Highly Reliable Rail-Mounted Smart Energy Acquisition and Control System

Abstract

This invention discloses a highly reliable rail-mounted intelligent energy acquisition and control system, belonging to the field of intelligent energy acquisition and control technology. The system employs standard rails for integrated deployment across the entire system, establishes multiple types of acquisition units and an adaptive acquisition mechanism to ensure data quality, and combines edge computing to achieve deep collaborative regulation of multiple energy sources and linkage with fault monitoring. It constructs a fault-based self-diagnosis and self-healing system and a rail status prediction mechanism. Stable operation is ensured through heterogeneous dual-redundant communication via Ethernet and LoRa, and dual-redundant power supply via external power grid and piezoelectric self-powered systems. A local and remote integrated operation and maintenance and closed-loop optimization iteration mechanism is established. The synergistic interaction of various technologies overcomes existing technological limitations, significantly improves system reliability and energy utilization, reduces operation and maintenance costs, and is suitable for complex scenarios such as integrated photovoltaic-energy storage-power distribution systems in industrial plants.

Description

Technical Field

[0001] This invention relates to the field of smart energy harvesting and control technology, specifically a highly reliable rail-mounted smart energy harvesting and control system. Background Technology

[0002] Smart energy harvesting and control technology is the core support for refined energy management in the industrial sector. With the expansion of industrial production scale and the widespread application of new energy sources such as photovoltaics and energy storage, higher requirements are placed on the reliability, coordination, and scenario adaptability of energy harvesting and control. Currently, rail-mounted energy-related equipment used in industrial scenarios generally suffers from decentralized deployment. Each functional module is installed independently, has poor compatibility, and lacks a unified integrated deployment solution, resulting in weak overall system coordination, cumbersome deployment, and high difficulty in subsequent maintenance.

[0003] Meanwhile, existing technologies mostly focus on single-function implementation, such as single energy acquisition, fixed parameter control, or simple fault alarms, without forming a complete process technology system: energy acquisition is mostly limited to single-type data, lacking accurate integration of multi-source data and adaptive acquisition mechanisms; control strategies are rigid and cannot be dynamically adjusted according to guide rail status, load changes, etc.; fault handling can only achieve basic alarms, without precise positioning and automatic self-healing capabilities, and key links such as communication and power supply lack redundancy protection, making it difficult to cope with complex environments such as electromagnetic interference and equipment vibration in industrial plants. Ultimately, this leads to insufficient system reliability, low energy utilization efficiency, and high operation and maintenance costs, failing to meet the high reliability operation requirements of complex industrial plant scenarios.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The purpose of this invention is to provide a highly reliable rail-mounted intelligent energy harvesting and control system to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, this invention provides a highly reliable rail-mounted intelligent energy acquisition and control system, comprising a rail body, a collaborative control module, a fault monitoring and self-healing module, an acquisition module, a control module, and a communication module. The collaborative control module and the fault monitoring and self-healing module communicate bidirectionally. The fault monitoring and self-healing module is integrated into the rail body and is used to collect real-time status data of the rail body and operational data of each module to complete fault diagnosis and self-healing. The collaborative control module receives data pushed by the fault monitoring and self-healing module and, combined with multi-source energy data collected by the acquisition module, achieves collaborative control of multi-source energy and adaptive adjustment of control parameters. The acquisition module… The control module and communication module are all adapted and installed with the guide rail body. The control module is electrically connected to the collaborative control module, fault monitoring and self-healing module, acquisition module and communication module respectively. The communication module is used to realize data interaction between the modules. The core architecture of the system is clearly defined, with the bidirectional collaboration of the collaborative control module and the fault monitoring and self-healing module as the core. It is different from the design of independent operation of each module in the prior art. It realizes the deep linkage between energy control and fault monitoring and self-healing, improves the overall reliability and energy utilization efficiency of the system, and ensures the convenience of system deployment and the stability and coordination of the overall system operation by adapting and installing each module with the guide rail body.

