An automated control system for a warehouse roller conveyor

Abstract

The present application belongs to the automatic control technical field of warehouse logistics equipment, and specifically relates to an automatic control system of a warehouse roller conveying equipment, aiming at solving the defects of insufficient synchronization accuracy, weak fault resistance and limited coordination range caused by centralized control of the prior art. The automatic control system comprises a self-organizing roller unit, a digital twin synchronization mapping engine, a three-level super-system coordination link and an industrial communication network. The self-organizing roller unit realizes distributed synchronization calibration and load adaptive adjustment of adjacent units through multi-dimensional perception and a variable dimension module. The digital twin synchronization mapping engine constructs a 1:1 virtual model to complete working condition prediction and virtual calibration of synchronization error. The three-level super-system coordination link interfaces with a supply chain whole-process system to realize dynamic adaptation.

Description

Technical Field

[0001] This invention belongs to the field of automatic control technology for warehousing and logistics equipment, specifically an automated control system for a warehousing roller conveyor. Background Technology

[0002] Synchronous control of warehouse roller conveyor equipment often adopts a centralized control architecture: that is, a single central controller issues unified operating instructions (such as speed and torque parameters) to each roller conveyor unit to achieve synchronized operation of each unit. However, this architecture has problems such as synchronization deviation being easily affected by signal delay and local faults easily causing global interruption.

[0003] CN115258518B discloses a chain roller conveyor, including a horizontally placed support. A controller and a reducer are fixedly installed on the upper part of the support plate. A motor is fixedly installed on the outside of the reducer. The output end of the motor is fixedly connected to the input end of the reducer. A drive gear is keyed to the output end of the reducer.

[0004] It also includes: fixed rods, which are evenly fixedly installed on the upper end of the bracket, and each fixed rod is rotatably connected to a roller body on its outer side, and a first gear and a second gear are fixedly installed on one side end of the roller body;

[0005] The inner cylinder is integrally set inside the drum body, and a cavity is provided between the inner cylinder and the drum body. An oil hole is opened at the end of the drum body away from the first gear, and the oil hole is sealed by a screw.

[0006] A guide tube is provided at one end of the roller body near the first gear. The guide tube is connected to the cavity, and a blocking rod is provided in the cavity. An air bladder is provided on the outside of the blocking rod, and a liquid hole is opened inside the blocking rod.

[0007] The sealing plug is located inside the cavity;

[0008] A collection box is fixedly installed on the inner side of the bracket. A filter plate is movably connected to the inner side of the collection box. An abutment rod is fixedly installed on the upper surface of the filter plate. A movable plate is installed below the filter plate. Meanwhile, a receiving box is supported on the outer side of the collection box.

[0009] The cam is fixedly installed on the outer end of the leftmost roller body.

[0010] The main drawbacks of the related technologies are as follows: centralized control relies on remote signal interaction between the central controller and each roller unit. Under dynamic operating conditions (such as sudden changes in cargo load and fluctuations in conveying flow), signal transmission delays will lead to increased parameter synchronization deviations in each unit, which cannot meet the conveying requirements of high-precision goods such as precision electronic components and fragile items. At the same time, each roller unit is a passive execution unit without autonomous decision-making and coordination capabilities. If a partial failure occurs in a certain unit, it is necessary to rely on the central controller for fault diagnosis and instruction adjustment, which can easily trigger a chain reaction of local failure leading to global conveying interruption.

[0011] The existing system can only achieve partial collaboration within the warehouse conveyor line and cannot connect with upstream supplier delivery data or downstream delivery timeliness requirements. When encountering scenarios such as urgent upstream delivery or expedited downstream delivery, the system cannot adapt to the roller working conditions in advance, which can easily lead to conveying bottlenecks or idle resources, resulting in low overall warehouse operation efficiency.

[0012] In a centralized architecture, each roller unit passively executes unified instructions and cannot autonomously adjust its operating parameters according to dynamic load, resulting in high energy consumption. Furthermore, the architecture lacks flexibility and is difficult to adapt to warehousing scenarios of different sizes and types of goods, leading to high operation and maintenance costs.

[0013] Traditional centralized control architectures typically require high-cost core controllers and dedicated control lines for all roller units, resulting in high hardware procurement and on-site deployment costs. At the same time, the transmission delay of control commands in centralized architectures is easily affected by line length, synchronization accuracy is difficult to guarantee, and failures can easily lead to global shutdowns, further increasing maintenance costs.

[0014] The aforementioned defects make it difficult for existing warehouse roller conveyor systems to meet the high-speed, high-flexibility, and full-chain collaborative operation requirements of modern warehousing and logistics in terms of operating efficiency, stability, and adaptability. Therefore, an automated control system with autonomous collaboration of roller units as its core, and with the ability to improve synchronization accuracy, autonomous fault compensation, and cross-system adaptability is needed.

[0015] Therefore, an automated control system for warehouse roller conveyor equipment is proposed to address the above problems. Summary of the Invention

[0016] The purpose of this invention is to address the shortcomings of existing technologies by providing an automated control system for a warehouse roller conveyor, thereby solving the technical problems mentioned in the background art.

[0017] To achieve the above objectives, this application proposes an automated control system for a warehouse roller conveyor, comprising multiple self-organizing roller units. Each self-organizing roller unit has a built-in multi-dimensional sensing module and a variable moment module. The self-organizing roller unit can collect local sensing data related to the operation of the warehouse roller conveyor and realize distributed synchronous calibration and load adaptive adjustment of adjacent units of the warehouse roller conveyor based on the local sensing data.

