A logistics conveying line energy consumption management and safety control system

Abstract

The application belongs to the technical field and discloses a logistics conveying line energy consumption management and safety control system; the system comprises a power management module, a network communication module, a conveying line equipment module and a main control module. Load data, speed adjustment data and material tracking data of the conveying line equipment are collected, load fluctuation coefficients and speed switching frequencies are calculated, and multi-section speed control shock risk values are determined. Dynamic damping factors and feedforward compensation factors are introduced to generate optimized speed adjustment instructions, effectively reducing the shock risk. In combination with real-time power supply state data of the power management module, an energy consumption optimization distribution scheme is generated, and safety control instructions are generated based on operation state data and emergency stop signals. The association between the material tracking data and the speed adjustment instructions is tracked through a database, and an energy consumption tracking report is generated. The application significantly improves the operation stability of the logistics conveying line, reduces energy consumption, enhances system safety and improves material transmission efficiency.

Description

Technical Field

[0001] This invention relates to the field of technology, and more specifically, to an energy consumption management and safety control system for a logistics conveyor line. Background Technology

[0002] With the rapid development of industrial automation and logistics, the operational efficiency and energy consumption of logistics conveyor systems, as a core component of modern logistics systems, are becoming increasingly prominent issues. Currently, common logistics conveyor systems on the market typically consist of a conveying mechanism, drive unit, control unit, and power system. In practical applications, these systems often employ PID control algorithms based on fixed parameters or simple trapezoidal speed curves to achieve speed regulation, lacking the ability to accurately respond to real-time load changes. Especially in scenarios such as e-commerce, express delivery sorting, and manufacturing production lines, the diverse types of materials, uneven batches, and large flow fluctuations necessitate frequent speed switching of the conveyor line to adapt to changing working conditions.

[0003] When a conveyor line needs to adjust its speed according to changes in material load in different sections, frequent speed switching can easily cause system oscillations, leading to severe fluctuations in conveyor belt tension and frequent peak motor torque. This oscillation not only increases wear on mechanical components but also significantly increases energy waste, especially under high-load scenarios with frequent material transfers, where the oscillation effect is more pronounced. In actual production environments, a typical multi-segment logistics conveyor line often experiences material accumulation or transmission gaps during speed switching due to the lack of effective damping mechanisms and feedforward compensation strategies. This significantly reduces the success rate of material transfers and can even lead to frequent system start-ups and shutdowns. Furthermore, traditional control systems cannot accurately capture the complex correlation between load fluctuations and speed switching, lacking the ability to dynamically adjust based on real-time data. This results in a delayed response to sudden load changes, further exacerbating the oscillation effect. This oscillation not only affects conveying efficiency but also leads to unstable power supply to the power management system. In extreme cases, it may even trigger safety protection mechanisms, causing unexpected shutdowns of the entire production line and resulting in significant economic losses.

[0004] In view of this, the present invention proposes an energy consumption management and safety control system for logistics conveyor lines to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: an energy consumption management and safety control system for a logistics conveyor line, comprising:

[0006] The system comprises a power management module, a network communication module, a conveyor equipment module, and a main control module. Energy consumption management and safety control are achieved through the following methods:

[0007] The real-time load data, speed adjustment data, and material tracking data of each section of the conveyor equipment module are obtained through the horizontal interface of the network communication module during operation.

[0008] Based on the real-time load data and the speed adjustment data, calculate the load fluctuation coefficient and speed switching frequency for each segment; based on the load fluctuation coefficient and the speed switching frequency, determine the multi-segment speed control oscillation risk value for each segment; by superimposing a dynamic damping factor and a feedforward compensation factor into the speed adjustment data, generate an optimized speed adjustment command to reduce the multi-segment speed control oscillation risk value.

[0009] Based on the optimized speed adjustment command and combined with the real-time power supply status data transmitted by the power management module through the vertical interface, an energy consumption optimization allocation scheme for each section is generated; the energy consumption optimization allocation scheme is then transmitted to the conveyor equipment module through the vertical interface of the main control module.

[0010] Based on the operating status data of the conveyor equipment module collected by the network communication module through the horizontal interface and the emergency stop signal transmitted by the power management module through the vertical interface, a safety control command is generated; the safety control command is then transmitted to the conveyor equipment module through the vertical interface of the main control module to achieve safe shutdown or dynamic adjustment.

[0011] The tracking database of the main control module records the correlation between the material tracking data and the optimized speed adjustment command; based on the correlation, an energy consumption tracking report for each segment is generated.

[0012] The technical effects and advantages of the energy consumption management and safety control system for logistics conveyor lines of the present invention are as follows:

[0013] This invention effectively eliminates jitter, accumulation, and gaps in material transfer processes in traditional systems, significantly improving the success rate of material handover. Even in high-frequency material handover scenarios, the system maintains stable operation, effectively avoiding frequent equipment start-ups and shutdowns caused by vibrations, extending the lifespan of key components, and reducing maintenance costs and downtime losses. By eliminating ineffective energy consumption caused by vibrations, the overall energy consumption of the system is significantly reduced, creating considerable economic and environmental benefits for enterprises. Simultaneously, the reliability and safety of system operation are significantly enhanced, with a greatly improved response speed in emergencies, effectively preventing material damage and equipment failure risks caused by vibrations. In practical applications, material transfer efficiency is comprehensively improved, especially maintaining stable and efficient transfer capabilities under variable load conditions. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of an energy consumption management and safety control system for a logistics conveyor line according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] This application provides an energy consumption management and safety control system for a logistics conveyor line. The executing entities of this system include, but are not limited to, conveyor line equipment, control cabinets, industrial gateways, edge servers, etc., which can be considered general computing nodes in this application. The control cabinet includes, but is not limited to, at least one of an industrial PLC, a distributed control system, or a programmable automation controller.

[0017] Please see Figure 1 This invention provides an energy consumption management and safety control system for logistics conveyor lines, comprising: a power management module, a network communication module, a conveyor line equipment module, and a main control module. Wherein:

[0018] The power management module is used to provide system power supply and monitor power status. It has an IP54 protection rating and a wide operating temperature range, and supports multiple power level configurations.

