Warehouse full-process digital operation process dynamic management and control optimization method

Abstract

This invention relates to the field of digital management and control of warehousing and logistics, and discloses a dynamic management and optimization method for the entire warehousing process based on digitalization. The method includes: establishing a distributed data acquisition node array to construct a digital twin model from physical space to virtual space; decomposing the work tasks into atomic operation sequences assigned to physical operating entities; querying a response feature mapping library to obtain the time and energy consumption deviations of the atomic operation sequences under the corresponding physical operating entities; introducing the deviations as decision parameters into a multi-objective optimization algorithm to correct theoretical operating costs and issue a pre-calibration instruction set. This invention internalizes the inherent characteristics of equipment, eliminates performance loss offsets caused by mechanical response lag, synchronizes scheduling instructions with physical operating capabilities, and ensures energy efficiency stability throughout the entire warehousing operation process.

Description

Technical Field

[0001] This invention relates to a method for dynamic control and optimization of operational processes based on full-process digitalization of warehousing, belonging to the field of digital control technology for warehousing and logistics. Background Technology

[0002] Currently, digital methods are commonly used to construct virtual mappings of physical entities, collecting operational data from warehousing, storage, and picking processes to achieve process monitoring. This control method, centered on digital mapping, improves the visibility of warehousing elements and supports the automatic scheduling of basic business logic. Digital models are usually built on the idealized assumption that physical entities follow preset parameters. However, the action feedback generated by physical entities in the real working environment is not ideal and lossless. Changes in acceleration during equipment start-up and shutdown, physical gaps in mechanical transmission parts, microsecond-level jitter in communication links, and operational deviations of operators together constitute systematic physical execution losses. Existing control methods treat such losses as external noise, causing the theoretical instructions generated by the digital model to be discounted in the physical world. This state deviation between the virtual and the real continues to arise as the work process progresses, leading to a disconnect between control decisions and physical reality.

[0003] Conventional methods typically attempt to increase sensor deployment density or construct high-precision dynamic models. However, because these methods fail to perceive the characteristic deviations of physical execution at the source of instruction generation, simple hardware stacking cannot eliminate the mismatch between the virtual and the real world. Furthermore, high-precision simulation models have high computational resource requirements and struggle to track slow time-varying loss characteristics caused by equipment wear or environmental fluctuations in real time, leading to a contradiction between the real-time nature and applicability of control decisions. Remedial measures focus on fine-tuning hardware or high-density sensor stacking, relying solely on the static parameter solidification at the hardware level. This is insufficient to address scheduling failures caused by the lack of dynamic loss perception at the control logic layer. For example, publication number CN1... Chinese invention patent application 20355330A discloses a warehouse park operation and maintenance management system and method based on digital twins. It plans routes through ant colony algorithm and uses weight changes for post-event anomaly judgment. The underlying technology relies on idealized completion and static a posteriori logic loops, treating the physical execution process as a black box. It is biased towards the overall path optimization and end result verification method, which cannot identify the mechanical response delay and dynamic redundancy loss at the sub-second atomic operation level. In the face of dynamic accuracy degradation caused by multiple factors, this existing technology cannot internalize physical losses at the source of instruction generation, and it is difficult to achieve deep synchronization between control decision and physical operation capabilities.

[0004] Therefore, the technical problem to be solved by this invention is how to establish a dynamic control mechanism that can sense and internalize deviations in physical execution characteristics, so that control commands have the ability to adaptively compensate for physical losses. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A method for dynamic control and optimization of operational processes based on full-process digitalization of warehousing, comprising the following steps: Step S1: Establish a distributed data acquisition node array covering the inbound, storage, picking, verification and outbound operation processes. Acquire multi-source heterogeneous data of the warehouse area through the data acquisition node array, and construct a digital twin model from the physical space of the warehouse to the digital virtual space based on the multi-source heterogeneous data, so as to realize the real-time dynamic mapping of the spatial layout and operation status of the physical warehouse entity in the digital virtual space. Step S2: In the scheduling and control engine, the warehousing operation tasks are structurally decomposed according to the preset decomposition rules to generate atomic operation sequences assigned to specific physical operation entities. The atomic operations include movement, grabbing, lifting and placing actions. Step S3: Query the built-in instruction response feature mapping library to obtain the estimated feature deviation of each atomic operation in the atomic operation sequence under the corresponding physical operation subject. The estimated feature deviation includes the estimated time deviation generated by the physical operation subject in the action response stage and the estimated energy consumption deviation generated in the energy consumption stage. Step S4: The predicted feature deviation is introduced as an endogenous decision parameter within the system into the objective function of the multi-objective optimization algorithm. The theoretical operating cost based on the atomic operation sequence is fed forward and quantitatively verified to calculate the comprehensive cost after internalizing the inherent kinematic characteristics and dynamic loss characteristics of the physical equipment. Step S5: Perform multi-objective optimization based on comprehensive cost and issue a pre-calibration instruction set to the physical operation layer. The pre-calibration instruction set is used to offset the mechanical response lag of the physical operation entity and the performance loss offset caused by positioning deviation.