[0018] The digital twin synchronization mapping engine communicates in real time with the self-organizing roller unit to construct a 1:1 virtual mapping model of the physical system corresponding to the storage roller conveyor equipment, so as to adapt to its operation and control requirements. It can receive historical conveying data and cross-system correlation data of the storage roller conveyor equipment, predict the subsequent working conditions and changing trends of the storage roller conveyor equipment, and output the optimal synchronization control parameters adapted to the operation of the storage roller conveyor equipment through virtual pre-simulation.

[0019] The three-level super system collaborative link connects to the upstream SRM system, the warehouse internal AGV scheduling system and the downstream delivery system through standardized interfaces to obtain data related to the operation of warehouse roller conveyor equipment, such as delivery urgency, AGV convergence requirements and delivery timeliness, and transforms them into dynamic adaptation basis for the synchronous control strategy of warehouse roller conveyor equipment.

[0020] Each organized roller unit interacts with the digital twin synchronization mapping engine and the three-level super system collaborative link through data exchange. Based on the optimal synchronization control parameters output by the digital twin synchronization mapping engine, and combined with the local perception data of the storage roller conveyor collected by itself, it autonomously participates in the collaborative determination of synchronization control parameters, thereby realizing the self-organized collaborative synchronization of the storage roller conveyor.

[0021] Preferably, the distributed synchronous calibration process of the self-organizing roller unit is as follows: adjacent units exchange real-time operating data through short-range industrial wireless communication. When the deviation of the operating parameters of any two adjacent units exceeds the preset synchronization threshold, a mutual calibration mechanism is triggered to adjust the output parameters until the deviation is reduced to within the threshold range.

[0022] Preferably, the self-organizing roller unit adopts the following load adaptive adjustment method: based on the load data collected by the multi-dimensional sensing module, the output torque is dynamically adjusted by the variable torque module; when the center of gravity of the goods is detected to be shifted, the three adjacent units synchronously trigger torque compensation to form a local load balance area.

[0023] Preferably, the working condition prediction process of the digital twin synchronization mapping engine is as follows: a working condition feature library is constructed based on historical transmission data; by comparing real-time working condition data with the feature library data, the subsequent working condition type and change trend are predicted; and a suitable synchronization strategy is selected based on the prediction results to enter the virtual pre-rehearsal stage.

[0024] Preferably, the virtual calibration process for synchronization error in the digital twin synchronization mapping engine is as follows: Synchronization data such as rotational speed difference and phase difference of the physical system are collected; the error propagation path is simulated in the virtual model; and the calibration amount of each unit is calculated using a back-calculation algorithm. The quantification formula for the back-calculation calibration amount is:

[0025] ;

[0026] In the formula Let be the calibration value for the i-th unit. The phase difference weighting coefficient is used. Let i be the phase difference of the i-th unit. The weighting coefficient is the speed difference. Let be the rotational speed difference of the i-th unit.

[0027] Preferably, the upstream prediction process of the three-level super system collaborative link is as follows: obtain supplier delivery data by connecting to the SRM system through a standardized interface; when the delivery data is detected to meet the preset emergency conditions, adjust the parameter threshold of the synchronization control strategy in advance to increase the unit torque reserve and improve the synchronization response sensitivity.

[0028] Preferably, the digital twin synchronization mapping engine employs an evolutionary control algorithm for operational complexity evaluation. The operational complexity evaluation process of the evolutionary control algorithm involves dynamic iterative evaluation based on the deviation between real-time and historical operational conditions and the synchronization error deviation. The iterative evaluation formula is as follows: ;

[0029] In the formula This is the evaluation value after iteration. This is the initial evaluation value. Weighting for changes in operating conditions For operating condition deviation, For stability weights, The synchronization error deviation is... .

[0030] Preferably, the data collected by the multi-dimensional sensing module of the self-organizing roller unit includes at least load data, rotational speed data, vibration data, and distance data between adjacent units. The sensing data is then processed by noise reduction and synchronously uploaded to the digital twin synchronous mapping engine and the adjacent self-organizing roller units.

[0031] Preferably, the data interaction between the self-organizing roller unit and the digital twin synchronization mapping engine is achieved through a hybrid communication network, which is a combination of an industrial wireless communication network and a wired industrial bus. The self-organizing roller units communicate with each other using industrial wireless communication, and the self-organizing roller unit communicates with the digital twin synchronization mapping engine using a wired industrial bus.

[0032] Preferably, the core warehousing link process of the three-level super system collaborative link is as follows: real-time interaction with the AGV scheduling system to exchange the status data of the intersection area, the digital twin synchronous mapping engine to calculate the optimal intersection timing, and control the self-organizing roller unit of the intersection area to adjust the operating parameters to achieve interference-free collaboration with the AGV.

[0033] The beneficial effects of this invention are:

[0034] This invention replaces the traditional centralized control architecture with a distributed architecture of self-organizing roller units, realizing autonomous synchronous calibration and adaptive load adjustment between adjacent roller units, improving synchronization accuracy by more than 30%; it can automatically fill in the gaps when a unit fails, avoiding global shutdown and significantly enhancing system stability; relying on the three-level super system collaborative link, it can connect to the entire supply chain data, improving warehouse operation efficiency by more than 25%.

[0035] This invention uses mass-produced general-purpose components, and the cost of a single self-organizing roller unit is controllable. At the same time, the hybrid communication architecture significantly reduces wiring costs, and the load adaptive adjustment can reduce system energy consumption by 15%-20% and reduce operation and maintenance costs by about 18%. The initial hardware incremental investment can be fully recovered within 2 years through energy consumption, operation and maintenance and wiring cost savings, and the long-term application has significant cost-effectiveness advantages. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] In the attached diagram:

[0038] Figure 1 This is a schematic diagram of the overall collaborative process of the present invention;

[0039] Figure 2 This is a schematic diagram of the operation of the self-organizing roller unit in this invention;

[0040] Figure 3 This is a flowchart illustrating the operation of the digital twin synchronization mapping engine in this invention;

[0041] Figure 4 This is a schematic diagram of the workflow of the three-level supersystem collaborative link in this invention. Detailed Implementation

[0042] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0043] Specific implementation examples are given below.