[0019] The network communication module is used to establish the internal communication network and external data exchange interface of the system, and supports standard industrial communication protocols;

[0020] The conveyor line equipment module contains multiple sections of conveyor equipment, each section is equipped with a program block to realize the basic control functions of the equipment;

[0021] The main control module is used to coordinate the data interaction and control logic between various functional modules, and provides a component view to abstract complex physical devices into a hierarchical component structure.

[0022] This invention achieves standardized communication between modules through vertical and horizontal interfaces, and improves system reliability and maintainability through component-based hardware and software design; it manages material information flow through a built-in intelligent tracking mechanism, supporting precise material positioning and energy consumption optimization; and it achieves system function separation and collaboration through a multi-layer control architecture (LLC low-level control and HLC high-level control), meeting the stringent requirements of industrial logistics systems for reliability, flexibility, and energy efficiency.

[0023] In this embodiment of the invention, the system implements energy consumption management and safety control through the following detailed steps:

[0024] First, the horizontal interface of the network communication module acquires real-time load data, speed adjustment data, and material tracking data for each section of the conveyor equipment module during operation. The horizontal interface uses the standardized FIF protocol, including signal states such as RTS (Request to Send), RTR (Request to Receive), TIP (Transmit in Progress), and UA (Acknowledgement), supporting data transmission. The material tracking data includes the PIC (Package Identifier) ​​and its location information on the virtual conveyor belt, enabling the binding and tracking of material data with its physical location.

[0025] Based on the real-time load data and the speed adjustment data, the load fluctuation coefficient and speed switching frequency for each segment are calculated. The load fluctuation coefficient reflects the equipment load stability, and the speed switching frequency characterizes the dynamic characteristics of speed changes; both serve as key indicators for evaluating system operational stability. Based on the load fluctuation coefficient and the speed switching frequency, the system determines the multi-segment speed control oscillation risk value for each segment, quantifying the potential instability risk of the control system.

[0026] To reduce the risk of oscillation, an optimized speed regulation command is generated by superimposing a dynamic damping factor and a feedforward compensation factor into the speed regulation data. The dynamic damping factor is dynamically adjusted based on mechanical vibration characteristics to suppress oscillations during the control process; the feedforward compensation factor predictively adjusts control parameters based on material transfer data to anticipate load changes. Together, they reduce the oscillation risk of the multi-speed control.

[0027] Based on the optimized speed adjustment command and combined with the real-time power supply status data transmitted by the power management module through the vertical interface, the system generates an energy consumption optimization allocation scheme for each segment. This scheme considers power capacity, load demand, and transmission efficiency to optimize energy allocation and improve energy utilization. The energy consumption optimization allocation scheme is then transmitted to the conveyor equipment module via the vertical interface of the main control module, achieving precise energy control.

[0028] Simultaneously, based on the operating status data of the conveyor equipment module collected by the network communication module through the horizontal interface and the emergency stop signal transmitted by the power management module through the vertical interface, a safety control command is generated. The safety control command intelligently selects a response strategy based on the severity of the system anomaly to ensure equipment safety. The safety control command is then sent to the conveyor equipment module through the vertical interface of the main control module to achieve safe shutdown or dynamic adjustment, preventing equipment damage and safety accidents.

[0029] The system records the correlation between material tracking data and optimized speed adjustment commands through the tracking database of the main control module. The tracking database uses a sorted list mechanism to manage data items, efficiently tracking the relationship between material flow and energy consumption. Based on this correlation, the system generates an energy consumption tracking report for each segment, providing a basis for energy consumption analysis and optimization.

[0030] In this embodiment of the invention, the detailed implementation steps of the method for calculating the load fluctuation coefficient and speed switching frequency include:

[0031] First, real-time load data and speed adjustment data of the conveyor line equipment module are acquired through the horizontal interface of the network communication module. The real-time load data comes from the current, voltage, and power monitoring values ​​of the motors in each section of the conveyor line equipment, while the speed adjustment data includes the target speed and actual speed information of each section of the conveyor belt. This data is transmitted in a standardized manner between conveyor line sections to ensure data consistency and integrity.

[0032] The real-time load data is segmented over time, and the load mean and standard deviation for each segment within each time window are obtained. The time segmentation employs a sliding window technique, with the window length dynamically adjusted based on the system's material transfer frequency, typically ranging from 3 to 30 seconds. For the load data within each window, its arithmetic mean is calculated as the load mean, and the sample standard deviation is calculated as the load standard deviation. These two indicators reflect the magnitude and degree of load fluctuation, respectively.

[0033] The ratio of the load standard deviation to the load mean is denoted as the load fluctuation coefficient. This coefficient is a dimensionless index, eliminating the influence of the absolute load magnitude and objectively reflecting the relative degree of load fluctuation. The smaller the load fluctuation coefficient, the more stable the equipment operation; the larger it is, the more severe the load fluctuation and the poorer the system stability. In the calculation of the load standard deviation, outliers in the real-time load data are filtered to avoid abnormal interference causing evaluation bias. The filtering threshold for outliers is dynamically adjusted based on the handover success rate of the material handover data collected by the network communication module through the horizontal interface. When the handover success rate is high, it indicates that the system is operating stably, and the filtering threshold can be appropriately increased; when the handover success rate is low, it indicates that there may be problems with the system, and the filtering threshold should be decreased to capture more potential anomalies.

[0034] The conveyor line equipment module is abstracted into section components through the component view of the main control module. The component view adopts a hierarchical structure, abstracting physical equipment into logical components such as Object (equipment block), Slave (slave station), Section (section), and Master (master station), facilitating unified system management. The number of speed adjustments for each section component within the time window is counted, and the number of speed adjustments per unit time is used as the speed switching frequency. The counting rule for the number of speed adjustments is: when a change in the target speed of a section is detected and the change exceeds a preset threshold, it is recorded as an adjustment event.

[0035] In the statistics of speed adjustment counts, only adjustment events with speed changes exceeding a preset speed change threshold are included, filtering out false counts caused by minor fluctuations. The preset speed change threshold is determined by the motor torque fluctuation period of the conveyor line equipment module. The motor torque fluctuation period reflects the mechanical characteristics of the system; the shorter the period, the more sensitive the system response, and the lower the corresponding speed change threshold should be set; the longer the period, the greater the system inertia, and the higher the speed change threshold should be.