[0007] Furthermore, the collaborative control module includes an edge computing unit, a fusion analysis submodule, and an adaptive control submodule. The edge computing unit is electrically connected to both the fusion analysis submodule and the adaptive control submodule. The fusion analysis submodule receives multi-source energy data collected by the acquisition module and performs fusion analysis. The adaptive control submodule adjusts control parameters and energy allocation strategies based on the analysis results from the edge computing unit and the data pushed by the fault monitoring and self-healing module. This refined internal structure of the collaborative control module enables rapid data processing through the edge computing unit, deep fusion of multiple types of energy data through the fusion analysis submodule, and dynamic adjustment of control parameters through the adaptive control submodule, ensuring the accuracy and adaptability of energy control and further improving energy utilization efficiency.

[0008] Furthermore, the fusion analysis submodule is connected to a photovoltaic acquisition unit, an energy storage acquisition unit, and a power distribution acquisition unit. The photovoltaic acquisition unit is used to collect power generation data from photovoltaic modules, the energy storage acquisition unit is used to collect charging and discharging data from energy storage modules, and the power distribution acquisition unit is used to collect operating data from power distribution terminals. By clearly defining the specific acquisition units for multi-source energy data, comprehensive acquisition of multi-type energy data from photovoltaic, energy storage, and power distribution is achieved, providing sufficient data support for multi-source energy fusion analysis, ensuring the accuracy of the fusion analysis results, and providing a reliable basis for subsequent adaptive regulation.

[0009] Furthermore, the fault monitoring and self-healing module includes a guide rail monitoring unit, a fault diagnosis unit, and a self-healing unit, which are electrically connected in sequence. The guide rail monitoring unit is used to collect data on the wear state, contact state, and vibration state of the guide rail body. The fault diagnosis unit is used to identify and locate faults based on the guide rail status data and the operating data of each module. The self-healing unit is used to automatically repair minor faults. By refining the internal structure of the fault monitoring and self-healing module, comprehensive monitoring of the guide rail status and accurate identification and location of faults are achieved. The self-healing unit enables automatic repair of minor faults, reducing manual maintenance workload and maintenance costs, while preventing minor faults from escalating into major faults and ensuring stable system operation.

[0010] Furthermore, the guide rail monitoring unit includes a vibration sensor, a contact resistance sensor, and a wear sensor. These sensors are all installed on the guide rail body and are used to collect vibration data, contact resistance data, and wear data of the guide rail body, respectively. By clearly defining the specific sensor types and installation locations for guide rail condition monitoring, comprehensive and accurate monitoring of guide rail wear, contact status, and vibration status is achieved. This provides accurate status data for the fault diagnosis unit, ensuring the accuracy and timeliness of fault identification and guaranteeing reliable system operation.

[0011] Furthermore, the fault diagnosis unit has a built-in fault feature library for storing characteristic parameters of various faults. By matching the collected status data with the fault feature library, the fault type is identified and the fault point is located, and a fault report is generated. Through the built-in fault feature library, the fault can be quickly identified and accurately located. The generated fault report provides clear fault information for maintenance personnel, which facilitates the rapid handling of severe faults, further improves maintenance efficiency, and shortens the fault handling time.

[0012] Furthermore, the communication module includes a dual-redundant heterogeneous communication unit. The dual-redundant heterogeneous communication unit is configured with a primary communication link and a backup communication link. The primary communication link is used for regular data interaction, and the backup communication link is used for redundancy backup in case of primary communication link failure, realizing seamless data interaction. Through the dual-redundant heterogeneous communication link design, data interaction interruption caused by the failure of a single communication link is avoided, ensuring the continuity and reliability of data transmission between modules, further enhancing the high reliability of the system, and avoiding control failure or fault omission due to communication failure.

