A power warehouse operation simulation and bottleneck diagnosis system based on digital twinning

Abstract

The application belongs to the technical field of electric power storage, and discloses a power warehouse operation simulation and bottleneck diagnosis system based on digital twinning. A multi-source perception architecture is constructed through an acquisition and adaptation module, covering full-factor data such as equipment operation and personnel operation. Combined with a five-dimension fusion model of a twinning modeling module and a laser scanning modeling technology, the physical space and operation logic of the power warehouse are accurately restored. A simulation and deduction module adopts a fusion simulation algorithm and a double-scene template library to realize linkage simulation of the whole process and control instructions of warehousing, storage, picking, and delivery, can simulate the operation state under different scenes, and avoid efficiency problems such as equipment waiting and path redundancy in advance. A bottleneck diagnosis module constructs a three-dimension evaluation system of operation state, control parameters, and equipment performance, integrates real-time working condition data and simulation analysis results, scientifically distributes index weights through an analytic hierarchy process, and realizes quantitative division of bottleneck grades.

Description

Technical Field

[0001] This invention belongs to the field of power storage technology, specifically a power warehouse operation simulation and bottleneck diagnosis system based on digital twins. Background Technology

[0002] Power warehouses are a crucial link in the material supply of the power system, responsible for the storage, allocation, and management of various types of materials, including transmission and transformation equipment, emergency repair tools, and safety protective equipment. Their operational efficiency directly impacts the safe and stable operation of the power system and the speed of emergency repair response. With the rapid development of the power industry, the demand for power materials is increasingly characterized by diverse categories, uneven batch sizes, and high emergency response requirements. The existing power warehouse operation and management model mainly faces the following technical problems: Power warehouse operations include multiple stages such as receiving, storage, picking, and outbound. The coordination of each stage mainly relies on human experience and judgment, lacking global simulation and advance prediction of the entire operation process. This easily leads to efficiency problems such as equipment waiting, personnel idleness, and path redundancy, forming operational bottlenecks. In particular, the process congestion problem is more prominent in high-intensity operation scenarios such as emergency repairs.

[0003] Existing technologies mostly identify operational bottlenecks through post-event statistical analysis, which cannot achieve real-time bottleneck location and root cause tracing. Furthermore, the diagnostic results are greatly affected by human experience and are difficult to adapt to the dynamically changing operating environment of power warehouses, resulting in a lack of targeted optimization measures.

[0004] Digital twin technology has been initially applied in the warehousing field, but its implementation in the power warehouse scenario still faces the following problems: existing digital twin warehousing systems are mostly geared towards general warehousing scenarios and have failed to fully adapt to the special attributes of power materials and the exclusive operating processes of power warehouses; simulation functions are mostly limited to the simulation of single operating links and lack the ability to conduct collaborative simulation of the entire power warehouse operation process; in addition, bottleneck diagnosis and optimization decision-making are disconnected, and a closed-loop control link of simulation diagnosis, optimization decision-making, and execution feedback has not been established. Summary of the Invention

[0005] The purpose of this invention is to provide a digital twin-based power warehouse operation simulation and bottleneck diagnosis system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a power warehouse operation simulation and bottleneck diagnosis system based on digital twins, including a data acquisition and adaptation module, a twin modeling module, a simulation and deduction module, a bottleneck diagnosis module, an optimization decision-making module, a virtual-real linkage module, and a monitoring and maintenance module; Preferably, the acquisition and adaptation module adopts a multi-source acquisition architecture consisting of sensors, RFID reading and writing devices, visual recognition devices, and industrial IoT gateways to collect all elements of power storage data, including stacker crane servo motor operating parameters, AGV drive system control signals, high-voltage protection area access control status, large equipment hoisting actuator action data, as well as basic data such as personnel operation, equipment operation, and inventory information. An integrated industrial heterogeneous protocol adaptation layer enables millisecond-level communication between heterogeneous devices. It adapts high-precision acquisition channels for large power storage equipment, configures edge control nodes, and simultaneously performs data cleaning, noise reduction, format standardization, redundancy removal, and control command preprocessing. It also converts cloud-based optimized decision commands into control signal formats that the devices can recognize. The format standardization adopts a unified JSON data structure, which includes data identifier, timestamp, numerical value, and precision level fields to ensure the consistency of data format collected by different devices.

[0007] An adaptive acquisition and control linkage mechanism is designed to dynamically bind the data acquisition frequency and control cycle. In emergency operation scenarios, a 10Hz acquisition frequency and a 1ms control cycle are used in coordination, while in regular operation scenarios, a 2Hz acquisition frequency and a 10ms control cycle are used. The time deviation of the entire system is ≤1μs through IEEE1588 clock synchronization technology to ensure the timing consistency of the acquired data and control commands.

[0008] The IEEE 1588 clock synchronization technology adopts a master-slave clock architecture. The master clock is deployed at the edge control node, and the slave clock is integrated into each sensing device and control unit. The synchronization period is set to 50ms. The network transmission delay is corrected through delay measurement and compensation algorithms. The time deviation calibration trigger condition is: when the master-slave clock deviation is >0.5μs, calibration is automatically started to ensure that the time deviation of the entire system is stable at ≤1μs, which is suitable for the high-precision timing requirements of power storage control commands.

[0009] The standard for the deployment density of multi-source sensing devices is: sensors every 5m 2 Each unit covers the warehouse operation area, with RFID reading and writing devices deployed at the inbound and outbound entrances and shelf aisle nodes at intervals of ≤10m. Visual recognition devices focus on key scenarios such as high-voltage protection areas and large equipment hoisting stations. The high-precision acquisition channel has a sampling bit depth of 16 bits and a sampling rate of ≥100kHz, enabling continuous acquisition of key parameters of core equipment such as stacker crane servo motors and AGV drive systems without data loss.