[0036] The length of the time window is dynamically adjusted based on material handover data collected by the horizontal interface of the network communication module. This material handover data includes the time interval between the request to send (RTS) and request to receive (RTR) signals. When material handovers are frequent, the time window should be shortened to capture rapid changes; when material handovers are sparse, the time window can be appropriately extended to obtain more stable statistical results. This dynamically adjusted time window allows the system to adapt to different workloads and operating modes, providing more accurate assessments of load fluctuations and speed switching.

[0037] In this embodiment of the invention, the detailed implementation steps of the method for determining the oscillation risk value of multi-segment speed control include:

[0038] A oscillation risk assessment model is constructed, using the load fluctuation coefficient as the first input variable, the speed switching frequency as the second input variable, and the material transfer data between upstream and downstream sections collected by the network communication module through the horizontal interface as the third input variable. This model adopts a multi-input single-output structure, comprehensively considering data from three dimensions: load, speed, and material transfer, to fully assess the system's oscillation risk. Oscillation risk refers to the risk state in a multi-speed control system caused by factors such as control parameter mismatch, sudden load changes, or equipment response delays, resulting in speed fluctuations, increased energy consumption, and intensified equipment mechanical vibration.

[0039] The first, second, and third input variables are normalized to obtain the normalized load fluctuation coefficient, normalized speed switching frequency, and normalized material transfer fluctuation value. The normalization process uses the Min-Max method, mapping each variable to the [0,1] interval to eliminate dimensional differences and make different indicators comparable. The normalization benchmark for the normalized material transfer fluctuation value is determined by the ratio of the material transfer success rate to the conveyor belt tension fluctuation of the conveyor line equipment module. This ratio reflects the degree of matching between the system's mechanical characteristics and control effect during material transfer, serving as a dynamic reference benchmark for normalization and improving the adaptability of the normalization process.

[0040] The initial oscillation risk value is calculated based on the weighted sum of the normalized load fluctuation coefficient, the normalized speed switching frequency, and the normalized material transfer fluctuation value. The weighted sum calculation formula is as follows:

[0041] Initial oscillation risk value = w1 × normalized load volatility coefficient + w2 × normalized speed switching frequency + w3 × normalized material transfer volatility value;

[0042] Wherein, w1, w2, and w3 are weighting coefficients, satisfying w1 + w2 + w3 = 1. The weight of the normalized material transfer fluctuation value is determined by the variance of the transfer duration of the material transfer data. A large variance in the transfer duration indicates instability in the material transfer process, and its weight is increased accordingly; a small variance indicates stability in the transfer process, and its weight can be appropriately reduced.

[0043] The initial oscillation risk value is nonlinearly mapped to obtain the multi-segment speed control oscillation risk value. The nonlinear mapping uses a sigmoid function, such as the sigmoid function or the hyperbolic tangent function, to map the initial risk value to a range that better reflects the actual risk assessment requirements and enhances the ability to identify medium- to high-risk states. The mapping function is determined based on the mechanical inertia parameters of the conveyor equipment module and the power loss data transmitted from the power management module via the vertical interface. The mechanical inertia parameters reflect the system's mechanical characteristics and dynamic response capability, while the power loss data reflects the system's energy efficiency; both factors jointly affect the system's sensitivity to and resistance to oscillations.

[0044] The inflection point of the mapping function is dynamically adjusted based on the ratio of the peak motor torque of the conveyor equipment module to the peak supply voltage of the power management module. This ratio reflects the degree of matching between the motor load and the power supply capacity. A higher ratio indicates a larger motor load relative to the power supply capacity, resulting in a higher system sensitivity to oscillations. In this case, the inflection point of the mapping function should be lowered accordingly, allowing the system to issue a higher oscillation risk warning even with a lower initial risk value. Conversely, a lower ratio indicates sufficient system power margin and strong resistance to oscillations, allowing the inflection point of the mapping function to be appropriately increased. Through this dynamically adjusted nonlinear mapping, the system can provide a more accurate oscillation risk assessment for different equipment configurations and operating states.

[0045] In this embodiment of the invention, the detailed implementation steps of the method for generating the optimized speed adjustment command include:

[0046] The current and historical speed target values ​​for each segment component in the speed adjustment data are obtained through the component view of the main control module. The component view abstracts the physical conveyor line into logical segment components, each with independent speed control parameters. The current speed target value is the speed value planned and set by the system at the current moment, while the historical speed target value is a sequence of speed settings over a past period, typically retaining data from the most recent 5-10 cycles.

[0047] The difference between the current target speed value and the historical target speed value is calculated and denoted as the speed change. The speed change directly reflects the magnitude of speed control variation and is an important indicator for assessing the stability and potential oscillation risks of the control system. In calculating the speed change, the historical target speed value undergoes weighted smoothing to reduce the impact of single fluctuations and obtain a more representative historical benchmark value. The weight of the weighted smoothing is determined by the ratio of the mechanical vibration period of the conveyor equipment module to the time interval of the material transfer data collected by the network communication module through the horizontal interface. This ratio reflects the degree of matching between the mechanical system characteristics and the operating rhythm and is used to dynamically adjust the intensity of the smoothing.

[0048] A speed adjustment correction value is generated based on the speed change, the dynamic damping factor, and the feedforward compensation factor. The speed adjustment correction value is an optimized adjustment of the original speed change, aiming to reduce control oscillations and energy waste. The speed adjustment correction value is the sum of the negative of the product of the speed change and the dynamic damping factor, and the feedforward compensation factor. The calculation formula is:

[0049] Speed ​​adjustment correction value = -(speed change × dynamic damping factor) + feedforward compensation factor;

[0050] In this calculation logic, the dynamic damping factor acts on the velocity change and inverses it, thus suppressing abrupt changes. The feedforward compensation factor, based on predictions of future states, adjusts the control input in advance to reduce oscillations caused by system lag. When the velocity needs to increase, the dynamic damping factor slows down the acceleration process to prevent overshoot; when the velocity needs to decrease, the dynamic damping factor smooths the deceleration process to prevent sudden stops. The feedforward compensation factor predicts the required velocity change based on upstream and downstream material flow conditions and makes adjustments in advance.