[0044] This invention discloses an automated control system for a warehouse roller conveyor. The core architecture includes: multiple self-organizing roller units, a digital twin synchronization mapping engine, a three-level super-system collaborative link, and an industrial communication network connecting all components. Each component interacts with the others in real time via the industrial communication network. The self-organizing roller units, acting as execution terminals, possess autonomous sensing, autonomous calibration, and adaptive adjustment capabilities. The digital twin synchronization mapping engine, as the core decision-making hub, enables condition prediction and synchronization parameter optimization. The three-level super-system collaborative link achieves dynamic adaptation between the system and the entire supply chain, ultimately achieving the core goal of self-organizing collaborative synchronization. Detailed descriptions of each core component are provided below.

[0045] The self-organizing roller unit is the core execution unit of this system. Each roller independently integrates a multi-dimensional sensing module, a variable torque module (used to dynamically adjust the output torque of the roller to adapt to load changes), and an embedded control module. It can realize distributed synchronous calibration and load adaptive adjustment of adjacent units based on local sensing data, and can complete local coordination without relying on a centralized controller, thus avoiding the risk of single point of failure.

[0046] The embedded control module uses an industrial-grade general-purpose microprocessor. The multi-dimensional sensing module includes mass-produced pressure sensing components, speed sensing components, and vibration sensing components. The variable torque module is integrated based on a conventional torque adjustment component. All of the above components are mature mass-produced products in the industrial field and do not require custom development. Based on bulk procurement calculations (referring to the 2024 industrial component market price and the typical negotiation space for a purchase quantity of 1000 sets), when the purchase quantity is ≥1000 sets, the hardware cost of a single self-organizing roller unit can be controlled within 200 yuan, which is more than 30% lower than the cost-sharing of the traditional solution of "centralized controller + passively executed roller".

[0047] 1. Multi-dimensional Sensing Module: This module collects core data supporting synchronous control and adaptive adjustment. The collected data includes at least load data, rotational speed data, vibration data, and distance data between adjacent units. Load data is collected via a pressure sensing component positioned on the roller surface (exemplarily, a thin-film pressure sensor can be used, deployed at three evenly distributed collection points on the roller surface to ensure comprehensive load data collection). Rotational speed data is collected via a rotational speed sensing component linked to the roller shaft. Vibration data is collected via a vibration sensing component integrated into the unit housing for predicting unit faults. Distance data between adjacent units is collected via a short-range ranging component to determine the range of adjacent collaborative units. All collected sensing data undergoes noise reduction processing (exemplarily, a mean filtering algorithm can be used to remove electromagnetic interference noise from the industrial environment). The noise-reduced data stream is synchronously uploaded to the digital twin synchronous mapping engine and adjacent self-organizing roller units, providing data support for distributed collaboration.

[0048] 2. Variable Torque Module: Linked with the roller's drive mechanism, this module dynamically adjusts the roller's output torque to adapt to different load conditions. It features a built-in torque adjustment component, enabling continuous adjustment of the output torque based on control commands from the embedded control module. The adjustment range covers the typical load requirements of warehousing and conveying scenarios.

[0049] The multi-dimensional sensing module and the variable torque module are essential technical components for realizing self-organizing collaborative control. The two work together and are indispensable: if the multi-dimensional sensing module is missing, the self-organizing roller unit cannot obtain operating status data such as load, speed, and vibration, and functions such as distributed synchronous calibration and fault prediction will lose their data foundation; if the variable torque module is missing, the self-organizing roller unit cannot convert the sensing data into torque adjustment actions, and the core technical problems of insufficient synchronization accuracy and load imbalance will still not be solved.

[0050] 3. Distributed Synchronous Calibration Process: Adjacent self-organizing roller units exchange operating data (including core parameters such as speed, torque, and load) in real time via short-range industrial wireless communication. The embedded control module compares the deviation of operating parameters between its own unit and adjacent units in real time. When the deviation of operating parameters between any two adjacent units exceeds a preset synchronization threshold, a mutual calibration mechanism is triggered: the unit with the larger deviation acts as the adjustment end, adjusting its own output parameters according to the direction of the deviation (e.g., low speed, insufficient torque), while the unit with the smaller deviation acts as the reference end, continuously feeding back real-time parameters to the adjustment end until the deviation of operating parameters between the two units falls within the preset synchronization threshold range. The preset synchronization threshold here can be flexibly set according to the accuracy requirements of warehousing and conveying (for example, for the scenario of conveying precision electronic components, the synchronization threshold can be set at a lower level; for the scenario of conveying conventional goods, the synchronization threshold can be appropriately relaxed). Those skilled in the art can reasonably select the threshold according to the actual application scenario, all of which fall within the protection scope of this invention.

[0051] The parameter deviation comparison, calibration triggering, and adjustment process of the aforementioned adjacent units is the core embodiment of the collaborative determination of synchronous control parameters. Without the need for instructions from the central controller, the units autonomously and collaboratively determine the adjustment range and calibration rhythm of their respective output parameters through real-time data interaction between adjacent units, ultimately achieving a local synchronous consensus.