[0006] Preferably, step S1 includes: step S11, acquiring material location, equipment operating status, and warehouse environmental parameter data transmitted back by the visual sensor, radio frequency identification sensor, and lidar in the acquisition node array; step S12, importing the data into the digital twin model, and synchronizing the spatial coordinate trajectory and load status of the physical operating entity in real time in the digital virtual space.

[0007] Preferably, in step S2, the scheduling and control engine breaks down the warehousing operation tasks into the smallest running units according to the priority of the warehousing operation tasks, and assigns the atomic operation sequence to the matching physical operation entities according to the current real-time location, remaining battery capacity and maximum load capacity of each physical operation entity.

[0008] Preferably, the response feature mapping library includes a mapping matrix composed of multiple sets of instruction parameters, operating entity identifiers, and historical operation feature associations; in step S3, the method for obtaining the estimated feature deviation includes: extracting the instruction parameters of the current atomic operation and querying the historical operation feature records corresponding to the operating entity identifier; and calculating the estimated feature deviation based on the average deviation between the historical operation feature records and the theoretical parameters.

[0009] Preferably, in step S4, the feedforward correction process includes: dynamically correcting the preset start time node and operation duration of the atomic operation using the estimated time deviation; and proactively reserving dynamic redundancy generated by the acceleration segment, deceleration segment, and end-positioning adjustment in the pre-calibration instruction set to neutralize the actual operation error caused by the mechanical inertia of the physical operating entity or the signal transmission delay.

[0010] Preferably, in step S5, the multi-objective optimization process employs an improved genetic algorithm or a constraint-based linear programming solver. The optimization objectives include minimizing the total operation time evaluation value and minimizing the total energy consumption evaluation value. When evaluating candidate solutions, the optimization algorithm accumulates the sum of the predicted characteristic deviations of all atomic operations in the atomic operation sequence in its cost function, so as to achieve a systematic and accurate reconstruction of the physical operation process in the digital space.

[0011] Preferably, the method further includes anomaly identification: real-time comparison of the operation trajectory of each physical entity in the digital twin model with the preset trajectory of the pre-calibration instruction set; when the temporal deviation or spatial overlap between the operation trajectory and the preset trajectory exceeds the preset safety warning line, it is determined that there is an operation anomaly in the physical operation layer, and obstacle avoidance instructions or path replanning instructions are issued based on the global resource distribution status in the digital virtual space.

[0012] Preferably, the method provides a continuous and visible control view of the entire process through a digital virtual space. The control view includes an inbound progress billboard, a heat map of the warehouse area, a material tracking link, and an equipment operation health index. The digital virtual space is synchronously updated in less than 1 second according to the feedback frequency of the data collection node array to ensure the adaptability of the control data to the operating rhythm of the physical operation entity.

[0013] Preferably, the method utilizes the collected operational characteristic data of the physical operating entity to identify slow time-varying factors such as equipment mechanical wear parameters and battery performance degradation coefficient; by dynamically updating the response feature mapping library, the generation strategy of the pre-calibration instruction set is synchronized with the current life cycle state of the physical operating entity, so as to maintain the operational energy efficiency stability of the warehousing system throughout its entire life cycle.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. In the dynamic management and optimization of the work process, by constructing a distributed data acquisition system and digital mapping framework covering the entire work process of receiving, storing, picking, verifying, issuing and inventorying, real-time mapping and full-node synchronization of the physical space of the warehouse to the digital space are realized, eliminating the node monitoring blind spots and data fragmentation in the traditional management and control model, and providing a consistent and continuously visible digital foundation for the refined management and control of the entire warehouse operation.

[0015] 2. By introducing a physical execution feature feedback mechanism into the dynamic management engine, the systematic losses generated by physical entities during action response, mechanical transmission, and network transmission are transformed into endogenous decision variables. This enables the optimization algorithm to actively internalize and compensate for time deviation and energy deviation features when generating scheduling instructions, bridging the consensus gap between the theoretical optimal path and the actual feasibility of the physical world, and improving the landing accuracy and execution efficiency of scheduling instructions under complex working conditions.

[0016] 3. By establishing an online micro-calibration closed loop based on job execution feedback, and dynamically updating the instruction feature mapping library using the collected actual characteristic data of atomic operations, the control system is able to identify and adapt to slow time-varying factors such as equipment mechanical wear, battery performance degradation, and changes in personnel proficiency. This ensures deep collaborative evolution between control strategies and physical entity status, and guarantees the stability of warehousing operation efficiency throughout the system's entire lifecycle. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the optimization of digital twin and dynamic control of the entire warehousing process in this invention. Figure 2 This is a logic block diagram of the dynamic evolution and compensation of the instruction response feature library of the present invention.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0020] A method for dynamic control and optimization of operational processes based on end-to-end digitalization of warehousing includes the following steps: Step S1: Establish a distributed data acquisition node array covering the inbound, storage, picking, verification and outbound operation processes. Acquire multi-source heterogeneous data of the warehouse area through the data acquisition node array, and construct a digital twin model from the physical space of the warehouse to the digital virtual space based on the multi-source heterogeneous data, so as to realize the real-time dynamic mapping of the spatial layout and operation status of the physical warehouse entity in the digital virtual space. Step S2: In the scheduling and control engine, the warehousing operation tasks are structurally decomposed according to the preset decomposition rules to generate atomic operation sequences assigned to specific physical operation entities. The atomic operations include movement, grabbing, lifting and placing actions. Step S3: Query the built-in instruction response feature mapping library to obtain the estimated feature deviation of each atomic operation in the atomic operation sequence under the corresponding physical operation subject. The estimated feature deviation includes the estimated time deviation generated by the physical operation subject in the action response stage and the estimated energy consumption deviation generated in the energy consumption stage. Step S4: The predicted feature deviation is introduced as an endogenous decision parameter within the system into the objective function of the multi-objective optimization algorithm. The theoretical operating cost based on the atomic operation sequence is fed forward and quantitatively verified to calculate the comprehensive cost after internalizing the inherent kinematic characteristics and dynamic loss characteristics of the physical equipment. Step S5: Perform multi-objective optimization based on comprehensive cost and issue a pre-calibration instruction set to the physical operation layer. The pre-calibration instruction set is used to offset the mechanical response lag of the physical operation entity and the performance loss offset caused by positioning deviation.