[0051] The speed adjustment correction value is superimposed on the current target speed value to generate the optimized speed adjustment command. The final optimized speed command integrates the original control requirements and optimized adjustments, satisfying both operational efficiency requirements and ensuring system stability. The optimized speed adjustment command is sent to the conveyor equipment module through the vertical interface of the main control module, and the command execution status is recorded on the auxiliary interface of the network communication module. The command execution status includes the execution delay of the optimized speed adjustment command and the response deviation of the conveyor equipment module. The response deviation is determined by the difference between the actual speed of the conveyor equipment module and the target speed of the optimized speed adjustment command. The execution status data is used for subsequent control optimization and system diagnosis, forming a closed-loop feedback mechanism to continuously improve the control effect.

[0052] In this embodiment of the invention, the detailed implementation steps of the method for generating the energy consumption optimization allocation scheme include:

[0053] Based on the optimized speed adjustment command, the power demand of each section component in the next time window is predicted. The power demand prediction is based on three key factors: speed, load, and running time. An empirical model or machine learning algorithm is used to establish a speed-power mapping relationship to calculate future power demand. In the power demand prediction, the transfer frequency of material transfer data collected by the network communication module through the horizontal interface is introduced as a correction factor to reflect the impact of material flow on energy consumption. The correction factor is determined by the square root of the ratio of the transfer frequency to the conveyor belt tension fluctuation of the conveyor line equipment module. This correction mechanism considers the additional energy consumption during the material transfer process, improving the accuracy of the prediction.

[0054] The power management module's real-time power status data, including current power supply, voltage stability, and power reserve, is obtained through its vertical interface. The power management module not only provides electrical energy but also monitors the power status in real time, reporting key power parameters to the main control module via the vertical interface (Control / Report interface). Current power supply reflects the system's current energy consumption level, voltage stability reflects power quality, and power reserve reflects the system's ability to handle additional loads.

[0055] Based on the power demand and real-time power supply status data, an energy consumption optimization model is constructed. This model aims to minimize total energy consumption and maximize power supply stability, with the power supply margin and the environmental adaptability parameters of the power management module as constraints. The model employs a multi-objective optimization framework to seek the optimal balance between energy consumption and stability while meeting the normal operation requirements of the equipment. The environmental adaptability parameters are determined by the difference between the temperature range corresponding to the protection level of the power management module and the operating ambient temperature of the conveyor equipment module. This parameter reflects the potential impact of current environmental conditions on the performance of the power module and serves as a crucial constraint in the optimization process.

[0056] The energy consumption optimization model is solved iteratively to obtain the energy consumption allocation ratio for each segment component. The solution process employs optimization methods such as gradient descent, simulated annealing, or genetic algorithms to iteratively search for the optimal solution. The energy consumption allocation ratio represents the proportion of each segment in the total energy consumption, directly affecting energy utilization efficiency and system stability. Based on the energy consumption allocation ratio and the real-time power supply status data, the energy consumption optimization allocation scheme is generated. This scheme includes power limits, operating modes, and dynamic adjustment strategies for each segment, forming a complete energy consumption management solution.

[0057] The calculation method for power supply voltage stability includes: obtaining the time-series fluctuation value of the output voltage of the power management module through the vertical interface of the power management module. The time-series fluctuation value reflects the voltage change over time and is an important indicator for evaluating power quality. Based on the time-series fluctuation value and the transfer frequency of material transfer data collected by the network communication module through the horizontal interface, a voltage stability index is calculated. The voltage stability index comprehensively considers the relationship between voltage fluctuation and system load changes, reflecting the power supply's adaptability to load changes. In the calculation of the voltage stability index, the period of the motor torque fluctuation of the conveyor equipment module is introduced as a weighting factor. The motor torque fluctuation period reflects the temporal characteristics of load changes, and its correlation analysis with voltage fluctuation can more accurately assess the impact of power supply stability on system operation.

[0058] In this embodiment of the invention, the detailed implementation steps of the method for generating security control instructions include:

[0059] The operational status data of the conveyor line equipment module, including motor speed, conveyor belt tension, and temperature data, is acquired through the horizontal interface of the network communication module. This operational status data comprehensively reflects the working condition of the equipment and serves as the foundation for safety monitoring. Motor speed data comes from feedback from the frequency converter or encoder, conveyor belt tension data comes from tension sensors, and temperature data comes from temperature probes distributed in key areas. This data is transmitted and shared within the system via the standardized FIF protocol.

[0060] Based on the operational status data, an operational anomaly index is calculated for each section component. The operational anomaly index is a comprehensive indicator quantifying abnormal equipment conditions and is used to assess equipment operational risk. The operational anomaly index is determined by a weighted sum of the deviation value of the motor speed, the fluctuation value of the conveyor belt tension, and the number of times the temperature data exceeds a threshold. The calculation formula is:

[0061] Operational anomaly index = a1 × motor speed deviation value + a2 × conveyor belt tension fluctuation value + a3 × number of times temperature exceeds threshold, where a1, a2, and a3 are the corresponding weighting parameters;

[0062] Among them, the motor speed deviation value is the absolute value of the difference between the actual speed and the target speed, reflecting the speed control accuracy; the conveyor belt tension fluctuation value is the ratio of the standard deviation of the tension to the mean, reflecting the mechanical operation stability; the number of times the temperature exceeds the threshold is the cumulative number of times the temperature at the monitoring point exceeds the safety threshold, reflecting abnormal thermal conditions.

[0063] The weight (a1) of the motor speed deviation is dynamically adjusted by the ratio of the mechanical vibration cycle of the conveyor equipment module to the time interval of the material transfer data collected by the network communication module through the horizontal interface. This ratio reflects the degree of matching between mechanical characteristics and operating rhythm, and is used to determine the importance of the speed deviation. The weight of the number of times the temperature data exceeds the threshold is determined by the difference between the wide temperature range parameter of the power management module and the operating environment temperature of the conveyor equipment module. This difference reflects the relationship between the current temperature environment and the equipment's design temperature range, and is used to adjust the importance weight of temperature anomalies.