[0052] 4. Load Adaptive Adjustment Mode: After the multi-dimensional sensing module collects load data, the embedded control module analyzes the load data to determine the current load size and the center of gravity position of the goods. When the load is detected to be at a normal load and the center of gravity is centered, the variable torque module outputs a reference torque to ensure that the roller runs at a uniform speed. When the center of gravity of the goods is detected to be offset (for example, the offset is determined by the load difference of multiple pressure sensing points; if the pressure value on one side is more than 30% higher than that on the other side, it is determined to be an offset), the embedded control module sends a torque compensation command to the three adjacent units. The variable torque modules of the adjacent units synchronously adjust their output torque to form a local load balance area, avoiding the tilting of goods or conveying jams caused by the offset of the center of gravity.

[0053] During the load balancing adjustment process, the three adjacent units will exchange real-time load data through short-range industrial wireless communication and work together to determine their respective torque compensation amounts (such as the unit on the side of the center of gravity offset bearing higher compensation torque, and the other units assisting in the adaptation), ensuring that the local load balance is not affected by the overall synchronization accuracy.

[0054] The digital twin synchronization mapping engine is the core decision-making hub of this system. It is deployed in the industrial control server and communicates with all self-organizing roller units in real time through the industrial communication network. Its core functions include building a 1:1 virtual mapping model of the physical system, predicting operating conditions, and virtually calibrating synchronization errors, providing optimal synchronization control parameters for the self-organizing roller units.

[0055] 1. Construction of a 1:1 Virtual Mapping Model: Based on the physical structure, connection relationships, and operating parameters of the warehouse roller conveyor, a virtual mapping model is constructed that perfectly matches the physical system. The model includes a virtual mirror image of each self-organizing roller unit, synchronizing in real-time the operating status (speed, torque, load, vibration, etc.), position information, and adjacency relationships of the physical units, achieving a full-dimensional virtual mapping of the physical system's state. The update frequency of the virtual mapping model is consistent with the data acquisition frequency of the physical system's operation, ensuring real-time synchronization between the virtual model and the physical system.

[0056] 2. Operating Condition Prediction Process: First, an operating condition feature library is constructed based on historical transportation data. The historical transportation data includes cargo type, transportation flow rate, load changes, environmental parameters (such as temperature and humidity), and synchronous control parameters over a period of time. Then, feature extraction is performed on the historical transportation data to obtain feature vectors for different operating condition types (e.g., peak transportation conditions are characterized by large transportation flow rate and frequent load fluctuations, while normal transportation conditions are characterized by stable transportation flow rate and uniform load). The feature vectors are then stored in the operating condition feature library.

[0057] During system operation, real-time data on current operating conditions is collected and feature vectors are extracted. By comparing the real-time feature vectors with feature vectors in the operating condition feature library, the subsequent operating condition type and trend are predicted (for example, when the similarity between the real-time feature vector and the peak delivery operating condition feature vector exceeds a preset threshold, it is predicted that the system will enter the peak delivery operating condition). Finally, based on the prediction results, suitable synchronization strategies are selected, and the selected synchronization strategies are input into the virtual mapping model for pre-running to verify the feasibility and adaptability of the strategies.

[0058] 3. Virtual calibration process for synchronization error: First, the synchronization data of each unit in the physical system is collected through the industrial communication network, including the speed difference (the difference between the speed of each unit and the reference speed) and the phase difference (the difference between the operating phases of each unit). Then, the synchronization data is input into the virtual mapping model to simulate the transmission path of synchronization error in the physical system (for example, simulating the impact of the speed deviation of a single unit on adjacent units and the diffusion trend of the deviation). Finally, the calibration amount of each unit is calculated through the reverse inference algorithm to ensure that the synchronization error of each unit is reduced to the preset range after calibration.

[0059] The quantification formula for the reverse-engineering calibration quantity is:

[0060] ;

[0061] In the formula, This is the calibration amount for the i-th unit (specifically, the adjustment amount of speed or torque). The phase difference weighting coefficient is used. Let i be the phase difference of the i-th unit. The weighting coefficient is the speed difference. Let be the rotational speed difference of the i-th unit.

[0062] The core of this formula is to solve the problem of "asynchronous rotation of the roller units". The asynchrony mainly comes from two actual situations, which correspond to the two variables in the formula:

[0063] First variable (Phase difference): This refers to the "inconsistency in the pace" of the unit rotation. For example, when the i-th unit has rotated half a circle, the adjacent unit has only rotated one-third of a circle, and the pace is not in sync.

[0064] The second variable (Difference in rotation speed): This refers to the different speeds of the unit rotation. For example, the i-th unit rotates 60 times per minute, while the adjacent unit rotates only 58 times per minute.

[0065] k and m are weighting coefficients used to balance the influence of two variables. The coefficient of the variable that causes more asynchrony is increased. For example, in the transportation of precision electronic components, "inconsistency in pace" ( If the impact is greater, increase k; when transporting heavy goods, "the speed is different" ( If the impact is greater, increase the value of m to ensure that the calibration can accurately solve the core problem.

[0066] In field testing, this solution was validated for the transport of precision electronic components (in which phase difference has a greater impact, so k=0.6 and m=0.4 were set). The synchronization data of the 8th self-organizing roller unit (i.e., the "i-th unit" in the formula, where i=8) and the adjacent 9th unit were selected.

[0067] Measured corresponding data: Phase difference of unit 8 =0.4 degrees, rotational speed difference Δω8=0.3 revolutions / minute;

[0068] Substituting into the original formula above, the calibration value for unit 8 is calculated as follows:

[0069] ;

[0070] ( Completely corresponds to the original formula The calibration amount corresponds to the "speed adjustment ratio," and 0.36 means that the speed of unit 8 needs to be increased by 3.6%.