[0021] Preferably, step S1 includes: step S11, acquiring material location, equipment operating status, and warehouse environmental parameter data transmitted back by the visual sensor, radio frequency identification sensor, and lidar in the acquisition node array; step S12, importing the data into the digital twin model, and synchronizing the spatial coordinate trajectory and load status of the physical operating entity in real time in the digital virtual space.

[0022] Preferably, in step S2, the scheduling and control engine breaks down the warehousing operation tasks into the smallest running units according to the priority of the warehousing operation tasks, and assigns the atomic operation sequence to the matching physical operation entities according to the current real-time location, remaining battery capacity and maximum load capacity of each physical operation entity.

[0023] Preferably, the response feature mapping library includes a mapping matrix composed of multiple sets of instruction parameters, operating entity identifiers, and historical operation feature associations; in step S3, the method for obtaining the estimated feature deviation includes: extracting the instruction parameters of the current atomic operation and querying the historical operation feature records corresponding to the operating entity identifier; and calculating the estimated feature deviation based on the average deviation between the historical operation feature records and the theoretical parameters.

[0024] Preferably, in step S4, the feedforward correction process includes: dynamically correcting the preset start time node and operation duration of the atomic operation using the estimated time deviation; and proactively reserving dynamic redundancy generated by the acceleration segment, deceleration segment, and end-positioning adjustment in the pre-calibration instruction set to neutralize the actual operation error caused by the mechanical inertia of the physical operating entity or the signal transmission delay.

[0025] Preferably, in step S5, the multi-objective optimization process employs an improved genetic algorithm or a constraint-based linear programming solver. The optimization objectives include minimizing the total operation time evaluation value and minimizing the total energy consumption evaluation value. When evaluating candidate solutions, the optimization algorithm accumulates the sum of the predicted characteristic deviations of all atomic operations in the atomic operation sequence in its cost function, so as to achieve a systematic and accurate reconstruction of the physical operation process in the digital space.

[0026] Preferably, the method further includes anomaly identification: real-time comparison of the operation trajectory of each physical entity in the digital twin model with the preset trajectory of the pre-calibration instruction set; when the temporal deviation or spatial overlap between the operation trajectory and the preset trajectory exceeds the preset safety warning line, it is determined that there is an operation anomaly in the physical operation layer, and obstacle avoidance instructions or path replanning instructions are issued based on the global resource distribution status in the digital virtual space.

[0027] Preferably, the method provides a continuous and visible control view of the entire process through a digital virtual space. The control view includes an inbound progress billboard, a heat map of the warehouse area, a material tracking link, and an equipment operation health index. The digital virtual space is synchronously updated in less than 1 second according to the feedback frequency of the data collection node array to ensure the adaptability of the control data to the operating rhythm of the physical operation entity.

[0028] Preferably, the method utilizes the collected operational characteristic data of the physical operating entity to identify slow time-varying factors such as equipment mechanical wear parameters and battery performance degradation coefficient; by dynamically updating the response feature mapping library, the generation strategy of the pre-calibration instruction set is synchronized with the current life cycle state of the physical operating entity, so as to maintain the operational energy efficiency stability of the warehousing system throughout its entire life cycle.

[0029] Example 1: Under high-load conditions in cold chain logistics warehousing centers, the concurrent issuance of multiple batches of outbound instructions leads to dense path overlap of automated guided vehicles (AGVs). During long-term physical operation, mechanical wear of the drive motor and fluctuations in the output voltage of the battery pack cause the actual start-up response time of the actuators to deviate from the preset nominal parameters. This superficial evolution at the physical level results in a cumulative time offset in the theoretical operation sequence generated in the digital virtual space at the physical warehousing site, thereby causing resource competition risks and workflow obstruction in narrow-band areas. To solve the above technical problems, the method of this invention uses a scheduling and control engine to process the real-time issued warehousing operation tasks. The scheduling and control engine breaks down the operation tasks into atomic operation sequences according to preset logical rules. The atomic operation sequences include movement, grasping, lifting, and placement actions. The system accesses the built-in instruction response feature mapping library and extracts the estimated feature deviation of the corresponding atomic operation in the current operating environment based on the identifier code of the physical operation entity. For a specific AGV, the estimated feature deviation includes the estimated time deviation. Deviation from estimated energy consumption Among them, the estimated time deviation It covers the dynamic lag of the servo system during the acceleration phase and the estimated energy consumption deviation. The energy conversion loss of the transmission chain under different load conditions was characterized. After determining the estimated characteristic deviation of each atomic operation, the system introduces the estimated characteristic deviation as an endogenous variable into the multi-objective optimization algorithm. Through feedforward verification logic, the system quantifies and corrects the theoretical operating cost of the task. The corrected total operation time evaluation value satisfies the following formula: ,in, This is the total operation time assessment value. The original operation completion time is calculated based on theoretical operating costs. For the first The estimated time deviation for each atomic operation.