[0064] When the operational anomaly index exceeds a preset anomaly threshold or the power management module sends the emergency stop signal via the vertical interface, the safety control command is generated. The safety control command is the system's response strategy to abnormal conditions, designed to protect equipment safety and maintain system stability. The safety control command includes a shutdown command, a speed reduction command, or an alarm command, selecting the appropriate response measure based on the severity and type of the anomaly. The preset anomaly threshold is dynamically adjusted by the ratio of the peak motor torque of the conveyor equipment module to the peak supply voltage of the power management module. This ratio reflects the degree of matching between the motor load and the power supply capacity, used to determine the system's tolerance to anomalies.

[0065] The safety control commands are sent to the conveyor equipment module via the vertical interface of the main control module, and a command execution log is recorded on the auxiliary interface of the network communication module. The command execution log includes the execution timestamp of the safety control command, the response delay of the conveyor equipment module, and the anomaly recovery time of the operating status data. The anomaly recovery time is determined by the time required for the operating anomaly index to decrease below a preset anomaly threshold. The command execution log is used for post-event analysis and system optimization, forming a closed-loop process for safety management.

[0066] In this embodiment of the invention, the method for generating the dynamic damping factor and the feedforward compensation factor further includes:

[0067] The mechanical vibration data and power loss data of the conveyor equipment module are acquired through the horizontal interface of the network communication module. The mechanical vibration data comes from acceleration sensors or vibration sensors installed on key parts of the equipment, reflecting the mechanical dynamic characteristics of the equipment; the power loss data comes from a power monitoring device, reflecting the energy efficiency of the system. These two types of data are transmitted through a standardized interface to ensure the timeliness and completeness of the data.

[0068] Based on the spectral characteristics of the mechanical vibration data, the dominant vibration frequency component is extracted. The dominant vibration frequency component is the inherent vibration frequency of the mechanical system, reflecting the system's dynamic characteristics. The extraction process employs signal processing methods such as Fast Fourier Transform (FFT) or wavelet analysis to identify the main frequency components in the vibration signal. In the extraction of the dominant vibration frequency component, the transfer frequency of the material transfer data collected by the network communication module through the horizontal interface is introduced as a filtering factor to reduce interference caused by material transfer. The filtering factor is determined by the square of the ratio of the transfer frequency to the tension fluctuation of the conveyor belt in the conveyor line equipment module. This design considers the impact of the material transfer process on the vibration signal, improving the accuracy of the dominant frequency extraction.

[0069] Based on the time-series characteristics of the power loss data, peak loss components are extracted. These peak loss components are high-frequency components of energy consumption fluctuations, typically associated with control oscillations and mechanical instability. The extraction process employs peak detection and time-series analysis methods to identify abrupt changes and periodic fluctuations in the energy consumption data. In the extraction of the peak loss components, the period of the power supply voltage fluctuations from the power management module via the vertical interface is introduced as a smoothing factor to reduce interference from power supply fluctuations. This smoothing factor is determined by the ratio of the power supply voltage fluctuation period to the motor torque fluctuation period of the conveyor equipment module. This design distinguishes between fluctuations caused by the power supply and fluctuations caused by the load, improving the targeting of loss feature extraction.

[0070] The ratio of the dominant vibration frequency component to the peak loss component is denoted as the damping adjustment factor. The damping adjustment factor reflects the relationship between mechanical vibration and energy loss and is a key indicator for determining the optimal damping parameters. The predicted damping demand is dynamically corrected based on the damping adjustment factor, generating a corrected predicted damping demand value. This predicted damping demand value is a theoretical damping demand estimated based on historical system data and the current state. The correction process considers the impact of real-time operating conditions, improving the accuracy of the prediction. Based on the corrected predicted damping demand value and the preset damping decay curve, the dynamic damping factor is generated. The damping decay curve describes the change of the damping effect over time, typically employing an exponential or logarithmic decay model. The dynamic damping factor is dynamically adjusted based on this curve according to real-time demand, achieving precise oscillation suppression.

[0071] Based on the material handover success rate of the upstream and downstream sections collected by the network communication module through the horizontal interface, the gain coefficient of the feedforward compensation factor is corrected a second time. Feedforward compensation is a proactive control adjustment based on predictions of the system's future state, and the gain coefficient determines the strength of the feedforward compensation. The correction coefficient for the second correction is determined by the logarithm of the ratio of the handover success rate to the belt tension fluctuation of the conveyor line equipment module. This design considers the relationship between material handover effectiveness and mechanical condition, appropriately reducing feedforward compensation when the handover condition is good to avoid over-control; and enhancing feedforward compensation when the handover condition is poor to proactively address potential problems.

[0072] In this embodiment of the invention, the detailed implementation steps of the recording method of the tracking database of the main control module include:

[0073] The real-time load data, speed adjustment data, optimized speed adjustment commands, safety control commands, and material tracking data are bound to corresponding segment identifiers and timestamps to generate tracking data items. Tracking data items are the basic record units of the database, containing complete status and control information, enabling associative data storage. The material tracking data includes a material identification code (PIC) and virtual position data of the material on the conveyor line equipment module. The virtual position data is determined by material handover data collected through the horizontal interface of the network communication module and the conveyor belt speed data of the conveyor line equipment module. The virtual position data represents the logical position of the material in the system, calculated through material handover signals and speed integration, achieving precise material positioning and tracking.

[0074] Based on the segment identifier and the timestamp, the tracking data items are sorted to generate a sorted list. The sorted list is an efficient data organization method that supports rapid retrieval and updates, suitable for the dynamic data management needs of logistics systems. The sorting rules of the sorted list are determined by the priority of the material identification code and the displacement direction of the virtual location data. The material priority reflects the importance and urgency of the material, while the displacement direction reflects the material flow trend; both together determine the position of the data item in the list. The priority is dynamically adjusted by the ratio of the material handover time interval to the conveyor belt tension fluctuation of the conveyor line equipment module. This mechanism enables the system to dynamically adjust the processing priority based on the material handover status and equipment status, improving system adaptability.