[0071] Results after calibration: After adjustment according to the calculation results, the synchronization error between unit 8 and unit 9 decreased from 1.1% to 0.4%, meeting the requirement of "synchronization error ≤ 0.5%" for precision electronic component transportation, and there was no deviation rebound after 24 hours of continuous operation.

[0072] If switching to heavy cargo conveying mode (where the speed difference has a greater impact), set k=0.3, m=0.7, and select unit 15 (i=15) for verification:

[0073] Actual measured data: 15=0.5 degrees, 15 = 0.6 revolutions per minute;

[0074] Substitute into the original formula to calculate: ;

[0075] Results after calibration: Synchronization error decreased from 1.5% to 0.6%, preventing delays in the transport of heavy goods.

[0076] The optimal parameters (including the initial values ​​of k and m) issued by the digital twin engine serve as a global unified benchmark. Each organizational roller unit combines its own local sensing data (such as real-time load and deviation between adjacent units) and interacts with surrounding units through the industrial communication network to verify and collaboratively determine the final personalized synchronization control parameters. For example, based on the global k=0.6 and m=0.4, a unit with a larger load will fine-tune m to 0.45 to ensure consistent synchronization. While maintaining stability, it adapts to speed fluctuations caused by load. change).

[0077] The reason for adopting this "global benchmark + local collaboration" approach is that the core requirements of synchronous control are concentrated between adjacent units. Direct data interaction in a local area is faster and can quickly adapt to local dynamic operating conditions (such as multiple goods suddenly overlapping or the center of gravity shifting in a certain section of the conveyor line). At the same time, it avoids the data transmission delay and parameter adaptation rigidity problems caused by global negotiation, and truly achieves the core goal of distributed self-organizing collaboration.

[0078] 4. Operating Complexity Evaluation Process of Evolutionary Control Algorithm: Operating complexity evaluation provides a basis for selecting synchronization strategies. The evaluation process is based on dynamic iterative evaluation of the deviation between real-time and historical operating conditions, and the deviation of synchronization errors. Specifically, real-time operating condition data and historical data of similar operating conditions are first acquired, and the deviation between the two is calculated. ( This is a dimensionless parameter, obtained through normalization, with a value range of 0 to 1. The larger the value, the greater the difference between real-time and historical operating conditions; then, the deviation between the current synchronization error and the preset synchronization error is calculated. ( Also a dimensionless parameter, ranging from 0 to 1; the larger ΔS is, the further the synchronization error deviates from the preset value. Finally, the operational complexity evaluation value is calculated using an iterative formula. The iterative formula is:

[0079] ;

[0080] In the formula, This represents the evaluated complexity value of the operating conditions after iteration. This is the initial assessment value (the initial assessment value is determined based on the complexity of similar historical working conditions). Weighting for changes in operating conditions As stability weights, and .

[0081] The design logic of this iterative formula is as follows: the complexity of the operating conditions is determined by the degree of change in the operating conditions and the stability of the system. The complexity evaluation value is updated in real time through dynamic iteration to ensure the timeliness of the evaluation results.

[0082] in, and The value of needs to be determined based on the requirements of the scenario: when priority is given to the impact of changes in operating conditions on synchronous control, it can be increased. The value of can be increased when prioritizing the impact of system stability on synchronization control. The value of ; for example, in scenarios where dynamic operating conditions change frequently, The value can be set to 0.6. The value can be set to 0.4; in scenarios with high stability requirements, The value can be set to 0.4. The value can be set to 0.6.

[0083] The operational complexity assessment value calculated using this formula ,when When the complexity exceeds a preset threshold, the system selects a synchronization strategy adapted to the complex operating conditions (such as a fuzzy PID control strategy); when When the complexity threshold is less than or equal to the preset threshold, the system selects a conventional synchronization strategy (such as the classic PID control strategy) to achieve adaptive matching of the control algorithm.

[0084] The three-tiered super-system collaborative link refers to a three-level cross-system collaborative channel built around warehouse roller conveyor equipment, covering the entire supply chain process. In simple terms, the three levels correspond to three distinct collaborative layers, interconnected to form a closed-loop chain: The upstream collaborative layer specifically connects to the upstream SRM (Supplier Relationship Management) system, its core function being to obtain data such as supplier delivery plans and urgent orders, allowing the conveyor equipment to adapt to incoming material needs in advance; the warehouse core collaborative layer focuses on the internal warehouse operations, connecting to the AGV scheduling system, its core function being to coordinate the operation of the conveyor equipment and AGVs, avoiding cross-interference and improving internal warehouse flow efficiency; the downstream collaborative layer connects to the downstream delivery system, its core function being to obtain data such as delivery timeliness and order priority, allowing the conveyor equipment to dynamically adjust its conveying rhythm to match outbound delivery needs. Secondly, the concept of a supersystem is relative to traditional single-equipment or single-system internal systems in warehousing. It refers to a cross-system collection that transcends the physical boundaries of the equipment itself and the warehouse. Simply put, it's not just about making the conveyor equipment work alone, but about including upstream suppliers, internal warehouse systems, and downstream distribution systems in the collaborative scope, forming a collaborative system covering the entire process from receiving materials, warehousing to outbound delivery. Finally, the collaborative link is the bridge that enables data interoperability within this large system. Standardized interfaces allow data to flow smoothly between systems at each level, ultimately allowing the control strategies of the conveyor equipment to adapt to changes in operating conditions throughout the entire link. This collaborative link connects to the aforementioned three levels of related systems through standardized industrial interfaces, achieving synchronous control strategies and dynamic adaptation to the entire supply chain process, breaking the limitations of isolated collaboration within traditional warehousing systems. The standardized industrial interfaces here are conventional interfaces in the industrial field, such as OPCUA interfaces and Profinet interfaces, which can be used as examples. Those skilled in the art can choose the appropriate interface type based on the interface type of the system being connected.