[0030] The scheduling and control engine derives the total energy consumption assessment value based on the work done by the motor and the law of conservation of energy, and calculates the estimated energy consumption deviation of each atomic operation by obtaining the transient current of the drive motor and the bus operating voltage under the corresponding load conditions of the physical operating entity. The scheduling and control engine calculates the total energy consumption assessment value, including physical execution losses. : ,in, To cover the total energy consumption assessment value that includes physical energy consumption offset; Pre-determine the theoretical total energy consumption for the execution of the digital twin model; For the first Each atomic operation corresponds to an estimated energy consumption deviation. The scheduling and control engine constructs a multi-objective evaluation function using a linear weighted scalarization method. Transform two-dimensional cost parameters into a single evaluation object: ,in, Dimensionless comprehensive evaluation cost for instruction optimization; and These are the historical baseline completion time and historical baseline total energy consumption for similar tasks under physical environments. For time weighting coefficients, The energy consumption weighting coefficients are both greater than 0 and their sum is always 1. These time weighting coefficients and energy consumption weighting coefficients are not arbitrary abstract constants preset within the system, but rather deterministic proportional parameters derived by introducing a full-load test fleet under factory calibration conditions. This is achieved by establishing a bivariate linear regression equation between operating time efficiency and transient discharge heat generation at the motor bus, and solving for the Pareto front extreme point corresponding to the system's physical safety heat dissipation limit. Similarly, the smoothing factor is triggered in subsequent schemes. The temperature fluctuation slope threshold on which the forced switching is based also comes from tests in a real climate test chamber. The test results show that when the space heating rate exceeds the set safe slope, the thermal expansion rate of the guide rail assembly will cause the dynamic resistance force at the drive end to increase exponentially, thus providing a solid engineering experimental physical basis for this hard switching boundary condition.

[0031] The multi-objective optimization algorithm optimizes based on the corrected evaluation value, generates a set of pre-calibrated instructions and transmits them to the physical operating entities. In the optimization execution step of the algorithm, the system uses the physical identifiers and action flow sequences of various operating entities as genes to construct an initial chromosome population in discrete integer encoding format. The algorithm uses the reciprocal of the dimensionless comprehensive evaluation cost as a single fitness function for scheme evaluation, and hard-coded the minimum physical anti-collision distance and the lower limit of battery deep discharge as penalty constraints in the iterative logic to automatically eliminate infeasible solutions. The optimization calculation cyclically calls the binary tournament selection operator and the heuristic crossover operator to perform population recombination and update. When the fitness of the globally optimal individual in the population is detected to be within twenty consecutive... The calculation terminates when the volatility in the next generation of propagation falls below the set convergence threshold of one-thousandth, locking the current optimal configuration sequence as the final production output. The pre-calibration instruction set proactively reserves a compensation margin equivalent to the mechanical response lag in the instruction timing to offset the performance drift of the physical execution end. This equivalent hedging mechanism from the time domain to the spatial domain is based on the fact that the mechanical wear or friction fluctuations of the physical entity will essentially cause the inertial slip distance during the execution of braking or steering commands to deviate from the nominal value. The system pre-calculates the spatial error of this slip distance and, based on the transient linear velocity of the current drive wheel, reverses it into an advance response amount in the time dimension. By triggering the action command in advance on the time axis, the physical entity can achieve the desired result before exhausting the mechanical inertia. After the response time, the end effector precisely stops at the desired spatial physical coordinate point. Thus, without adding high-frequency spatial positioning sensors, self-consistent elimination of spatial positioning deviations is achieved through the translation of control timing. Through this control logic, the spatial motion trajectory of the physical entity remains synchronized in real time with the twin model in the digital virtual space. This method of mapping the inherent physical characteristics of the equipment to the software scheduling layer eliminates the interference of physical environmental noise on the digital twin system. Without changing the physical characteristics of the hardware, it improves the control accuracy and energy efficiency stability of the entire warehousing operation process. In the decomposition of warehousing tasks, the scheduling and control engine, based on the motion envelope of the physical operating entity and the execution... The maximum lifting height of the actuator defines the execution boundary of the atomic operation sequence. For pallet picking tasks, the decomposition rule maps the task path to a discrete set of points composed of linear displacement vectors and rotational motion vectors. The scheduling and control engine sets a deceleration margin at the switching nodes of adjacent atomic operations based on the moment of inertia of the actuator, so that the generated atomic operation sequence satisfies the dynamic response constraints of the physical operation subject in the time dimension. The deterministic algorithm logic of the preset decomposition rule is as follows: the scheduling engine analyzes the starting location and target three-dimensional spatial coordinates of the overall warehousing task and extracts the global continuous planning path; using the spatial orthogonal projection rule, the continuous path is forcibly divided into the projection component in the horizontal walking coordinate system and the lifting component in the vertical direction.The system scans the slope of the normal vector of the path matrix. When the absolute value of the change in the direction angle of the horizontal coordinate component exceeds a set safe turning threshold, it automatically inserts a single origin rotation action unit. When a non-zero jump in the vertical component coordinate is detected, the horizontal movement command is cut off and lifting and grasping action units are seamlessly inserted. This reduces the deterministic nature of the multi-dimensional coupled three-dimensional overall addressing action to an atomic-level sequence that can be executed linearly by a single underlying actuator.