[0010] Preferably, the twin modeling module constructs a five-dimensional integrated twin model based on standardized data output by the acquisition and adaptation module, encompassing geometry, physics, behavior, timing, and control. The geometric model uses laser scanning modeling technology to recreate the equipment installation location, transmission links, and warehouse spatial layout, ensuring the correspondence between the virtual and physical spaces. The physical model incorporates power equipment control constraints, including power storage-specific attributes such as stacker crane servo motor load limits, high-voltage equipment operation timing constraints, and insulation protection requirements, as well as general parameters such as equipment motion mechanics characteristics. The behavior model depicts the interaction logic of equipment control actions based on the actual operation process of the power warehouse. The timing model records the evolution trajectory of control parameters as the operation progresses. The control model embeds equipment control algorithms to achieve virtual simulation and pre-verification of control commands. The system employs a dual-drive model update engine that combines data and control. It updates the device status based on real-time data collected from edge control nodes and dynamically corrects model parameters by combining control command execution feedback. This ensures the consistency of control between the virtual model and the physical device. For large-scale power storage equipment models, a layered optimization strategy of geometric lightweighting and high-precision control parameters is adopted. Polygon simplification is performed on non-core control areas of the equipment, while model details are preserved for key control components such as servo motors and positioning mechanisms. This achieves a balance between the accuracy of control simulation and the efficiency of model operation.

[0011] The specific implementation standards for the hierarchical optimization strategy are as follows: Non-core control areas such as equipment shells and frame structures adopt polygon simplification algorithms, and the number of faces in the simplified model is ≤30% of the original model, with a geometric error ≤5mm; Key control components such as servo motors, positioning mechanisms, and transmission gears retain the details of the original model, with a geometric error ≤0.1mm; Key components are determined based on those that directly participate in the execution of control commands or parameter feedback, and are associated with and labeled by the control dimension parameters of the five-dimensional model; The specific determination is made through the execution association identifier field in the control dimension parameters of the five-dimensional model, and if the identifier is correct, it is determined to be a key control component.

[0012] Preferably, the simulation and deduction module is based on a five-dimensional model constructed by the twin modeling module. It adopts a simulation algorithm that integrates discrete event simulation and control command timing simulation, abstracts personnel, equipment, and control units into intelligent agents with autonomous decision-making capabilities, presets differentiated operation rules for each intelligent agent, constructs a control rule library and incorporates dedicated control logic for power warehousing, and realizes the linkage simulation of the entire process of warehousing acceptance, storage shelving, picking verification, and outbound scheduling with control commands, thereby simulating the impact of different control parameters on operation efficiency and quantifying and analyzing the efficiency fluctuation pattern. The operational rules for different types of intelligent agents are set differently according to their functional characteristics. For example, human intelligent agents focus on operational process compliance rules, equipment intelligent agents focus on motion control and safety constraint rules, and control unit intelligent agents focus on instruction coordination rules.

[0013] A dual-scenario template library is built, which includes operational scenarios such as routine operations, emergency repairs, material allocation, and equipment maintenance, as well as control strategy verification scenarios such as multi-AGV collaborative control conflict avoidance and stacker crane positioning accuracy optimization. Users can customize the scenario parameters according to their actual needs. It has a control command pre-execution function and can fully simulate the command execution process in a virtual environment to verify feasibility. The simulation results output includes both operational efficiency and control performance indicators. The control performance indicators include control response time, positioning error, and command execution success rate.

[0014] Preferably, the bottleneck diagnosis module combines the real-time operating data from the acquisition and adaptation module with the simulation analysis results from the simulation and deduction module to construct a three-dimensional bottleneck diagnosis system encompassing operation status, control parameters, and equipment performance. This system covers the three core dimensions of operation process, control logic, and equipment status. An evaluation system including operation indicators and control indicators is established. The operation indicators include equipment utilization rate, personnel operation efficiency, and material flow efficiency. The control indicators include control response efficiency, control accuracy stability, and control energy consumption. The control indicators also include secondary indicators such as servo motor positioning error fluctuation rate and AGV path tracking deviation. The weights of the indicators are determined using the analytic hierarchy process (AHP) based on the characteristics of power warehousing operations, thereby achieving a quantitative assessment of the bottleneck level. A diagnostic method that combines real-time operating condition monitoring and control simulation prediction is adopted to calculate the processing efficiency and control parameter matching degree of the operation in real time. Combined with the control parameter change prediction data output by the simulation module, potential bottlenecks can be identified in advance. By introducing a control causal reasoning algorithm, based on the interaction trajectory of operation time sequence data and control commands, the root cause of bottlenecks is traced, and a diagnostic report containing specific optimization suggestions and implementation steps is generated.

[0015] The weight allocation steps of the analytic hierarchy process are as follows: ① The target layer is set as a bottleneck level quantitative assessment, the criterion layer consists of three dimensions: operation status, control parameters, and equipment performance, and the indicator layer consists of specific indicators under each dimension, including secondary indicators; ② A judgment matrix is ​​constructed based on the priority of power storage operations, with emergency operations > routine operations and control accuracy > energy consumption. The rationality of the matrix is ​​verified by consistency test (CR < 0.1). ③ Final weight allocation reference: 40% for operation status dimension, 35% for control parameter dimension, and 25% for equipment performance dimension. The weight of secondary indicators is allocated according to the importance ratio within their respective primary dimensions. For example, among control indicators, control response efficiency accounts for 12%, control accuracy and stability accounts for 12%, and control energy consumption accounts for 11%.