[0013] Furthermore, it also includes a power supply module, which comprises a dual-redundant power supply unit. The dual-redundant power supply unit adopts an external power supply and a piezoelectric self-powered structure. The piezoelectric self-powered structure is integrated into the guide rail body and is used to generate and store electrical energy using the vibration of the guide rail body as a backup power source. Through the dual-redundant power supply structure, the system is prevented from being completely paralyzed due to the interruption of external power supply. The piezoelectric self-powered structure is integrated with the guide rail body, eliminating the need for additional installation space. At the same time, it realizes energy recovery and utilization, reduces power supply costs, and ensures the continuous operation of the core functions of the system.

[0014] Furthermore, the control module includes an emergency control unit, which is used to activate an emergency control strategy when the fault monitoring and self-healing module detects a severe fault, to transfer the energy load of the faulty area to the normal area and cut off the energy supply to the faulty area; through the emergency control unit, rapid emergency response is achieved when a severe fault occurs, to prevent the fault from expanding, to ensure the normal operation of the non-faulty area, to reduce the impact of the fault on the overall system operation, and to further improve the reliability and fault resistance of the system.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention differs from existing technologies that suffer from decentralized deployment, single-function operation, and lack of collaborative linkage. It constructs a comprehensive technical system adapted to the complex scenarios of industrial plants. The entire system is deployed in an integrated manner using 35mm standard guide rails. Combined with multiple types of acquisition units and an adaptive acquisition mechanism, it ensures accurate and complete multi-source energy data. Edge computing enables deep collaborative energy regulation and control, and bidirectional linkage with the fault monitoring module breaks the limitations of independent module operation, significantly improving system synergy and energy utilization efficiency.

[0016] 2. Relying on the fault full-cycle self-diagnosis-self-healing system and guide rail status prediction mechanism, it realizes accurate fault location, mild automatic repair and severe emergency linkage. With the support of Ethernet and LoRa heterogeneous dual-redundant communication and external power grid and piezoelectric self-powered dual-redundant power supply design, it enhances system reliability from multiple dimensions such as communication, power supply and fault handling, effectively resists the effects of electromagnetic interference and equipment vibration in the industrial environment, and avoids system paralysis caused by a single link failure.

[0017] 3. Through integrated local and remote operation and maintenance and a closed-loop optimization and iteration mechanism, real-time system status monitoring, rapid fault handling, and dynamic parameter optimization are achieved, significantly reducing the workload and cost of manual operation and maintenance. The entire technical solution works in synergy, forming a complete closed loop from deployment, data collection, and control to operation and maintenance, solving the core pain point that existing technologies cannot adapt to complex industrial scenarios. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a highly reliable rail-mounted intelligent energy acquisition and control system. Detailed Implementation

[0019] 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.

[0020] Please see Figure 1 This invention provides a technical solution: a highly reliable rail-mounted intelligent energy acquisition and control system. This embodiment is based on an integrated photovoltaic-energy storage-power distribution scenario in an industrial plant. The scenario is a 5000-square-meter large-scale machine shop, with 35mm standard rails covering all production and auxiliary areas. The rails integrate photovoltaic modules (total installed capacity 500kW), energy storage modules (10kWh per module), power distribution terminals, and production loads such as precision machine tools and automated production lines. The scenario involves complex factors such as electromagnetic interference, vibration, and temperature and humidity fluctuations from industrial equipment, placing extremely high demands on the reliability, efficiency, and ease of maintenance of energy acquisition and control.

[0021] Step 1: Overall System Deployment: This step completes the overall system deployment, including the installation of 35mm standard DIN rails, the selection, installation, and snap-fit ​​fixing of each functional module, as well as the establishment of electrical connections between modules and dual redundant communication links. This enables the coordinated operation of all components, laying a stable hardware foundation for all subsequent steps and ensuring that the system structure is reasonable, deployment is convenient, and operation is reliable.