[0032] Example 2: In a warehouse operation verification platform comprising four submersible automated guided vehicles (AGVs) and a distributed lidar array, the system faces measurement interference from environmental electromagnetic noise and random fluctuations in equipment battery voltage. The visual sensor's detection accuracy is better than 2mm, and the sampling frequency is set to 25Hz to capture the transient displacement of the working entity. To simulate real industrial conditions, random noise with a signal-to-noise ratio of 25dB is superimposed on the signal source in the test environment to verify the system's stability under non-ideal sensing conditions. Key parameter sampling period... The settings involve balancing the real-time performance of the data stream with the load on the processing unit, when the physical operating unit has its maximum moving speed. When the speed is in the range of 1.5 m / s to 2.0 m / s, in order to satisfy the sampling theorem and prevent the motion trajectory from aliasing in the digital virtual space, the sampling period is... The value tends towards the lower limit of the range and was calculated to be 40ms. The baseline data was obtained through comparison with sample group one, which used static path planning to handle a picking task containing 10 storage locations, and its theoretically preset total completion time... The completion time of the physical entity on site was 128.5s. Due to the mechanical lag of the drive motor and the wear of the transmission mechanism wheel diameter, the completion time of the physical entity on site shifted to 142.2s, resulting in a cumulative time lag of 13.7s. At this time, the maximum deviation between the spatial coordinates of the physical entity and the mapped coordinates of the digital virtual space reached 458mm, indicating that a single theoretical model cannot adapt to entities with physical losses.

[0033] When processing the same task, the scheduling and control engine of this invention decomposes the task into 120 atomic operation sequences, accesses the built-in instruction response feature mapping library, and extracts the estimated feature deviations of linear movement and rotation. Actual test data shows that the estimated time deviation under no-load conditions... The deviation was initially 88.3 ms, but increased to 110.6 ms when the load increased to 500 kg. This positive linear correlation between the deviation and the increasing load weight provides a deterministic physical basis for subsequent command calibration. The system uses corrective logic to calculate the total operation time assessment value after internalizing the physical characteristics. : ,in, This is the total operation time assessment value. The original completion time of the operation. For the first The multi-objective optimization algorithm generates a pre-calibration instruction set based on the estimated time deviation corresponding to each atomic operation, and reserves a compensation margin in the instruction timing equivalent to the mechanical response hysteresis.

[0034] The test results show that the completion time of the sample group of the present invention is 142.1s, which is consistent with the evaluated value. The error was only 0.21%, and the dynamic deviation of the virtual-real mapping coordinates converged to within 28mm. Compared with sample group 2, which removed the atomic-level disassembly step and only used the global compensation coefficient at the system level, its coordinate deviation fluctuation was still at a high level of 115mm, confirming the role of deviation correction for atomic operations in improving the accuracy of virtual-real synchronization. In the boundary test, when the manually set mechanical loss exceeded the preset range by 30%, the energy efficiency optimization gain curve of the system tended to flatten, indicating that the system's compensation logic was consistent with the physical overload limit of the actuator. This verification process confirmed that by mapping the dynamic characteristics of the physical entity to the source of instruction generation, this method eliminated the control delay caused by mechanical loss and achieved high-fidelity synchronization between the physical space and the digital virtual space. This processing method, which transforms the inherent physical characteristics of the equipment into scheduling variables, solves the problem of accuracy degradation in the entire warehousing process without changing the hardware structure, through virtual-real interaction logic.

[0035] Example 3: This example combines Figures 1 to 2 This paper explains the dynamic control and optimization method for operational processes based on the digitalization of the entire warehousing process, such as... Figure 1 As shown, the dynamic control and optimization method for the entire warehousing process digitalization covers five core stages: Step S1 establishes a distributed acquisition node array to obtain multi-source heterogeneous data, constructs a digital twin model from physical space to virtual space, and realizes real-time dynamic mapping of the physical space layout and operational status of the warehouse; Step S2 decomposes the warehousing operation tasks in the scheduling and control engine according to preset decomposition rules, generating atomic operation sequences assigned to specific physical operation entities; Step S3 queries the built-in instruction response feature mapping library to obtain the estimated time deviation and estimated energy consumption deviation of each operation in the atomic operation sequence under the corresponding operation entity; Step S4 introduces the estimated feature deviation as an endogenous decision parameter into a multi-objective optimization algorithm to perform feedforward correction and quantitative verification of the theoretical operating cost, calculates the comprehensive cost that internalizes the equipment loss characteristics; and Step S5 performs multi-objective optimization based on the comprehensive cost and issues a pre-calibration instruction set to the physical operation layer to offset the performance loss caused by the mechanical response lag and positioning deviation of the operation entity.