[0016] Preferably, the optimization decision module, based on the diagnostic report and optimization suggestions output by the bottleneck diagnosis module, adopts a multi-objective optimization algorithm (specifically, the non-dominated sorting genetic algorithm NSGA-Ⅲ) with the objectives of operation efficiency, control accuracy, and energy consumption. It balances the various optimization objectives through a weight allocation mechanism and combines the power storage-specific control constraints such as high-voltage area operation control authority, large equipment hoisting speed limit, and priority scheduling of emergency materials to generate a comprehensive scheme that combines operation optimization and control stability. The core output of the scheme is the linkage result between the control optimization command and the operation adjustment scheme. Configure a control command conflict detection and resolution engine, perform full-scenario pre-simulation of generated control commands based on a five-dimensional twin model, detect timing conflicts of control commands from multiple devices, and resolve conflicts through control timing rearrangement and priority allocation; The design optimizes the automatic conversion module for decision and control instructions, and incorporates a mainstream PLC device instruction protocol library. It can parse optimization schemes into PLC executable control code and supports one-click distribution to edge control nodes.

[0017] Preferably, the virtual-real linkage module receives control commands from the optimization decision module, constructing a three-tier industrial control architecture consisting of a cloud decision layer, an edge control layer, and an equipment execution layer. The cloud is used for global optimization decision-making and control strategy planning, and synchronously coordinates all warehouse operation data resources. The edge control node integrates a PLC core control unit, receives cloud control commands, and executes real-time control tasks such as stacker crane positioning control and AGV motion control, while synchronously collecting equipment execution feedback data. The equipment execution layer is interconnected with the edge control node through an industrial bus. An adaptive control execution adjustment mechanism is designed. Based on the equipment execution feedback data, the control parameters are dynamically adjusted through an adaptive algorithm to compensate for the deviation between the model and the physical entity. The core adjustment logic of the adaptive algorithm is: ① Adjustment is initiated when the deviation between the feedback data and the target parameters exceeds the preset threshold, specifically when the positioning deviation exceeds 3mm, the speed deviation exceeds 5% of the rated value, and the energy consumption deviation exceeds 8%. ② When the deviation is 3-5mm (or 5%-10%), use proportional control (Kp=0.8-1.2); when the deviation is >5mm (or >10%), use proportional-integral control (Kp=1.2-1.5, Ki=0.1-0.3); when the energy consumption deviation is 8%-15%, use proportional control (Kp=0.7-1.0); when the energy consumption deviation is >15%, use proportional-integral control (Kp=1.0-1.3, Ki=0.08-0.2). ③ The adjusted control parameters must not exceed the rated range of the power equipment. For example, the positioning control voltage of the stacker crane should be ≤220V and the speed of the AGV drive motor should be ≤3000rpm to avoid overloading the equipment.

[0018] Establish a closed-loop control link encompassing simulation diagnosis, optimization decision-making, control execution, and feedback correction. Real-time data from equipment execution is transmitted back to the twin modeling module for model updates, while simultaneously being synchronized to the monitoring and maintenance module for real-time status monitoring. This triggers model updates and secondary simulations to verify the effectiveness of control optimization.

[0019] Preferably, the monitoring and maintenance module integrates real-time data from the acquisition and adaptation module, execution feedback data from the virtual-real linkage module, and core status information of each module. It adopts industrial-grade 3D visualization technology to construct a multi-dimensional interactive monitoring panel, which displays the operation status, bottleneck location, equipment operating parameters, and core information of the control dimension in real time. The control dimension information includes real-time curves of control parameters, execution status of control commands, and ranking of equipment control performance indicators. It supports multi-view switching monitoring such as global, local, and equipment close-up. A hierarchical intelligent early warning and alarm mechanism is established, and early warning thresholds are set according to the power storage safety specifications. These thresholds include early warnings for operational bottlenecks and abnormal control parameters. Differentiated alarms are executed according to the level of the alarm. Emergency alarms trigger audible and visual alarms, information push notifications, and safety shutdown commands. Important alarms trigger pop-up prompts with control parameter adjustment plans.

[0020] Early warning threshold setting standards: ① Operational bottleneck thresholds: Equipment utilization rate <30% or >85%, personnel work efficiency <20 pieces / hour, material flow efficiency <5 tons / hour (normal scenario), and material flow efficiency <10 tons / hour triggers an early warning in emergency scenarios; ② Abnormal thresholds for control parameters: control response time > 50ms, positioning error > 5mm, servo motor positioning error fluctuation rate > 3%, AGV path tracking deviation > 4mm; ③ Classification criteria: General alarm (single indicator exceeds the threshold but does not affect operation), Important alarm (two or more indicators exceed the threshold or key indicators deviate by 10%-20%), Emergency alarm (key indicator deviation > 20% or involves high voltage safety or equipment failure).

[0021] The beneficial effects of this invention are as follows: 1. This invention constructs a multi-source perception architecture through a data acquisition and adaptation module, covering all elements of data such as equipment operation and personnel operation. Combined with the five-dimensional fusion model of the twin modeling module and laser scanning modeling technology, it accurately restores the physical space and operation logic of the power warehouse. The simulation and deduction module adopts a fusion simulation algorithm and a dual-scenario template library to realize the linkage simulation of the entire process of warehousing, storage, picking, and warehousing with control commands. It can simulate the operation status under different scenarios and avoid efficiency problems such as equipment waiting and path redundancy in advance. Especially in high-load scenarios such as emergency repairs, it can avoid process congestion and improve operation coordination.