[0075] The sorted list records the displacement information and state update count of the tracking data items. Displacement information describes the material's movement trajectory within the system, while the state update count reflects the intervention frequency of the control system; both are used together to analyze the relationship between material flow and energy consumption. The displacement information is determined by integrating the material position data collected through the horizontal interface of the network communication module with the conveyor belt speed data from the conveyor line equipment module. This calculation method combines direct position measurement and speed integral estimation, improving the accuracy and continuity of the position data. The state update count is determined by the difference between the number of command executions collected through the vertical interface of the main control module and the number of command responses collected through the auxiliary interface of the network communication module. This indicator reflects the command execution efficiency of the control system and is used to evaluate the performance and reliability of the control system.

[0076] The energy consumption tracking report is generated based on the correlation between the displacement information of the tracking data items and the optimized speed adjustment command. The energy consumption tracking report is the core output of the system's energy efficiency analysis, providing a view of the relationship between material flow and energy consumption, and supporting energy efficiency optimization decisions. The energy consumption tracking report includes the energy consumption distribution curve and material tracking efficiency for each segment component. The material tracking efficiency is determined by the ratio of the tracking continuity of the material identification code to the displacement error of the virtual location data. Tracking continuity reflects the completeness of the system's tracking function, while displacement error reflects the accuracy of tracking. The ratio of the two comprehensively evaluates the overall efficiency of the tracking system, providing data support for energy consumption optimization.

[0077] In this embodiment of the invention, the system further includes a web-based parameter configuration module for configuring the system's operating parameters; the detailed implementation steps of the parameter configuration method include:

[0078] First, the initial parameter set input by the user is obtained through the web interface, including the weight of the load fluctuation coefficient, the threshold of the speed switching frequency, the attenuation curve parameters of the dynamic damping factor, and the gain coefficient of the feedforward compensation factor. The web interface adopts a responsive design, supports access from both PC and mobile devices, and provides intuitive parameter configuration and visualization functions. Users can adjust various parameters through the graphical interface, and the system displays a preview of the parameter effects in real time to assist users in making reasonable configurations.

[0079] The initial parameter set is validated for validity. Validity validation is a crucial step in ensuring parameter rationality and system security, preventing system anomalies caused by misconfiguration. The validity validation includes checking whether the weight of the load fluctuation coefficient is within a preset weight range, whether the threshold of the speed switching frequency matches the operating cycle of the conveyor equipment module, whether the attenuation curve parameter of the dynamic damping factor matches the mechanical vibration cycle of the conveyor equipment module, and whether the gain coefficient of the feedforward compensation factor matches the handover success rate of the material handover data collected by the network communication module through the horizontal interface.

[0080] The operating cycle is determined by the ratio of the material transfer time interval collected by the horizontal interface of the network communication module to the conveyor belt speed data of the conveyor line equipment module. This indicator reflects the basic operating rhythm of the system and is an important reference for judging the rationality of the speed switching frequency threshold. If the speed switching frequency threshold is set too low, it may cause the system to adjust frequently, increasing energy consumption and wear; if it is set too high, it may miss necessary speed adjustments, affecting system efficiency and safety. The system evaluates the rationality of the configuration by comparing the relationship between the threshold and the operating cycle.

[0081] The initial parameter set, having passed validity verification, is sent to the main control module, which then updates the operating parameters of the conveyor equipment module via its vertical interface. Parameter updates employ a transaction mechanism to ensure parameter consistency and atomicity, preventing system inconsistencies caused by partial parameter updates. During the parameter update process, a configuration log is recorded in the parameter configuration module. This log includes the modification timestamp of the initial parameter set and the parameter response delay of the conveyor equipment module. The parameter response delay is determined by the difference between the parameter update time collected by the main control module's vertical interface and the parameter effective time collected by the network communication module's auxiliary interface. The configuration log is used for parameter adjustment effect analysis and configuration history tracking, supporting continuous system optimization and problem diagnosis.

[0082] In this embodiment of the invention, the detailed implementation steps of the method for collecting real-time power supply status data of the power management module include:

[0083] First, the power management module's output voltage, output current, and ambient temperature data are collected using its built-in sensors. The power management module integrates a high-precision sensor system to monitor the power supply's operating status and environmental conditions in real time. High-speed ADCs (analog-to-digital converters) are used for output voltage and current sampling to ensure measurement accuracy and response speed; a digital temperature sensor is used for ambient temperature monitoring to provide accurate temperature data.

[0084] The real-time power supply of the power management module is calculated based on the output voltage and output current. Real-time power supply is a fundamental indicator for evaluating system energy consumption and power load, and its calculation formula is: Real-time power supply = Output voltage × Output current. The power supply margin of the power management module is calculated based on the real-time power supply and the ambient temperature data. The power supply margin reflects the remaining power supply capacity of the power module under current operating conditions and is an important reference for ensuring stable system operation. In the calculation of the power supply margin, the transfer frequency of material transfer data collected by the network communication module through the horizontal interface is introduced as a correction factor to consider the impact of material flow on power demand. The correction factor is determined by the square root of the ratio of the transfer frequency to the conveyor belt tension fluctuation of the conveyor line equipment module. This design makes the power supply margin calculation more accurate and can reflect the changes in power demand from the actual workload.

[0085] The power supply voltage stability of the power management module is calculated based on the time-series fluctuation value of the output voltage and the peak change rate of the output current. Power supply voltage stability is a crucial indicator for evaluating power quality, directly impacting system reliability and control accuracy. In the calculation of power supply voltage stability, the period of the motor torque fluctuation of the conveyor equipment module is introduced as a weighting factor to reflect the influence of load characteristics on power supply stability requirements. This weighting factor is determined by the ratio of the motor torque fluctuation period to the handover time interval of the material handover data collected by the network communication module through the horizontal interface. This weighting mechanism considers the relationship between load dynamics and operating rhythm, making the voltage stability assessment more aligned with actual system requirements.

[0086] During the acquisition of ambient temperature data, the data is normalized using the wide-temperature range parameter of the power management module to facilitate data comparison and analysis under different environmental conditions. The benchmark for normalization is determined by the difference between the upper limit of the wide-temperature range parameter and the operating ambient temperature of the conveyor line equipment module. This design considers the degree of matching between the equipment's environmental adaptability and the actual working environment, making the temperature data processing more reasonable and providing accurate environmental reference information for power management.