[0036] like Figure 2As shown, the system ensures control accuracy through a dynamic update mechanism of the command response feature mapping library. Its detailed logical framework includes: extracting the measured displacement time series using the actual execution data of atomic operations, and performing real-time deviation sampling to calculate the deviation between the measured value and the theoretical parameter. The sampling results are output to the weighted sliding window model for fusion. This model simultaneously calls the multidimensional deviation mean of atomic operations stored in the historical operation feature record, and maintains the dynamic update of the mapping library data through feature evolution loop. At the same time, the slow time-varying factor monitoring module identifies the mechanical wear of equipment and the degradation of battery performance in real time, and drives the smoothing factor dynamic adjustment module to switch the weight coefficient according to the slope of environmental fluctuations, thereby intervening in the calculation logic of the weighted sliding window model in real time. Finally, the calculated deviation data is updated to the command response feature mapping library, the operation subject identification mapping matrix is ​​synchronized, and command response margin compensation is generated. By injecting reverse position compensation and time margin into the scheduling command, the system achieves systematic accuracy reconstruction of the physical operation process in the digital virtual space.

[0037] Example 4: In the cleanroom of a semiconductor precision wafer storage facility, the system faces challenges such as the positioning accuracy requirement of the robotic arm end effector being better than 0.1mm and the thermal deformation caused by the low heat dissipation rate of the vacuum environment. Due to the heat accumulation of the drive motor under high-frequency start-stop conditions, the rigidity characteristics of the transmission mechanism undergo nonlinear drift with the duration of continuous operation, causing dynamic misalignment between the trajectory planning scheme generated by the digital virtual space and the actual displacement of the physical entity in the vacuum chamber. To solve the problem of displacement vector conversion under multi-source heterogeneous sensor data, the scheduling and control engine acquires the data streams returned by the visual sensors and LiDAR in the distributed acquisition node array, extracts the features of the physical displacement image sequence acquired by the visual sensors, and converts it into a three-dimensional coordinate vector. The point cloud data generated by the lidar is then converted into a reference coordinate vector. The three-dimensional coordinate vector is transformed through the spatial mapping operator. With reference coordinate vector Projected onto a unified digital virtual space coordinate system, the real-time twin state coordinates of the physical entity are calculated.

[0038] To address the non-steady-state evolution of equipment performance, the system dynamically updates the built-in command response feature mapping library using a weighted sliding window model, collecting data on the physical operating entity as it completes its first... Actual displacement time data after subatomic operation and its theoretical completion time Real-time deviation between The updated predicted characteristic deviation is calculated according to the following formula. : ,in, This is the updated predicted feature bias. The original mean deviation in the mapping library. This represents the real-time deviation of the current sample. The smoothing factor is dimensionless, with a value range between 0.15 and 0.25. Furthermore, when the slope of the cleanroom ambient temperature fluctuation exceeds 5℃ / h, the system will... Switching to 0.25 improves the deviation capture rate. Addressing the inherent microsecond-level jitter in the communication link, this control logic does not directly filter transient high-frequency signals using a weighted sliding window. In actual execution, the system configures a microsecond-level high-frequency sampling buffer queue on the underlying servo driver board of the physical entity. This hardware queue absorbs and smooths out high-frequency random jitter during command issuance, outputting a steady-state structured command stream with deterministic delay. The aforementioned real-time deviation calculation and weighted sliding model only specifically extract the low-frequency delay mean component of this steady-state command stream over a long time span, thus achieving a two-layer decoupled operation: hard isolation of microsecond-level high-frequency noise by the underlying hardware and focused tracking of slow time-varying physical mechanical wear by the top-level algorithm. The scheduling and control engine then uses the updated predicted characteristic deviation... By pre-injecting reverse position compensation into the atomic operation sequence, the spatial coordinate deviation of the robotic arm end was maintained within 0.08 mm during a 24-hour continuous operation verification process. This confirms that by sensing the performance degradation of the physical entity and driving the evolution of the virtual mapping logic, self-healing of instruction execution accuracy in an unsteady environment was achieved.