[0022] 2. The bottleneck diagnosis module of this invention constructs a three-dimensional evaluation system of operation status, control parameters, and equipment performance, integrates real-time operating data and simulation analysis results, and scientifically allocates index weights through the analytic hierarchy process to achieve quantitative classification of bottleneck levels. It adopts a diagnostic method that integrates real-time monitoring and simulation prediction, which can identify potential bottlenecks in advance and use control causal reasoning algorithms to trace the root cause, clearly distinguish the causes of different types of bottlenecks such as equipment, personnel, and parameter settings, and generate optimization suggestions containing specific implementation steps, thus solving the problems of traditional diagnosis lacking specificity and poor adaptability.

[0023] 3. This invention deeply integrates the unique attributes of power storage, embedding specific constraints such as high-voltage protection and large equipment hoisting restrictions into each stage of modeling, simulation, and optimization; the optimization decision module balances efficiency, accuracy, and energy consumption through multi-objective algorithms, coupled with control command conflict detection and automatic conversion functions, to achieve rapid conversion of optimization schemes into executable commands for equipment; the three-level industrial control architecture and adaptive adjustment mechanism of the virtual-real linkage module ensure real-time synchronization of virtual and real data and dynamic parameter correction, forming a continuous iterative closed-loop optimization, improving the operational efficiency and control stability of power storage. Attached Figure Description

[0024] Figure 1 This is a flowchart of the overall system of the present invention; Figure 2 This is a flowchart of the data acquisition and adaptation process for this invention. Figure 3 This is a flowchart of the bottleneck diagnosis process of the present invention; Figure 4 This is a flowchart illustrating the virtual-real linkage and optimized execution process of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] like Figures 1 to 4 As shown in the figure, this embodiment of the invention provides a power warehouse operation simulation and bottleneck diagnosis system based on digital twins, including a data acquisition and adaptation module, a twin modeling module, a simulation and deduction module, a bottleneck diagnosis module, an optimization decision-making module, a virtual-real linkage module, and a monitoring and maintenance module. The specific implementation of each module is as follows: The data acquisition and adaptation module employs a multi-source acquisition architecture comprised of sensors, RFID readers, visual recognition devices, and industrial IoT gateways. It collects comprehensive data on all aspects of power warehousing, including stacker crane servo motor operating parameters, AGV drive system control signals, high-voltage protection area access control status, large equipment hoisting actuator action data, and basic data such as personnel operation, equipment operation, and inventory information. The stacker crane servo motor operating parameters include speed, torque, and positioning error, achieving comprehensive coverage of both control and operational data.

[0027] It integrates an industrial heterogeneous protocol adaptation layer to achieve millisecond-level high-efficiency communication between heterogeneous devices. It adapts high-precision acquisition channels for large power storage equipment, configures edge control nodes, and simultaneously realizes data cleaning, noise reduction, format standardization, redundancy removal and control command preprocessing functions. It also directly converts cloud-optimized decision commands into control signal formats that can be recognized by the equipment. An adaptive acquisition and control linkage mechanism is designed to dynamically bind the data acquisition frequency and control cycle. In emergency operation scenarios, a 10Hz acquisition frequency and a 1ms control cycle are used in coordination, while in regular operation scenarios, a 2Hz acquisition frequency and a 10ms control cycle are used. The time deviation of the entire system is ≤1μs through IEEE1588 clock synchronization technology to ensure the timing consistency of the acquired data and control commands.

[0028] The industrial heterogeneous protocol adaptation layer supports mainstream protocols including Modbus, Profibus, OPCUA, and EtherNet / IP, enabling seamless communication between heterogeneous devices through protocol conversion interfaces. The data preprocessing process of the edge control node is as follows: outliers are first removed using the 3σ criterion, and then the sliding window method is used to smooth and denoise the data. The redundancy removal criterion is that the data collected in three consecutive samples are repeated and the deviation is ≤0.1%, ensuring the validity of the output data.

[0029] The twin modeling module constructs a five-dimensional integrated twin model based on standardized data output from the acquisition and adaptation module, encompassing geometry, physics, behavior, timing, and control. The geometric model uses laser scanning modeling technology to recreate the installation location of control equipment, transmission links, and warehouse spatial layout, achieving millimeter-level modeling accuracy to ensure precise correspondence between virtual and physical spaces. The physical model incorporates power equipment control constraints, including power storage-specific attributes such as stacker crane servo motor load limits, high-voltage equipment operation timing constraints, and insulation protection requirements, as well as general parameters such as equipment motion mechanics characteristics. The behavior model depicts the interaction logic of equipment control actions based on the actual operation process of the power warehouse, such as the collaborative control timing of AGVs and elevators. The timing model records the evolution trajectory of control parameters with millisecond-level granularity as the operation progresses. The control model embeds equipment control algorithms to achieve virtual simulation and pre-verification of control commands. The system employs a dual-drive model update engine that combines data and control. It updates the device status based on real-time data collected from edge control nodes and dynamically corrects model parameters by combining control command execution feedback. This ensures the consistency between the virtual model and the physical device, with a control command response error of ≤1%. For large-scale power storage equipment models, a layered optimization strategy of geometric lightweighting and high-precision control parameters is adopted. Polygonal simplification is performed on non-core control areas of the equipment, while high-precision model details are retained for key control components such as servo motors and positioning mechanisms. This achieves a balance between the accuracy of control simulation and the efficiency of model operation.

[0030] The specific parameters of the laser scanning modeling technology are: scanning accuracy ±0.1mm, point cloud density 100 points / mm², scanning range covering the entire warehouse area, including key internal components of the equipment; the update cycle of the dual-drive model update engine is: 100ms / time in normal scenarios, 20ms / time in emergency scenarios, and the emergency update trigger condition is control command execution deviation >1% or sudden change in equipment status, ensuring that the virtual model and physical entity are synchronized in real time.

