified and the coordinated decision is readjusted.

10. A collaborative decision-making method for intelligent warehousing equipment based on digital twins, characterized in that: The following steps are involved: Step 1: monitor the real-time data synchronization status in the constructed warehouse digital twin model in real time to obtain a real-time data synchronization effectiveness score, where the real-time data synchronization effectiveness score is used to evaluate the real-time synchronization effectiveness of real-time data between the physical warehouse equipment and the three-dimensional digital model of the warehouse equipment; Step 2: According to the obtained real-time data synchronization effectiveness score, the real-time synchronization data of the warehouse digital twin model within a preset time period is collected and data processing is performed, and at the same time, based on the changes of real-time operation parameters during the data processing process, a data processing evaluation index is obtained, and the data processing evaluation index is used to evaluate the data processing reliability of the real-time synchronization data within the preset time period; Step 3: construct a graph structure and simulate the collaboration process of storage equipment in a preset scenario to obtain a collaboration strategy effect evaluation value, and determine whether to adjust the collaboration strategy parameters based on the acquired collaboration strategy effect evaluation value, wherein the collaboration strategy effect evaluation value is used to evaluate the collaborative performance of the storage equipment within a preset time period; Step 4: Generate collaborative decisions based on the adjusted collaborative strategy parameters, and monitor the actual execution of storage equipment under the collaborative decision in real time to determine whether to trigger the emergency response mechanism.

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