[0022] System deployment is a prerequisite for operation. Existing rail-mounted energy harvesting systems are mostly deployed in a decentralized manner, resulting in poor module compatibility, cumbersome deployment, weak coordination, and insufficient stability. They are unable to adapt to the complex scenarios of industrial plants. Therefore, a scientific overall deployment optimization of the layout and link design is needed to ensure the coordinated and stable operation of the system. Specific technical means are as follows: A 35mm standard DIN rail is selected as the main body and fixed to the factory wall and equipment bracket with expansion bolts to ensure a firm and level installation. The spacing is set at 1.2-1.5 meters to accommodate modular installation and future maintenance and expansion. Each functional module adopts a snap-on fixing structure that is compatible with the rail, enabling quick assembly and disassembly. All selected modules meet the requirements of industrial-grade electromagnetic interference resistance, wide temperature range (-20℃ to 60℃), high stability, and low power consumption.

[0023] Precise planning of module installation locations: The collaborative control module is installed on the guide rail of the energy control center as the core dispatching unit; the fault monitoring and self-healing module is deployed in a distributed manner with one module every 50 meters of guide rail to achieve full monitoring coverage; the acquisition module is installed near photovoltaic, energy storage, power distribution and load; the control module and communication module are integrated in the control center; the power supply module is divided into main and backup dual units, with the main unit connected to the external 10kV power grid and the backup unit integrated at key nodes of the guide rail.

[0024] Each module uses shielded cables for wiring to reduce electromagnetic interference. The communication module establishes an Ethernet main link (1000Mbps) and a LoRa backup link (transmission distance 1000 meters) to achieve bidirectional data communication between modules and ensure continuous and reliable data transmission.

[0025] Example: This 5000-square-meter factory is divided into four production workshops. A 500kW distributed photovoltaic system is deployed on the factory roof and in unused areas. Each workshop corner has a 10kWh energy storage module, and power distribution terminals are evenly distributed. During deployment, 35mm standard DIN rails are fixed along the workshop walls and supports at 1.5-meter intervals. The collaborative control module is installed in the central energy control center, while fault monitoring and self-healing modules are deployed at 50-meter intervals. Acquisition modules are installed at photovoltaic junction boxes, energy storage interfaces, power distribution terminals, and near machine tools. Control and communication modules are integrated in the control center. The main power supply is connected to the 10kV power grid, and backup power is integrated into key nodes on the rails. Shielded cables are used for wiring, and dual-link communication is employed. After commissioning, all modules operate normally, and data transmission is smooth.

[0026] This step can refer to the rail mounting technology of the Sailin SL-80 series vibration feeder controller, but it is only used for single-module installation and does not include multi-module collaboration, dual-redundant links, or power supply deployment. The unique feature of this solution is the use of standardized rails to achieve integrated deployment of the entire system, optimized layout to ensure full monitoring coverage, and the establishment of dual-redundant communication and power supply modules, significantly improving deployment convenience and compatibility, reducing the failure rate, and laying a stable foundation for subsequent steps.

[0027] Step 2: Multi-source energy data acquisition: This step uses the acquisition module and its four sub-units to collect real-time multi-source energy data from photovoltaics, energy storage, power distribution, and loads. The data is filtered, standardized to remove interference, and formatted to ensure accuracy and completeness, providing reliable data support for coordinated control and fault diagnosis.

[0028] Data acquisition is the core foundation of smart energy systems. Existing technologies often focus on single-energy source acquisition, and suffer from problems such as low acquisition accuracy, high interference, and lack of a unified processing mechanism. These technologies are unsuitable for the multi-source data and highly interference-prone scenarios in industrial plants. Therefore, professional acquisition and processing methods are needed to achieve accurate and stable data collection. Specific technical methods are as follows: The data acquisition module comprises four units: photovoltaic, energy storage, power distribution, and load, all electrically connected to the coordinated control module. The photovoltaic acquisition unit uses a 0.5-level high-precision sensor to collect data such as power generation current, voltage, and output. The acquisition frequency is adaptively adjusted according to sunlight intensity (10 seconds / time when stable, 1 second / time when fluctuating). The energy storage acquisition unit is equipped with a charge / discharge monitoring chip to collect charge / discharge parameters, remaining capacity, and battery temperature. The power distribution acquisition unit collects input / output parameters of the power distribution terminal, power factor, and harmonic content. The load acquisition unit uses non-contact acquisition to obtain load consumption data.