[0031] In the five-dimensional fusion twin model, the geometric model provides a spatial coordinate reference, the physical model calculates the motion constraints of the device based on this reference, the behavioral model calls the constraint parameters of the physical model to characterize the action logic, the temporal model records the execution trajectory of the behavioral model, and the control model synchronously reads the data of the first four dimensions for instruction simulation verification; the data interaction of each dimension is realized through a standardized data interface, with a data transmission latency of ≤10ms, ensuring the collaborative operation of the model.

[0032] The simulation and deduction module is based on a five-dimensional model constructed by the twin modeling module. It adopts a simulation algorithm that integrates discrete event simulation and control command timing simulation. It abstracts personnel, equipment, and control units into intelligent agents with autonomous decision-making capabilities, presets differentiated operation rules for each intelligent agent, constructs a control rule library, and incorporates power storage-specific control logic, such as large equipment hoisting anti-impact control rules, high-voltage area operation permission control rules, and emergency material priority scheduling rules. It realizes the linkage simulation of the entire process of warehousing acceptance, storage shelving, picking verification, and outbound scheduling, thereby simulating the impact of different control parameters on operation efficiency and quantifying the efficiency fluctuation pattern. The intelligent agent adopts a three-level module architecture of perception, decision-making, and execution. The perception module inputs real-time data of power storage, including equipment operating parameters, work task queues, and space occupancy status; the decision-making module embeds power storage-specific rules, such as high-voltage area operation priorities and emergency material dispatching logic; and the execution module outputs specific operation / control instructions. Intelligent agents can work together through task collaboration interfaces. For example, AGV intelligent agents and stacker crane intelligent agents can avoid path conflicts by sharing the occupation status data of the work area. The input data format of intelligent agents is uniformly JSON structure, which includes data type, timestamp, and precision identifier. The output instructions are directly mapped to the control dimension parameters of the five-dimensional twin model to ensure consistency between virtual simulation and physical execution.

[0033] A dual-scenario template library is built, which includes operational scenarios such as routine operations, emergency repairs, material allocation, and equipment maintenance, as well as control strategy verification scenarios such as conflict avoidance in multi-AGV collaborative control and stacker crane positioning accuracy optimization. Users can customize the scenario parameters according to their actual needs. It has a control command pre-execution function and can fully simulate the command execution process in a virtual environment to verify feasibility and avoid the risk of physical equipment misoperation. The simulation results output includes both operational efficiency and control performance indicators. The control performance indicators include control response time, positioning error, and command execution success rate.

[0034] The logic of combining discrete event simulation and control command timing simulation is as follows: discrete event simulation triggers the issuance of control commands, such as the AGV scheduling command triggered by the start of the inbound task; control command timing simulation synchronously simulates the command execution process, such as the AGV movement trajectory and changes in positioning accuracy; the two achieve real-time interaction through the event-command-feedback data link. The reference range for user-defined scenario parameters is: 50-500 pieces / batch of work tasks, 2-20 units of equipment, and control parameter thresholds of ±20% of the rated value.

[0035] The control rule base adopts a three-level storage structure of scenario, device, and action. The first-level directory is classified according to the operation scenario, including routine scenario, emergency scenario, maintenance scenario, etc. The second-level directory is divided according to the device type, including AGV equipment, stacker crane, high-voltage equipment, etc. The third-level directory stores the specific action rule call trigger conditions, which are scenario matching and device status satisfaction. For example, in the emergency scenario, the AGV triggers the rule of giving priority to avoiding the emergency repair material transportation channel, and the call response time is ≤5ms to ensure that the simulation and actual operation logic are consistent.

[0036] The bottleneck diagnosis module combines real-time operating data from the acquisition and adaptation module with simulation analysis results from the simulation and deduction module to construct a three-dimensional bottleneck diagnosis system encompassing operational status, control parameters, and equipment performance. This system covers three core dimensions: operational process, control logic, and equipment status. It establishes an evaluation system including operational indicators and control indicators. The operational indicators include traditional operational indicators such as equipment utilization rate, personnel operational efficiency, and material flow efficiency. The control indicators include indicators such as control response efficiency, control accuracy stability, and control energy consumption. The control indicators also include secondary indicators such as servo motor positioning error fluctuation rate and AGV path tracking deviation. The weights of the indicators are determined using the analytic hierarchy process (AHP) based on the characteristics of power warehousing operations, enabling a quantitative assessment of bottleneck levels. Bottleneck levels are categorized as general, important, and urgent. A diagnostic method that combines real-time operational condition monitoring and control simulation prediction is adopted to calculate the processing efficiency and control parameter matching degree of the operation in real time. Combined with the control parameter change prediction data output by the simulation module, potential bottlenecks can be identified in advance, such as the sorting bottleneck caused by the stacker crane positioning error exceeding the threshold. By introducing a control causal reasoning algorithm, based on the interaction trajectory of operation time sequence data and control commands, the root cause of bottlenecks is traced, and it is clearly distinguished whether the equipment bottleneck is caused by fault or parameter setting, and whether the personnel bottleneck is caused by task allocation or command delay. Finally, a diagnostic report containing specific optimization suggestions and implementation steps is generated.

[0037] The control causal reasoning algorithm adopts a Bayesian network structure. The input data adapted to the power storage scenario includes operation time sequence data, control command interaction data, and equipment status data. The operation time sequence data includes equipment start-up and shutdown time, task completion time, and personnel operation interval. The control command interaction data includes command issuance time, execution feedback time, and parameter adjustment records. The equipment status data includes load rate, positioning error, and energy consumption value. The core steps of the algorithm are: ① Remove outlier data with a time deviation > 1 μs; ② Construct a cause-and-effect graph, with bottleneck phenomena as result nodes and operational / control / equipment factors as cause nodes; ③ Calibrate conditional probabilities based on historical power storage operation data; ④ Output the core reasons and related evidence with a confidence level of ≥85%.