[0039] Example 5: In the debugging scenario of the new model of automated guided vehicle (AGV) accessing the scheduling system, the scheduling and control engine initializes and finalizes the instruction response feature mapping library. The physical operating entity is then directed to displace at specific initial velocities and accelerations along a preset straight reference path. The measured displacement time series of the physical operating entity in each motion interval is collected by an external laser measurement station. Calculate the measured displacement time series Compared with nominal running time The difference is used to obtain the initial deviation matrix. ;in, This is a measured displacement time series. Nominal running time, This is the initial deviation matrix; the initial deviation matrix Each data point corresponds to a specific load weight and battery remaining status, and the performance characteristics of the physical operating entity are mapped to the command response feature mapping library. In order to enable the two-dimensional initial deviation matrix obtained solely from the translational straight-line benchmark test to accurately generalize to the three-dimensional warehousing business scenario covering complex lifting and variable angle rotation, the feature mapping library is equipped with a spatial coupling decoupling logic operator. When encountering complex three-dimensional multi-degree-of-freedom movements, the operator uses the principle of dynamic vector superposition to extract the basic linear displacement deviation that completely corresponds to the current load and power of the main body from the initial matrix. Then, it retrieves the inherent rotational inertia of the servo motor at the factory calibration and the no-load vertical lifting damping ratio as the correction coefficient vector. The basic deviation and the correction coefficient vector are multiplied and amplified, and then the comprehensive characteristic deviation parameters that increase nonlinearly under the composite motion mode are autonomously deduced, bridging the gap in motion complexity between the calibration environment and the actual working conditions.

[0040] When the system is deployed in a warehouse environment with varying ground friction coefficients, the scheduling and control engine completes on-site benchmark calibration during the operation initiation phase. It updates the average deviation by continuously collecting the response delay of the physical operating entities in atomic operations and introduces a smoothing factor. Adjust the weights of historical data and update the formula as follows: ,in, This is the updated predicted feature bias. The original deviation value in the mapping library. For the first The real-time delay deviation value obtained from the second sampling. The smoothing factor is dimensionless and has a value of 0.20; when the variation coefficient of the deviation of continuous sampling exceeds the preset stability threshold of 0.05, the system updates the predicted characteristic deviation. Adjust the command timing to ensure that the end-positioning accuracy of the physical operating entity is within a 5mm error range.

[0041] Example 6: In the deployment scenario of a newly built automated intelligent warehouse, the system faces the challenge of obtaining pixel coordinates from the visual sensors output within a distributed data acquisition node array. polar coordinates of LiDAR feedback To address mapping errors caused by inconsistent spatial benchmarks, the scheduling and control engine collects multiple sets of coordinate data from corresponding points at preset standard calibration target points in the storage area, and uses the least squares method to calculate the affine transformation matrix from the physical entity to the digital virtual space. And define the affine transformation matrix. The calculation accuracy threshold is 1mm; the physical operating body undergoes linear acceleration tests under no-load and full-load conditions to obtain the speed response curves of the drive mechanism under different loads, thereby determining the accuracy for a 0.5mm load under a 1000kg load. The command response margin corresponding to the acceleration command is used to offset the idle travel delay caused by the mechanical transmission backlash by shifting the start time of the acceleration segment forward.

[0042] When the physical operation entity performs picking operations, the coefficient of friction of the warehouse ground... The system monitors the slip ratio of the drive wheels in real time as the ambient humidity changes and feeds it back to the scheduling and control engine. It also collects the transient angular velocity output from the drive motor encoder and converts it into the wheel end baseline velocity. Simultaneously, the translational velocity of the centroid of the entity in space is collected from the feedback of the lidar. The lidar sampling frequency is no less than 50Hz, the ranging spatial accuracy is better than 2mm, and the system calculates the real-time slip rate based on the principle of rigid body relative sliding kinematics. If the real-time slip rate s exceeds the preset safe slip warning value of 0.05 within three consecutive sampling periods, dynamic deviation correction is triggered, and the scheduling and control engine calculates and updates the feedforward compensation amount. : ,in, This is the updated feedforward compensation amount; Extract the initial time deviation for the mapping library; Pre-calibrate the slip compensation gain coefficient for the specific physical operating body test bench characteristics; This is a dimensionless quantity for current real-time slip ratio measurement; To calibrate the dimensionless quantity of the background slip ratio under the ground level of the benchmark reservoir area for the main physical operation, and to address the ground friction coefficient When the operating condition decreases from 0.45 to 0.35, the scheduling and control engine retrieves the damping compensation coefficient stored in the instruction response feature mapping library to estimate the time deviation. The feedforward compensation was adjusted from 50ms to 65ms. After the above on-site calibration and parameter dynamic fine-tuning process, the maximum deviation between the actual motion trajectory of the physical entity and the digital virtual space mapping view was maintained within 3mm, ensuring that the virtual model accurately expressed the control logic of the physical operation subject in a complex physical environment.

[0043] When assigning tasks for atomic operation sequences, the scheduling and control engine obtains the battery voltage slope data of each physical operating entity transmitted back by the acquisition node array. The system calculates the estimated energy efficiency loss value required for the physical operating entity to move to the target storage location and complete the lifting action under the current load weight. And compare the available power reserve of the physical operating unit. If the evaluation results meet the following criteria: ,in, This refers to the available electrical capacity of the main physical operating unit. To estimate energy efficiency loss values, If the preset energy safety factor is between 1.1 and 1.3, the scheduling and control engine will assign the atomic operation sequence to the physical operating entity.