[0029] Data collected by each unit is transmitted digitally, filtered using a second-order filtering algorithm to remove noise, and standardized using a standardized algorithm to ensure that the data can be recognized by the collaborative control module. The acquisition module and the fault monitoring and self-healing module communicate bidirectionally. Data from the acquisition unit is pushed to the fault monitoring module for diagnosis, and the guide rail and early warning data pushed by the fault monitoring module are used to adjust the acquisition frequency and accuracy to avoid data distortion.

[0030] Example: Using the factory scenario described earlier, during the stable sunlight hours of 8-11 AM, the photovoltaic (PV) data acquisition unit collects data every 10 seconds to accurately monitor PV output. During the fluctuating sunlight hours of 1-3 PM, the acquisition frequency increases to once per second to ensure real-time data reflection. The energy storage acquisition unit monitors charging progress when PV power is excessive and discharge efficiency during peak load periods. The power distribution acquisition unit monitors the power distribution terminals in each workshop in real time, immediately pushing abnormal data when the power factor falls below 0.9. The load acquisition unit collects energy consumption data from machine tools and production lines; when idle, power data triggers the collaborative control module to adjust energy allocation. All data is processed and transmitted in real-time to the collaborative control module, while the acquisition unit's operating data is simultaneously pushed to the fault monitoring module.

[0031] This step can refer to Acrel ADW600 multi-loop power metering solution and relevant documents on industrial green microgrid construction. The former only focuses on power acquisition and lacks adaptive frequency functionality, while the latter only proposes an acquisition approach without providing specific solutions. The unique feature of this solution is its construction of a multi-type acquisition unit system to achieve full-source acquisition, its layout designed to adapt to various scenarios, its use of adaptive frequency and bidirectional data linkage, and its filtering and standardization processing to ensure data quality, improve acquisition accuracy and completeness, reduce power consumption, and provide high-quality support for subsequent steps.

[0032] Step 3: Multi-source energy synergy and adaptive control: This step uses the synergy control module to receive multi-source data from the acquisition module and guide rail and fault data from the fault monitoring module. After fusion analysis by the edge computing unit, combined with preset strategies, synergistic control of photovoltaic, energy storage, and power distribution is achieved. Parameters are adaptively adjusted according to guide rail status, load changes, etc., to ensure maximum energy utilization efficiency and stable system operation.

[0033] The output of photovoltaic systems in industrial plants fluctuates greatly due to the influence of sunlight. Energy storage charging and discharging, and power distribution need to be matched with load demand. Existing technologies mostly use fixed parameter control, with each energy system operating independently, resulting in poor coordination and low utilization. Efficient coordination and improved stability need to be achieved through synergistic integration and adaptive control. Specific technical means are as follows: The collaborative control module comprises an edge computing unit, a fusion analysis submodule, and an adaptive control submodule, all connected via an internal bus. The fusion analysis submodule integrates multi-source data with rail and fault data, mines correlations, and determines the matching of energy supply and load, as well as the impact of various factors on control. The edge computing unit rapidly processes the data and generates precise control commands. The adaptive control submodule adjusts parameters and allocation strategies accordingly.

[0034] The control strategies include: prioritizing photovoltaic output to meet load demands, with excess power stored; adjusting charging and discharging thresholds and rates based on output, load, and remaining power to achieve peak shaving and valley filling; and allocating power distribution according to load and rail status to avoid overloading the rails. The adaptive control submodule dynamically adjusts load power and sampling frequency based on rail faults, electromagnetic interference, and sudden load changes to ensure effective control. Simultaneously, the collaborative control module pushes control parameters to the control module and fault monitoring module in real time for coordinated operation.