[0038] The algorithm output is linked to the bottleneck level quantification result. For example, if the AGV path tracking deviation exceeds the standard (control parameter bottleneck), the output will show the specific optimization direction for adjusting the path deviation threshold.

[0039] The specific calculation method for the secondary indicator is as follows: Servo motor positioning error fluctuation rate = (maximum positioning error in a single operation - average positioning error) / average positioning error × 100%; AGV path tracking deviation = the average Euclidean distance between the actual running trajectory and the planned trajectory; the calculation cycle is synchronized with the data acquisition frequency, 2Hz for normal scenarios and 10Hz for emergency scenarios, to ensure the real-time performance and accuracy of the indicators.

[0040] The optimization decision module, based on the diagnostic report and optimization suggestions output by the bottleneck diagnosis module, adopts a multi-objective optimization algorithm with operational efficiency, control accuracy, and energy consumption as objectives. It balances the various optimization objectives through a weight allocation mechanism and combines the unique control constraints of power warehousing, such as high-voltage area operation control authority, large equipment hoisting speed limit, and emergency material priority scheduling, to generate a comprehensive solution that combines operational optimization and control stability. The core output of the solution is the linkage result of control optimization instructions and operational adjustment schemes. For example, for picking bottlenecks, it simultaneously outputs personnel reassignment schemes, picking path optimization suggestions, and optimized values ​​of AGV path tracking control parameters (path deviation threshold adjusted from 5mm to 3mm). The input data for the multi-objective optimization algorithm includes bottleneck diagnostic indicators, power storage constraint parameters, and target weight coefficients. Bottleneck diagnostic indicators include quantitative data such as equipment utilization rate, control response time, and energy consumption value. Power storage constraint parameters include high-voltage area operation permission codes, upper limit of hoisting speed for large equipment, and priority identifiers for emergency supplies. In normal scenarios, the efficiency weight coefficient is 0.4, the accuracy weight coefficient is 0.3, and the energy consumption weight coefficient is 0.3. In emergency scenarios, the efficiency weight coefficient is 0.5, the accuracy weight coefficient is 0.2, and the energy consumption weight coefficient is 0.3.

[0041] The algorithm achieves multi-objective solution through non-dominated sorting genetic algorithm (NSGA-Ⅲ), and the output data is a structured optimization scheme, including operation adjustment parameters and control optimization parameters. The output parameter format is consistent with the preset format of PLC instruction protocol library to ensure the feasibility of automatic conversion. The operation adjustment parameters include personnel allocation ratio and picking path node coordinates. The control optimization parameters include servo motor positioning error threshold and AGV movement speed curve parameters.

[0042] Configure a control command conflict detection and resolution engine, perform full-scenario pre-simulation of generated control commands based on a five-dimensional twin model, detect timing conflicts of control commands of multiple devices, such as overlapping control commands of AGV and stacker crane, and conflicts in high-pressure area operations. Conflicts are resolved by rearranging control timing and prioritizing, with emergency material-related control commands having the highest priority. The design optimizes the automatic conversion module for decision and control instructions. It has a built-in library of mainstream PLC device instruction protocols and can parse the optimization scheme into PLC executable control code such as LAD / FBD language. It supports one-click deployment to edge control nodes without the need for manual secondary compilation.

[0043] The control command conflict detection cycle is 10ms / time. The conflict judgment criterion is that within the same time window, the control commands received by two or more devices involve the same work area or there is motion interference. The weight adjustment trigger condition of the multi-objective optimization algorithm is: when a single objective indicator does not reach the preset threshold, such as work efficiency <80% or control accuracy deviation >5mm, the weight of that objective is automatically increased by 5%-10%, while ensuring that the total weight sum is 1.

[0044] The iteration termination condition of the multi-objective optimization algorithm is: the change in the objective function value of three consecutive iterations is ≤0.01, or the number of iterations reaches the preset upper limit, which is 50 times in normal scenarios and 20 times in emergency scenarios. During the iteration process, solutions that exceed the power storage constraints are eliminated in real time, such as solutions where the hoisting speed of large equipment exceeds the rated value, to ensure the feasibility of the output solution.

[0045] The virtual-real linkage module receives control commands from the optimization decision-making module, constructing a three-tiered industrial control architecture consisting of a cloud decision-making layer, an edge control layer, and an equipment execution layer. The cloud is used for global optimization decision-making and control strategy planning, and synchronously coordinates all warehouse operation data resources. The edge control node integrates a PLC core control unit, quickly receives control commands from the cloud, and executes real-time control tasks such as stacker crane positioning control and AGV motion control, while synchronously collecting equipment execution feedback data. The equipment execution layer is interconnected with the edge control node through an industrial bus to ensure the stability of control command execution. The data transmission verification of the industrial bus adopts the CRC32 verification algorithm. Each data frame contains three types of additional information: check code, device identifier, and timestamp. When the verification fails, retransmission is automatically triggered. The number of retransmissions is ≤3, and the retransmission interval is 1ms. The data feedback priority of the full closed-loop control link is set as follows: equipment fault data > control parameter deviation data > normal operation data, to ensure that critical information is processed first.