[0085] 1. Upstream Prediction Process: A standardized industrial interface is used to establish bidirectional communication with the upstream SRM system in the supply chain, enabling real-time acquisition of supplier delivery data. This data includes the type of goods delivered, delivery batch size, delivery time, and delivery urgency. The system incorporates delivery data judgment logic. When delivery data meets preset urgency conditions (for example, preset urgency conditions include delivery time ≤ 2 hours, urgent order marking, and delivery batch size ≥ preset batch threshold; these preset urgency conditions can be flexibly set according to warehouse operation needs), the digital twin synchronization mapping engine adjusts the parameter thresholds of the synchronization control strategy in advance. Specifically, this includes increasing the torque reserve of the self-organizing roller unit (ensuring rapid adaptation to the load requirements of large-volume goods) and improving synchronization response sensitivity (shortening the response time for parameter calibration), thus avoiding conveying bottlenecks caused by untimely adaptation of the synchronization strategy after goods arrive at the warehouse.

[0086] 2. Core Warehousing Process: Real-time data exchange with the warehouse's internal AGV scheduling system is achieved through a standardized industrial interface. This exchanged data includes the AGV's route, location information, and the status of the intersection area (e.g., whether there are goods in the intersection area, whether the AGV is about to arrive). Based on this exchanged data and combined with the virtual mapping model's pre-simulation function, the digital twin synchronous mapping engine calculates the optimal intersection timing between the AGV and the roller conveyor line (the criteria for determining the optimal intersection timing are "no goods collision, no conveying interruption." For example, 10 seconds before the AGV arrives at the intersection area, the roller conveyor line is controlled to complete the conveying of goods in the intersection area, freeing up working space for the AGV). Then, parameter adjustment commands are sent to the self-organizing roller units in the intersection area to control their adjustment of operating parameters (e.g., speed, conveying direction), achieving interference-free collaboration with the AGV and improving warehousing operation efficiency.

[0087] 3. Downstream Adaptation Link (Example): It connects with the downstream delivery system through a standardized industrial interface to obtain the timeliness requirements of delivery orders, dispatch information of delivery vehicles, etc.; the digital twin synchronization mapping engine adjusts the synchronization control strategy according to the delivery timeliness requirements. For example, for expedited delivery orders, a high-speed and precise synchronization mode is adopted to shorten the dwell time of goods on the conveyor line; for regular delivery orders, an energy-saving synchronization mode is adopted to reduce energy consumption while ensuring synchronization accuracy, so as to achieve dynamic adaptation between conveying efficiency and delivery needs.

[0088] The data interaction between the self-organizing roller unit and the digital twin synchronous mapping engine is achieved through a hybrid communication network, which is a combination of an industrial wireless communication network and a wired industrial bus. The rationale for this design is as follows: the self-organizing roller units require frequent local data interaction, and the units are relatively dispersed. Using industrial wireless communication can reduce wiring costs and improve deployment flexibility. On the other hand, the data flow between the self-organizing roller unit and the digital twin synchronous mapping engine is large (including sensing data, control commands, etc.), and the requirements for communication stability and real-time performance are high. Using a wired industrial bus can ensure the reliability of data transmission.

[0089] Compared to traditional wired control networks, the above-mentioned hybrid communication architecture can reduce the amount of on-site wiring work by 80%. Taking a 100-meter conveyor line as an example, the cost of wiring materials and construction can be reduced by 4,000-6,000 yuan, effectively offsetting the incremental hardware cost of the self-organizing roller unit.

[0090] Industrial wireless communication can employ conventional industrial wireless communication technologies such as industrial WiFi and LoRa, while wired industrial buses can employ conventional industrial bus technologies such as Profibus and Modbus. Those skilled in the art can choose the appropriate technology based on actual communication needs, and all fall within the scope of this invention. Simultaneously, the industrial communication network incorporates a data transmission priority mechanism, setting core data such as synchronization control commands and error calibration data as high priority to ensure priority transmission of core data; and setting non-core data such as historical data uploads and status statistics as low priority to avoid non-core data consuming excessive communication bandwidth and affecting the implementation of core functions.

[0091] The following section describes the entire collaborative process of the system using specific application scenarios:

[0092] Scenario: An upstream supplier has a batch of urgent electronic components that need to be delivered to the warehouse within 2 hours, while the downstream delivery system requires the components to be shipped out within 1 hour.

[0093] 1. Upstream Collaboration Phase: The three-level super system collaboration link obtains the emergency delivery data by connecting to the SRM system. If the delivery time is ≤2 hours and meets the preset emergency conditions, an early warning signal is sent to the digital twin synchronization mapping engine. The digital twin synchronization mapping engine adjusts the parameter thresholds of the synchronization control strategy, increases the torque reserve of the respective organizational roller units, and improves the synchronization response sensitivity.

[0094] 2. Working Condition Prediction Stage: The digital twin synchronization mapping engine builds a feature library based on historical emergency delivery working condition data. After comparing with real-time data, it predicts that the subsequent high-precision delivery working condition will be entered. It selects suitable high-precision adaptive synchronization strategies, performs a pre-play in the virtual mapping model, and determines the optimal synchronization control parameters after verifying the feasibility of the strategies.

[0095] 3. Conveying Synchronization Phase: After the emergency electronic components arrive at the warehouse, the multi-dimensional sensing module of the self-organizing roller unit collects data such as load and speed, and after noise reduction processing, it is synchronously uploaded to the adjacent units and the digital twin synchronization mapping engine; the adjacent units ensure local synchronization accuracy through distributed synchronization calibration; when the center of gravity of some components is detected to be off, the three adjacent units synchronously trigger torque compensation to form a local load balance area.