[0035] Example: From 9-11 AM, sunlight is stable, with photovoltaic output reaching 450kW and total load demand at 300kW. After the fusion analysis submodule confirms the guide rail is normal, the edge computing unit instructs the adaptive control submodule to adjust the energy storage charging threshold, storing 150kW of excess power and allocating it to idle loads. From 12-2 PM, the load drops to 150kW, but the photovoltaic output remains at 400kW, and the energy storage is nearing saturation. The control submodule then reduces the photovoltaic output to 200kW, stops energy storage charging, and supplies the excess power to backup loads. From 4-6 PM, the photovoltaic output drops to 100kW, but the load rises to 350kW. The control submodule then controls the energy storage to discharge 250kW to supplement the insufficient power, prioritizing power supply to precision machine tools. During this period, the contact resistance of the guide rail in Workshop 2 increases. The control submodule immediately reduces the load power in that area and increases the sampling frequency. After self-healing, the system returns to normal.

[0036] This step can refer to relevant documents on edge computing-enabled real-time energy management and industrial green microgrid construction. The former does not involve linkage with the fault monitoring module, and the latter only proposes a collaborative approach without providing specific mechanisms. The unique feature of this solution is that it integrates edge computing with multi-source data to build a deep collaborative control system, designs an adaptive mechanism that can dynamically adjust parameters, achieves bidirectional linkage with fault monitoring, improves energy utilization, avoids control failure, and expands the scope of scenario adaptability.

[0037] Step 4: Full-cycle self-diagnosis of faults - self-healing and guide rail status monitoring: This step uses the fault monitoring and self-healing module to collect real-time operating data of the guide rail and each module. The fault diagnosis unit identifies and locates the fault. Minor faults are automatically self-healed, while severe faults are accurately located and reports are pushed. At the same time, the wear trend of the guide rail and the aging trend of the modules are predicted, realizing full-cycle fault management, ensuring system stability and reducing manual maintenance.

[0038] Industrial plant guide rails are widely distributed and operate in complex environments, making them prone to guide rail failures and module malfunctions. Current technologies only provide simple alarms and cannot accurately locate or self-heal the problem. Manual maintenance is costly and slow to respond. Therefore, a comprehensive fault lifecycle management system is needed to achieve integrated prevention, diagnosis, and self-healing to improve reliability. Specific technical methods are as follows: The fault monitoring and self-healing module comprises three units: guide rail monitoring, fault diagnosis, and self-healing, which are electrically connected in sequence to form a closed loop. Vibration, contact resistance, and wear sensors in the guide rail monitoring unit are installed on the guide rail and collect relevant data in real time, pushing it to the fault diagnosis unit. The fault diagnosis unit has a built-in dynamically updated fault feature library, which identifies fault types and locates fault points (with centimeter-level accuracy) through data matching, generating detailed fault reports.

[0039] Minor faults (slight contact issues, data acquisition lag, etc.) are automatically repaired by the self-healing unit, such as adjusting the guide rail holder pressure or restarting the module. After repair, the results are pushed to the relevant modules for parameter adjustment. Severe faults (severe wear, module damage, etc.) cannot be self-healed. A report is pushed to the control module, collaborative control module, and remote operation and maintenance platform. The collaborative control module activates emergency strategies to prevent the fault from escalating, and operation and maintenance personnel perform on-site repairs. The fault diagnosis unit can also analyze historical data, predict wear and aging trends, and push maintenance suggestions to achieve early prevention.