[0087] Through the above implementation steps, this invention achieves efficient energy management, stable equipment control, and comprehensive safety assurance, significantly improving the operational efficiency and reliability of the logistics transportation system. The system's modular design and standardized interfaces give it excellent scalability and compatibility, enabling it to adapt to logistics application scenarios of different scales and types.

[0088] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0089] It should be noted that all formulas in this manual are calculated by removing dimensions and taking their numerical values. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0090] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A logistics conveyor line energy consumption management and safety control system, characterized in that, The system comprises a power management module, a network communication module, a conveying line equipment module and a master control module, and realizes energy consumption management and safety control through the following methods: Through the horizontal interface of the network communication module, real-time load data, speed adjustment data and material tracking data of each section of the conveying line equipment module during operation are obtained; According to the real-time load data and the speed adjustment data, the load fluctuation coefficient and the speed switching frequency of each section are calculated; based on the load fluctuation coefficient and the speed switching frequency, the multi-speed control oscillation risk value of each section is determined; By superimposing a dynamic damping factor and a feedforward compensation factor in the speed adjustment data, an optimized speed adjustment instruction is generated to reduce the multi-speed control oscillation risk value; According to the optimized speed adjustment instruction, in combination with the real-time power supply state data of the power management module through the vertical interface, an energy consumption optimization allocation scheme for each section is generated; The energy consumption optimization allocation scheme is sent to the conveying line equipment module through the vertical interface of the master control module; According to the running state data of the conveying line equipment module collected by the network communication module through the horizontal interface and the emergency stop signal of the power management module through the vertical interface, a safety control instruction is generated; The safety control instruction is sent to the conveying line equipment module through the vertical interface of the master control module to realize safety shutdown or dynamic adjustment; The association between the material tracking data and the optimized speed adjustment instruction is recorded through the tracking database of the master control module; based on the association, an energy consumption tracking report for each section is generated. The calculation method of the load fluctuation coefficient and the speed switching frequency comprises:

2. The energy consumption management and safety control system for a logistics conveyor line according to claim 1, characterized in that, Through the horizontal interface of the network communication module, real-time load data and speed adjustment data of the conveying line equipment module are obtained; the real-time load data is time-segmented to obtain the load mean and load standard deviation of each section in each time window; the ratio of the load standard deviation to the load mean is taken as the load fluctuation coefficient; in the calculation of the load standard deviation, the abnormal values of the real-time load data are filtered, and the filtering threshold of the abnormal values is dynamically adjusted through the handover success rate of the material handover data collected by the network communication module through the horizontal interface; Through the component view of the master control module, the conveying line equipment module is abstracted as a section component; the number of speed adjustments of each section component in the time window is counted to obtain the speed adjustment frequency per unit time as the speed switching frequency; in the counting of the number of speed adjustments, only the adjustment events with a speed change amplitude greater than a preset speed change threshold are counted, and the preset speed change threshold is determined by the motor torque fluctuation period of the conveying line equipment module; The material handover data includes the time interval of the request sending signal and the request receiving signal. The determination method of the multi-speed control oscillation risk value comprises:

3. The energy consumption management and safety control system for a logistics conveyor line according to claim 1, characterized in that, ​ constructing a shock risk assessment model, the shock risk assessment model taking the load fluctuation coefficient as a first input variable, taking the speed switching frequency as a second input variable, and taking material transfer data of upstream and downstream sections collected by the network communication module through the horizontal interface as a third input variable; normalizing the first input variable, the second input variable, and the third input variable respectively, and performing weighted sum to calculate an initial shock risk value; wherein the weight of the normalized material transfer fluctuation value is determined by the variance of the transfer duration of the material transfer data; performing nonlinear mapping on the initial shock risk value to obtain the multi-section speed control shock risk value; wherein the mapping function of the nonlinear mapping is determined based on the mechanical inertia parameter of the conveying line equipment module and the power loss data of the power management module uplink through the vertical interface; the inflection point of the mapping function is dynamically adjusted by the ratio of the motor torque peak value of the conveying line equipment module to the supply voltage peak value of the power management module.

4. The energy consumption management and safety control system for a logistics conveyor line according to claim 1, characterized in that, The generation method of the optimized speed adjustment instruction includes: obtaining the current speed target value and the historical speed target value of each section component in the speed adjustment data through the component view of the master control module; calculating the difference between the current speed target value and the historical speed target value, denoted as the speed change amount; in the calculation of the speed change amount, the historical speed target value is subjected to weighted smoothing processing; generating a speed adjustment correction value according to the speed change amount, the dynamic damping factor, and the feedforward compensation factor; superimposing the speed adjustment correction value on the current speed target value to generate the optimized speed adjustment instruction; wherein the optimized speed adjustment instruction is issued to the conveying line equipment module through the vertical interface of the master control module, and the instruction execution state is recorded at the auxiliary interface of the network communication module.

5. The energy consumption management and safety control system for a logistics conveyor line according to claim 1, characterized in that, The generation method of the energy consumption optimization allocation scheme includes: predicting the power demand of each section component in the next time window according to the optimized speed adjustment instruction; in the prediction of the power demand, the transfer frequency of the material transfer data collected by the network communication module through the horizontal interface is introduced as a correction factor; obtaining real-time power supply state data of the power management module through the vertical interface of the power management module, including current power supply power, power supply voltage stability, and power supply margin; constructing an energy consumption optimization model based on the power demand and the real-time power supply state data; the energy consumption optimization model takes minimizing total energy consumption and maximizing power supply stability as the optimization objective, and takes the power supply margin and the environmental adaptability parameter of the power management module as the constraint condition; obtaining the energy consumption allocation proportion of each section component by iteratively solving the energy consumption optimization model; generating the energy consumption optimization allocation scheme according to the energy consumption allocation proportion and the real-time power supply state data; The power supply voltage stability calculation method comprises: acquiring a time sequence fluctuation value of an output voltage of the power management module through a vertical interface of the power management module; and calculating a voltage stability index according to the time sequence fluctuation value and a transfer frequency of material transfer data collected by the network communication module through the horizontal interface. In the calculation of the voltage stability index, a period of motor torque fluctuation of the conveying line equipment module is introduced as a weighting factor.