[0046] An adaptive control execution adjustment mechanism is designed. Based on the equipment execution feedback data, such as actual positioning error and movement speed, the control parameters are dynamically adjusted through an adaptive algorithm to compensate for the deviation between the model and the physical entity. When the positioning deviation exceeds 3mm, the proportional coefficient is automatically corrected. The adaptive algorithm adopts a PID parameter self-tuning algorithm. The input data adapted to the power storage scenario includes: equipment execution feedback data, model preset parameters, and deviation thresholds. The equipment execution feedback data includes positioning error, movement speed, and load current, and the sampling frequency is consistent with the acquisition and adaptation module. The model preset parameters include target positioning accuracy, rated speed, and allowable load range. The deviation thresholds include a positioning deviation of 3mm and a speed deviation of 5% of the rated value. The core logic of the algorithm is: ① Calculate the deviation between the feedback data and the target parameters in real time; ② Adjust the PID proportional (Kp), integral (Ki), and derivative (Kd) coefficients based on the deviation change rate (dE / dt); ③ Output the adjusted control parameters, such as the stacker crane positioning control voltage and AGV drive motor speed command, with an adjustment step size ≤ 0.01 units to avoid equipment operation fluctuations; Input data and output parameters are linked through a deviation and adjustment mapping table to ensure compatibility with the control characteristics of power equipment.

[0047] Establish a closed-loop control link encompassing simulation diagnosis, optimization decision-making, control execution, and feedback correction. Real-time data from equipment execution is transmitted back to the twin modeling module for model updates, while simultaneously being synchronized to the monitoring and maintenance module for real-time status monitoring. This triggers model updates and secondary simulations, verifies the control optimization effect, and forms a continuous iterative control optimization mechanism.

[0048] The industrial bus type used for interconnection between the device execution layer and the edge control node is Profibus-DP or EtherCAT, with a communication rate of ≥10Mbps, ensuring that the transmission delay of control commands is ≤2ms. The data feedback frequency of the full closed-loop control link is consistent with that of the acquisition and adaptation module, 2Hz in normal scenarios and 10Hz in emergency scenarios. The feedback data includes three core types of information: device execution status code, actual operating parameters, and deviation value, and the format is uniformly standardized JSON structure.

[0049] The monitoring and maintenance module integrates real-time data from the acquisition and adaptation module, execution feedback data from the virtual-real linkage module, and core status information from each module. Employing industrial-grade 3D visualization technology, it constructs a multi-dimensional interactive monitoring panel that displays real-time operation status, bottleneck locations, equipment operating parameters, and core control dimension information. Control dimension information includes real-time control parameter curves, control command execution status, and equipment control performance rankings. It supports multi-view monitoring, including global, local, and close-up views of the equipment, and allows focusing on a single equipment control unit to view detailed logs and trajectories. Real-time control parameter curves include servo motor speed changes and positioning error trends. Control command execution status includes issued, executing, completed, and abnormal.

[0050] A hierarchical intelligent early warning and alarm mechanism is established, and early warning thresholds are set according to the power storage safety specifications. These thresholds include early warnings for operational bottlenecks and abnormal control parameters. Differentiated alarms are executed according to the level of the alarm. Emergency alarms trigger audible and visual alarms, information push notifications, and safety shutdown commands. Important alarms trigger pop-up prompts with control parameter adjustment plans.

[0051] The multi-dimensional visualization monitoring panel refreshes at 0.5 seconds per refresh in normal scenarios and 0.1 seconds per refresh in emergency scenarios, and supports manual switching between real-time monitoring and historical backtracking modes. The early warning information feedback mechanism sets the feedback cycle according to the alarm level: 30 seconds per alarm for general alarms, 10 seconds per alarm for important alarms, and immediate feedback for emergency alarms. The feedback data includes alarm number, indicator name, deviation value, and scope of impact, and directly connects to the indicator input interface of the bottleneck diagnosis module and the parameter adjustment interface of the optimization decision module.

[0052] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0053] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A power warehouse operation simulation and bottleneck diagnosis system based on digital twins, characterized in that, Includes the following modules; Data Acquisition and Adaptation Module: Deploys multi-source sensing devices to realize power storage data acquisition, integrates protocol adaptation and edge processing units to complete data standardization and control command adaptation and conversion, and builds a data acquisition and control linkage mechanism to ensure data time sequence consistency; Twin modeling module: Constructs a multi-dimensional fusion twin model based on standardized data, realizes the correspondence between virtual and physical space through laser scanning modeling and incorporates power equipment-specific control constraints, configures a dual-drive model update engine, and combines a hierarchical optimization strategy to balance simulation accuracy and operating efficiency; Simulation and deduction module: Based on a multi-dimensional model and using a fusion simulation algorithm, the operation unit is constructed as an intelligent agent and a special control rule library for power storage is established to realize the linkage simulation of the entire process operation and control command; a multi-scenario template library is built, which has the function of pre-execution verification of control commands and outputs quantitative indicators of operation and control dimensions; Bottleneck Diagnosis Module: Integrates real-time operating data and simulation analysis results to establish a dual-dimensional evaluation index system for operation and control; adopts a fusion diagnostic method to identify potential bottlenecks, traces the root cause through causal reasoning algorithms, and generates a diagnostic report with optimization suggestions; Optimization Decision Module: Based on the diagnostic report, a multi-objective optimization algorithm is used, combined with power storage-specific control constraints, to generate a collaborative optimization scheme for operation and control; Configure a control command conflict detection and resolution engine, and a design decision and automatic command conversion module to convert the scheme into executable commands for the device; The virtual-real linkage module constructs a three-level industrial control architecture to receive optimization decision commands and execute real-time control, while synchronously collecting equipment execution feedback data; it designs an adaptive control execution adjustment mechanism to dynamically adjust control parameters and establishes a full closed-loop control link to achieve data feedback and model update linkage. Monitoring and Maintenance Module: Integrates data from multiple modules to build a visual monitoring panel to achieve real-time display of the entire system status; constructs a hierarchical intelligent early warning and alarm mechanism, executes differentiated alarms according to the level, and feeds back the early warning information to the bottleneck diagnosis and optimization decision-making module.