[0044] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for dynamic management and control optimization of work processes based on warehouse full-process digitization, characterized in that, Includes the following steps: Step S1: Establish a distributed data acquisition node array covering the inbound, storage, picking, verification and outbound operation processes. Acquire multi-source heterogeneous data of the warehouse area through the data acquisition node array, and construct a digital twin model from the physical space of the warehouse to the digital virtual space based on the multi-source heterogeneous data, so as to realize the real-time dynamic mapping of the spatial layout and operation status of the physical warehouse entity in the digital virtual space. Step S2: In the scheduling and control engine, the warehousing operation tasks are structurally decomposed according to the preset decomposition rules to generate atomic operation sequences assigned to specific physical operation entities. The atomic operations include movement, grabbing, lifting and placing actions. Step S3: Query the built-in instruction response feature mapping library to obtain the estimated feature deviation of each atomic operation in the atomic operation sequence under the corresponding physical operation subject. The estimated feature deviation includes the estimated time deviation generated by the physical operation subject in the action response stage and the estimated energy consumption deviation generated in the energy consumption stage. Step S4: The predicted feature deviation is introduced as an endogenous decision parameter within the system into the objective function of the multi-objective optimization algorithm. The theoretical operating cost based on the atomic operation sequence is fed forward and quantitatively verified to calculate the comprehensive cost after internalizing the inherent kinematic characteristics and dynamic loss characteristics of the physical equipment. Step S5: Perform multi-objective optimization based on comprehensive cost and issue a pre-calibration instruction set to the physical operation layer. The pre-calibration instruction set is used to offset the mechanical response lag of the physical operation entity and the performance loss offset caused by positioning deviation. 2.The warehouse full-process digitalization-based job flow dynamic management and control optimization method according to claim 1, characterized in that, Step S1 includes: Step S11, acquiring material location, equipment operating status, and warehouse environmental parameter data transmitted back by the visual sensor, radio frequency identification sensor, and lidar in the acquisition node array; Step S12, importing the data into the digital twin model, and synchronizing the spatial coordinate trajectory and load status of the physical operating entity in real time in the digital virtual space. 3.The warehouse full-process digitalization-based job flow dynamic management and control optimization method of claim 1, wherein, In step S2, the scheduling and control engine breaks down the warehousing operation tasks into the smallest running units according to their priority, and assigns the atomic operation sequence to the matching physical operation entity based on the current real-time location, remaining battery capacity, and maximum load capacity of each physical operation entity.

4. The warehouse full-process digitalization-based job flow dynamic management and control optimization method according to claim 1, characterized in that, The response feature mapping library includes a mapping matrix consisting of multiple sets of instruction parameters, operating entity identifiers, and historical operation feature associations; In step S3, the method for obtaining the estimated feature deviation includes: extracting the instruction parameters of the current atomic operation and querying the historical operation feature records corresponding to the operation subject identifier; The predicted characteristic deviation is calculated based on the average deviation between historical operating characteristic records and theoretical parameters.

5. The method of claim 1, wherein the method is based on warehouse full-process digitization. In step S4, the feedforward correction process includes: dynamically correcting the preset start time node and operation duration of the atomic operation using the estimated time deviation; and actively reserving dynamic redundancy generated by the acceleration, deceleration and end-positioning adjustments in the pre-calibration instruction set to neutralize the actual operation error caused by the mechanical inertia of the physical operating entity or the signal transmission delay.

6. The warehouse full-process digitalization-based job flow dynamic management and control optimization method according to claim 1, characterized in that, In step S5, the multi-objective optimization process uses an improved genetic algorithm or a constraint-based linear programming solver, and its optimization objectives include minimizing the total operation time evaluation value and minimizing the total energy consumption evaluation value. When evaluating candidate solutions, the optimization algorithm adds the sum of the predicted feature deviations of all atomic operations in the atomic operation sequence to its cost function, so as to achieve a systematic and accurate reconstruction of the physical operation process in the digital space.

7. The method for dynamic control and optimization of operational processes based on full-process digitalization of warehousing, as described in claim 1, is characterized in that... The method also includes anomaly identification: real-time comparison of the operation trajectory of each physical entity in the digital twin model with the preset trajectory of the pre-calibration instruction set; when the temporal deviation or spatial overlap between the operation trajectory and the preset trajectory exceeds the preset safety warning line, it is determined that there is an operation anomaly in the physical operation layer, and obstacle avoidance instructions or path replanning instructions are issued based on the global resource distribution status in the digital virtual space. 8.The warehouse full-process digitalization-based job flow dynamic management and control optimization method of claim 1, wherein, The method provides a continuous and visible control view of the entire process through a digital virtual space. The control view includes inbound progress billboards, warehouse area heat map, material tracking links, and equipment operation health index. The digital virtual space is updated synchronously in less than 1 second according to the feedback frequency of the data collection node array to ensure the compatibility of the control data with the operating rhythm of the physical operation entity.

9. The method for dynamic control and optimization of operational processes based on full-process digitalization of warehousing, as described in claim 1, is characterized in that... The method utilizes the collected operational characteristic data of the physical operating entities to identify slow time-varying factors such as equipment mechanical wear parameters and battery performance degradation coefficients; by dynamically updating the response feature mapping library, the generation strategy of the pre-calibration instruction set is synchronized with the current life cycle state of the physical operating entities, so as to maintain the operational energy efficiency stability of the warehousing system throughout its entire life cycle.

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