[0096] In this scenario, the collaborative determination of synchronous control parameters is more dynamic. Each unit not only relies on its own perception data, but also synchronously receives the convergence demand data from the AGV scheduling system. Through cross-unit data interaction, the operating parameters are collaboratively adjusted (such as the convergence area units negotiating to reduce the speed to match the AGV picking rhythm), ensuring a dual adaptation between synchronization accuracy and warehouse collaboration requirements.

[0097] 4. Warehouse Collaboration Stage: The AGV scheduling system sends a signal to the three-level super system collaboration link that the goods need to be picked up in the intersection area; the digital twin synchronous mapping engine calculates the optimal intersection timing, controls the self-organizing roller unit in the intersection area to adjust the speed, and delivers the goods to the picking position in advance, realizing interference-free collaboration with the AGV.

[0098] 5. Downstream Adaptation Stage: The three-level super system collaborative link obtains the expedited delivery instructions from the downstream delivery system. The digital twin synchronous mapping engine adjusts the synchronization strategy to a high-speed and accurate synchronization mode to shorten the cargo transportation time and ensure timely outbound delivery.

[0099] 6. Fault Response Phase (Example): If the vibration data of a certain self-organizing roller unit is abnormal, the multi-dimensional sensing module will immediately send an early warning to the adjacent units and the digital twin synchronous mapping engine after detection; the adjacent units will automatically trigger the compensation logic and adjust their own torque to make up for the conveying power of the faulty unit; the digital twin synchronous mapping engine will simulate the scope of the fault in the virtual model and optimize the synchronization parameters of other units to ensure continuous synchronous conveying.

[0100] Verification of this technical solution: 30 self-organizing roller units were deployed on a 50-meter conveyor line. The test system was built using the above-mentioned mass-produced components and operated continuously and stably for 1200 hours. During this period, the accuracy of the sensing data acquisition was ≥98.5%, the distributed synchronous calibration response time was ≤30ms, the torque adjustment error was ≤±2%, and there were no module failures or data loss. It meets the operating requirements of industrial-grade conveyor equipment.

[0101] To further verify the feasibility of the solution, this technical solution underwent a three-month field application test in a medium-sized intelligent warehousing park. The test environment and data are as follows:

[0102] Experimental hardware configuration: The test object is a 50-meter-long warehouse roller conveyor line, with 30 sets of self-organizing roller units deployed; the embedded control module uses an industrial-grade STM32F407 microprocessor, the pressure sensing component uses a mass-produced FSR402 thin-film sensor, the speed sensing component is a Hall-effect speed sensor, the vibration sensing component uses a MEMS microelectromechanical sensor, and the variable torque module is based on a modification of a conventional DC geared motor; the comparison group is a traditional centralized control system of the same specifications (the core controller is a Siemens S7-1500, with fully wired cabling).

[0103] Test conditions and specific data:

[0104] Operating Condition 1: Uniform conveying of conventional load (single cargo weight 20kg, center of gravity centered, conveying speed 0.8m / s) This solution: synchronization error is stable within 0.5%, torque adjustment response time ≤25ms, and energy consumption is 12.6kWh for continuous operation for 8 hours; Traditional solution: synchronization error is 1.8%, torque adjustment response time ≥80ms, and energy consumption is 15.3kWh for continuous operation for 8 hours;

[0105] Working Condition 2: Center of Gravity Offset Conveying (Single cargo weight 30kg, center of gravity offset 35% on one side, conveying speed 0.6m / s) This solution: Torque compensation response time of 3 adjacent units is 30ms, cargo is not tilted or jammed, and synchronization error is 0.7%; Traditional solution: cargo tilt risk rate is 60%, synchronization error is 2.3%, and manual intervention is required for adjustment;

[0106] Operating Condition 3: Upstream Emergency Delivery Peak (120 batches of goods received continuously within 2 hours, with each batch weighing 5-30kg) This solution: Emergency data is obtained in advance through the three-level super system collaborative link, torque reserve is increased by 30% in advance, there are no transportation bottlenecks, and the average transit time of goods is 45 seconds / batch; Traditional solution: There are 3 transportation congestion events, the average transit time of goods is 78 seconds / batch, and the synchronization deviation causes two batches of goods to slightly collide.

[0107] Long-term stability and fault testing: During the test, the line ran for a total of 1,800 hours, including two simulated unit faults (manually shutting down the drive function of units 12 and 25). Adjacent units triggered the compensation logic within 2 seconds, adjusting the torque to make up for the power gap. There was no global shutdown, and the overall operational stability of the conveyor line reached 99.8%. The accuracy of the sensing data acquisition rate remained above 98.5%, and there were no control failures caused by data packet loss or transmission delay.

[0108] The automated control system of the warehouse roller conveyor in this embodiment, through its core architecture of self-organizing roller units, digital twin synchronous mapping engine, and three-level super system collaborative link, achieves the following technical effects compared to existing technologies:

[0109] The synchronization accuracy is improved by more than 30%, effectively solving the synchronization error problem under dynamic operating conditions through the dual guarantee of distributed synchronization calibration and virtual error calibration; it solves the synchronization deviation problem caused by signal transmission delay in centralized control in the background technology, and can meet the transportation needs of high-precision goods such as precision electronic components and fragile items; the system response speed is improved by 50%, and the working condition prediction function of digital twin enables the early adaptation of synchronization strategy and avoids lagging adjustment; the fault self-healing capability is significantly enhanced, and the self-organizing unit's autonomous diagnosis and compensation logic shortens the fault self-healing time to the second level; it solves the defects of the roller unit as a passive execution unit in the background technology, and the chain reaction defect that local failure can easily lead to global transportation interruption, greatly improving the system's operational stability; the scope of collaboration is expanded to the entire supply chain process, realizing the collaborative optimization of upstream delivery, warehousing operations, and downstream distribution, improving the overall warehousing operation efficiency, breaking the limitation of the system in the background technology that can only achieve local collaboration within the warehouse, and successfully adapting to the fluctuating working conditions of the entire supply chain, such as upstream emergency delivery and downstream expedited delivery.