2. The power warehouse operation simulation and bottleneck diagnosis system based on digital twin as described in claim 1, characterized in that, The multi-source acquisition architecture of the acquisition and adaptation module includes sensors, RFID readers, visual recognition devices, and industrial IoT gateways to collect comprehensive data on power storage. It integrates an industrial heterogeneous protocol adaptation layer to achieve efficient communication between heterogeneous devices and configures edge control nodes to complete data preprocessing and control command format conversion. The acquisition and control linkage mechanism is an adaptive linkage mechanism that uses clock synchronization technology to achieve time deviation control of the entire system and can dynamically match the acquisition frequency and control cycle according to the operation scenario.

3. The power warehouse operation simulation and bottleneck diagnosis system based on digital twin as described in claim 2, characterized in that, The multi-dimensional fusion twin model of the twin modeling module includes five dimensions: geometry, physics, behavior, timing, and control. The geometric model uses laser scanning modeling technology to recreate the equipment installation location, transmission links, and warehouse space layout. The physical model incorporates unique attributes and general kinematic parameters, including load limits and operational timing constraints of the power equipment. The behavior model depicts the interaction logic of the equipment control actions, the timing model records the evolution trajectory of control parameters, and the control model embeds the equipment control algorithm to realize virtual simulation verification of commands. The dual-drive model update engine corrects model parameters based on real-time data acquisition and control command execution feedback, and the hierarchical optimization strategy simplifies non-core control areas of the equipment while retaining details of key control components.

4. The power warehouse operation simulation and bottleneck diagnosis system based on digital twin as described in claim 3, characterized in that, The simulation algorithm of the simulation and deduction module adopts a combination of discrete event simulation and control command timing simulation. The intelligent agent includes personnel, equipment and control unit and presets differentiated operation rules. The dual-scenario template library includes operation scenarios and control strategy verification scenarios, and supports user-defined scenario parameters. Among the output quantitative indicators, the control performance indicators include control response time, positioning error and command execution success rate.

5. The power warehouse operation simulation and bottleneck diagnosis system based on digital twin as described in claim 4, characterized in that, The evaluation index system of the bottleneck diagnosis module includes three dimensions: operation status, control parameters, and equipment performance. The operation status dimension indexes include equipment utilization rate, personnel operation efficiency, and material flow efficiency. The control indexes include control response efficiency, control accuracy stability, control energy consumption and secondary sub-indicators. The equipment performance indexes include equipment failure rate, equipment load rate, and equipment maintenance cycle compliance rate. The Analytic Hierarchy Process (AHP) is used in conjunction with the characteristics of power storage operations to determine the weight of indicators and achieve quantitative assessment of bottleneck levels. A diagnostic method that integrates real-time operating condition monitoring and control simulation prediction is used to identify potential bottlenecks, and the root cause of bottlenecks is traced based on the interaction trajectory of operation time sequence data and control commands.

6. The power warehouse operation simulation and bottleneck diagnosis system based on digital twin as described in claim 5, characterized in that, The multi-objective optimization algorithm of the optimization decision module takes operational efficiency, control accuracy, and energy consumption as optimization objectives, balances objective priorities through a weight allocation mechanism, and generates a comprehensive optimization scheme by combining the unique control constraints of power storage. The conflict detection and resolution engine detects instruction timing conflicts based on a five-dimensional twin model pre-simulation, and completes conflict resolution through timing rearrangement and priority allocation. The decision and instruction automatic conversion module has a built-in PLC device instruction protocol library, which can parse the optimization scheme into PLC executable control code and support its distribution to edge control nodes.

7. The power warehouse operation simulation and bottleneck diagnosis system based on digital twin as described in claim 6, characterized in that, The three-tier industrial control architecture of the virtual-physical linkage module includes a cloud-based decision-making layer, an edge control layer, and an equipment execution layer. The cloud-based decision-making layer is responsible for global optimization decisions and data coordination. The edge control node integrates the PLC core control unit to execute real-time control tasks and collect feedback data. The equipment execution layer is interconnected with the edge control node through an industrial bus. The adaptive control execution adjustment mechanism dynamically adjusts control parameters based on feedback data to compensate for the deviation between the model and the physical entity. The full closed-loop control link transmits equipment execution data back to the twin modeling module for model updates and synchronizes it to the monitoring and maintenance module to realize status monitoring and optimization effect verification.

8. The power warehouse operation simulation and bottleneck diagnosis system based on digital twin as described in claim 7, characterized in that, The monitoring and maintenance module uses industrial-grade 3D visualization technology to construct a multi-dimensional interactive monitoring panel, displaying core information such as operation status, bottleneck location, equipment operating parameters, and control parameters, and supports multi-view switching monitoring. The hierarchical intelligent early warning and alarm mechanism sets early warning thresholds according to power storage safety specifications, including early warnings for operation bottlenecks and abnormal control parameters, and executes differentiated alarms according to levels. Emergency alarms trigger audible and visual alarms, information push and safe shutdown instructions, while important alarms trigger pop-up prompts and control parameter adjustment schemes.

9. The power warehouse operation simulation and bottleneck diagnosis system based on digital twin as described in claim 8, characterized in that, The monitoring and maintenance module's visual monitoring panel supports historical data backtracking, enabling users to query job status, control parameters, and bottleneck diagnosis history records by time dimension.

10. The power warehouse operation simulation and bottleneck diagnosis system based on digital twin as described in claim 9, characterized in that, The normal update cycle of the dual-drive model update engine is 100ms / time, and the update cycle is 20ms / time in emergency scenarios.

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