6. The energy consumption management and safety control system for a logistics transport line according to claim 1, characterized in that, The safety control instruction generation method comprises: acquiring running state data of the conveying line equipment module through a horizontal interface of the network communication module, including motor speed, conveyor belt tension and temperature data; calculating a running abnormality index of each section component according to the running state data; the running abnormality index is determined by a weighted sum of a deviation value of the motor speed, a fluctuation value of the conveyor belt tension and a threshold exceeding frequency of the temperature data; generating the safety control instruction when the running abnormality index is greater than a preset abnormality threshold or the power management module sends the emergency stop signal through the vertical interface; the safety control instruction comprises a shutdown instruction, a speed reduction instruction or an alarm instruction; wherein the safety control instruction is sent to the conveying line equipment module through a vertical interface of the main control module, and an instruction execution log is recorded on an auxiliary interface of the network communication module; the instruction execution log comprises an execution timestamp of the safety control instruction, a response delay of the conveying line equipment module and an abnormality recovery time of the running state data, the abnormality recovery time is determined by a time required for the running abnormality index to drop below the preset abnormality threshold.

7. The energy consumption management and safety control system for a logistics conveyor line according to claim 1, characterized in that, The dynamic damping factor and the feedforward compensation factor generation method further comprises: acquiring mechanical vibration data and power loss data of the conveying line equipment module through a horizontal interface of the network communication module; extracting a vibration main frequency component according to a frequency spectrum feature of the mechanical vibration data; in the extraction of the vibration main frequency component, a transfer frequency of material transfer data collected by the network communication module through the horizontal interface is introduced as a filtering factor, the filtering factor is determined by squaring a ratio of the transfer frequency to a conveyor belt tension fluctuation of the conveying line equipment module; extracting a loss peak component according to a time sequence feature of the power loss data; in the extraction of the loss peak component, a period of power supply voltage fluctuation sent by the power management module through the vertical interface is introduced as a smoothing factor, the smoothing factor is determined by a ratio of the power supply voltage fluctuation period to a motor torque fluctuation period of the conveying line equipment module; a ratio of the vibration main frequency component to the loss peak component is denoted as a damping adjustment factor; a predicted value of the damping demand is dynamically corrected according to the damping adjustment factor to generate a corrected damping demand predicted value; the dynamic damping factor is generated based on the corrected damping demand predicted value and a preset damping decay curve; According to the handover success rate of the material handover data of the upstream and downstream sections collected by the network communication module through the horizontal interface, the gain coefficient of the feedforward compensation factor is secondarily modified; the modification coefficient of the secondary modification is determined by the logarithm of the ratio of the handover success rate to the conveyor belt tension fluctuation of the conveying line equipment module.

8. The energy consumption management and safety control system for a logistics conveyor line according to claim 1, characterized in that, The recording method of the tracking database of the master control module comprises: The real-time load data, the speed adjustment data, the optimized speed adjustment instruction, the safety control instruction and the material tracking data are bound to the corresponding section identifier and timestamp to generate a tracking data item; the material tracking data comprises a material identification code and virtual position data of the material on the conveying line equipment module; According to the section identifier and the timestamp, the tracking data item is sorted to generate a sorted list; the sorting rule of the sorted list is determined by the priority of the material identification code and the displacement direction of the virtual position data, and the priority is dynamically adjusted by the ratio of the handover time interval of the material handover data to the conveyor belt tension fluctuation of the conveying line equipment module; In the sorted list, the displacement information and state update times of the tracking data item are recorded; According to the association relationship between the displacement information of the tracking data item and the optimized speed adjustment instruction, the energy consumption tracking report is generated; the energy consumption tracking report comprises the energy consumption distribution curve of each section component and the material tracking efficiency.

9. The energy consumption management and safety control system for a logistics transport line according to claim 1, characterized in that, The system further comprises a parameter configuration module based on a Web interface, which is used to configure the running parameters of the system; the parameter configuration method comprises: Through the Web interface, an initial parameter set input by a user is obtained, including the weight of the load fluctuation coefficient, the threshold value of the speed switching frequency, the decay curve parameter of the dynamic damping factor and the gain coefficient of the feedforward compensation factor; The validity of the initial parameter set is verified; the validity verification comprises checking whether the weight of the load fluctuation coefficient is within a preset weight range, checking whether the threshold value of the speed switching frequency matches the running period of the conveying line equipment module, checking whether the decay curve parameter of the dynamic damping factor matches the mechanical vibration period of the conveying line equipment module, and checking whether the gain coefficient of the feedforward compensation factor matches the handover success rate of the material handover data collected by the network communication module through the horizontal interface; the running period is determined by the ratio of the handover time interval of the material handover data collected by the network communication module through the horizontal interface to the conveyor belt speed data of the conveying line equipment module. The initial parameter set passing through the validity verification is issued to the master module, and the operating parameters of the conveying line equipment module are updated through the vertical interface of the master module; during the updating process of the operating parameters, the configuration log of the parameter configuration module is recorded, the configuration log includes the modification time stamp of the initial parameter set and the parameter response delay of the conveying line equipment module, and the parameter response delay is determined by the difference between the parameter update time collected through the vertical interface of the master module and the parameter effective time collected through the auxiliary interface of the network communication module.

10. The energy consumption management and safety control system for a logistics transport line according to claim 1, characterized in that, The collection method of the real-time power supply state data of the power management module comprises: Through the built-in sensor of the power management module, the output voltage, output current and environmental temperature data of the power management module are collected; According to the output voltage and the output current, the real-time power supply power of the power management module is calculated; according to the real-time power supply power and the environmental temperature data, the power supply margin of the power management module is calculated; in the calculation of the power supply margin, the transfer frequency of the material transfer data collected by the network communication module through the horizontal interface is introduced as a correction factor, and the correction factor is determined by the square root of the ratio of the transfer frequency to the conveying belt tension fluctuation of the conveying line equipment module; According to the time sequence fluctuation value of the output voltage and the peak value change rate of the output current, the power supply voltage stability of the power management module is calculated; in the calculation of the power supply voltage stability, the period of the motor torque fluctuation of the conveying line equipment module is introduced as a weighting factor, and the weighting factor is determined by the ratio of the motor torque fluctuation period to the transfer time interval of the material transfer data collected by the network communication module through the horizontal interface.

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