[0110] In the description of this invention, it should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions provided in this disclosure can be achieved, and no limitation is imposed herein.

[0111] The above description is merely a preferred embodiment of the present invention and does not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. An automated control system for a warehouse roller conveyor, characterized in that, include: Multiple self-organizing roller units, each of which has a built-in multi-dimensional sensing module and a variable moment module, replace the traditional centralized control architecture with distributed data interaction. The self-organizing roller unit can collect local sensing data related to the operation of the storage roller conveyor and realize distributed synchronous calibration and load adaptive adjustment of adjacent units of the storage roller conveyor based on the local sensing data. The digital twin synchronous mapping engine communicates in real time with the self-organizing roller unit to construct a virtual mapping model of the physical system corresponding to the storage roller conveyor equipment in order to adapt to its operation and control requirements. The digital twin synchronous mapping engine can receive historical conveying data and cross-system correlation data of the storage roller conveyor equipment, predict the subsequent operating conditions of the storage roller conveyor equipment, and output synchronous control parameters adapted to the operation of the storage roller conveyor equipment through virtual pre-simulation. The three-level super system collaborative link connects to the upstream SRM system, the warehouse internal AGV scheduling system, and the downstream delivery system through standardized interfaces to obtain data related to the operation of the warehouse roller conveyor equipment. This data is then transformed into a dynamic adaptation basis for the synchronous control strategy of the warehouse roller conveyor equipment.

2. The automated control system for the storage roller conveyor according to claim 1, characterized in that, The distributed synchronous calibration process of the self-organizing roller unit is as follows: adjacent units exchange real-time operating data through short-range industrial wireless communication. When the deviation of the operating parameters of any two adjacent units exceeds the preset synchronization threshold, a mutual calibration mechanism is triggered to adjust the output parameters until the deviation is reduced to within the threshold range.

3. The automated control system for the storage roller conveyor according to claim 1, characterized in that, The self-organizing roller unit's load adaptive adjustment method is as follows: based on the load data collected by the multi-dimensional sensing module, the output torque is dynamically adjusted through the variable torque module; when the center of gravity of the goods is detected to be shifted, the three adjacent units synchronously trigger torque compensation to form a local load balance area.

4. The automated control system for the storage roller conveyor according to claim 1, characterized in that, The working condition prediction process of the digital twin synchronization mapping engine is as follows: a working condition feature library is built based on historical transmission data. By comparing real-time working condition data with the feature library data, the subsequent working condition types and trends are predicted. Based on the prediction results, a suitable synchronization strategy is selected to enter the virtual pre-rehearsal stage.

5. The automated control system for the storage roller conveyor according to claim 1, characterized in that, The virtual calibration process of the synchronization error of the digital twin synchronization mapping engine is as follows: collect synchronization data such as rotational speed difference and phase difference of the physical system, simulate the error propagation path in the virtual model, and calculate the calibration amount of each unit through the reverse inference algorithm. The calibration amount is determined based on the weight ratio of phase difference and rotational speed difference.

6. The automated control system for the storage roller conveyor according to claim 1, characterized in that, The upstream prediction process of the three-level super system collaborative link is as follows: obtain supplier delivery data by connecting to the SRM system through a standardized interface; when the delivery data is detected to meet the preset emergency conditions, adjust the parameter threshold of the synchronization control strategy in advance to increase the unit torque reserve and improve the synchronization response sensitivity.

7. The automated control system for the storage roller conveyor according to claim 1, characterized in that, The digital twin synchronization mapping engine uses an evolutionary control algorithm to evaluate the complexity of operating conditions. The evaluation process is as follows: dynamic iterative evaluation is performed based on the deviation between real-time operating conditions and historical operating conditions, as well as the synchronization error deviation. The evaluation value is obtained by weighted iterative calculation of operating condition change weight and system stability weight.

8. The automated control system for the storage roller conveyor according to claim 1, characterized in that, The data collected by the multi-dimensional sensing module of the self-organizing roller unit includes at least load data, rotational speed data, vibration data, and distance data between adjacent units. The sensing data is uploaded synchronously to the digital twin synchronous mapping engine and the adjacent self-organizing roller unit after noise reduction processing.

9. The automated control system for the storage roller conveyor according to claim 1, characterized in that, The data interaction between the self-organizing roller unit and the digital twin synchronization mapping engine is achieved through a hybrid communication network, which is a combination of an industrial wireless communication network and a wired industrial bus. The self-organizing roller units communicate with each other using industrial wireless communication, while the self-organizing roller units communicate with the digital twin synchronization mapping engine using wired industrial bus communication.

10. The automated control system for the storage roller conveyor according to claim 1, characterized in that, The core process of the three-level super system collaborative link in warehousing is as follows: real-time interaction with the AGV scheduling system to exchange intersecting area status data, the digital twin synchronous mapping engine to calculate the optimal intersecting time, and control the self-organizing roller unit in the intersecting area to adjust the operating parameters to achieve interference-free collaboration with the AGV; the parameter adjustment instructions of the self-organizing roller unit are generated by distributed interaction between adjacent units, without the need for a dedicated linkage controller.

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