[0040] Example: At 10:00 AM, the contact resistance of the guide rail in Workshop 3 increased to 10Ω (normal ≤5Ω), and the vibration amplitude exceeded the standard. The fault diagnosis unit identified it as a minor fault. The self-healing unit adjusted the card holder pressure to 70N and increased the sampling frequency to 2 seconds / time. After 30 seconds, it returned to normal, and the collaborative control module synchronously restored the load power. At 3:00 PM, the wear of the guide rail in Workshop 1 reached 0.8mm (normal ≤0.5mm), which was diagnosed as a severe fault. A report was pushed out, clearly indicating the fault location and type. The collaborative control module transferred the 60kW load in that area to an adjacent guide rail, cut off the power supply to the faulty area, and maintenance personnel replaced the guide rail section on-site. After the repair, the system returned to normal. At the same time, the fault diagnosis unit found that the guide rail in Workshop 4 was wearing out faster and pushed a maintenance suggestion for inspection 15 days later, preventing the fault in advance.

[0041] This step can refer to the online vibration monitoring solution for guide rail transportation and the complementary application research of power distribution network automation communication. The former can only monitor and warn without self-healing and collaborative functions, while the latter only addresses communication failures without self-healing capabilities. The unique feature of this solution is the construction of a full-cycle fault management system, which deeply integrates guide rail monitoring with fault self-diagnosis and self-healing. This enables precise fault location, mild self-healing, severe emergency response, and trend prediction, along with bidirectional linkage with collaborative control, improving fault handling efficiency and reducing operation and maintenance costs.

[0042] Step 5: Dual Redundancy Communication and Data Interaction Guarantee: This step uses dual redundant heterogeneous links of the communication modules to achieve real-time data interaction between modules, monitor the transmission process to ensure continuity, reliability and security, and seamlessly switch to the backup link in the event of a primary link failure, ensuring that each step can be carried out smoothly.

[0043] Data interaction is the core of module collaboration. Strong electromagnetic interference and equipment vibration in industrial plants can easily cause single-link interruptions and data loss. Existing technologies have weak anti-interference capabilities and insufficient reliability, necessitating the construction of dual-redundant heterogeneous communication links to ensure smooth data interaction. Specific technical measures are as follows: The communication module includes dual-redundant heterogeneous communication units. The primary link uses Ethernet (1000Mbps) for high-speed transmission of large amounts of data. The backup link uses LoRa wireless, which is highly resistant to interference and has a long transmission range, serving as a backup for the primary link. A built-in link monitoring unit monitors the primary link's transmission speed, connection status, and bit error rate (threshold 1%) in real time. In case of failure, it switches to the backup link within 50ms, with a seamless switching mechanism.

[0044] Data transmission employs AES-256 end-to-end encryption to prevent leakage and tampering. The communication module and fault monitoring module communicate bidirectionally, adjusting the communication frequency and encryption strength based on abnormal data pushed by the fault monitoring module, while simultaneously pushing link status data for fault diagnosis. The communication module categorizes and processes data, accurately pushing it to the relevant modules, and also pushes data to the remote operation and maintenance platform via 4G / 5G.

[0045] Example: During normal operation, the Ethernet main link transmits collected data and control commands with a latency of less than 10ms. At 2 PM, a large machine tool starts up, generating strong electromagnetic interference. The main link's bit error rate rises to 3%. The link monitoring unit immediately switches to the LoRa backup link, and data exchange resumes normally. After 30 minutes, the electromagnetic interference disappears, and the link automatically switches back to the main link. The fault monitoring module analyzes the abnormal link data and pushes maintenance suggestions for checking the wiring interfaces. Maintenance personnel promptly reinforce the links. During this process, data is transmitted encrypted to ensure security.

[0046] This step can refer to research on complementary applications of distribution network automation communication and related documents on dual-redundant communication in industrial equipment. The former is not suitable for multi-module collaborative data interaction, while the latter only deploys dual links without monitoring, encryption, or collaborative functions. The unique feature of this solution is the use of heterogeneous dual-redundant links of Ethernet and LoRa, a smart and seamless switching mechanism, a built-in link monitoring unit, and full-link encryption with bidirectional linkage to fault monitoring, thereby improving data transmission reliability, anti-interference capability, and ensuring data security.

[0047] Step Six: Dual Redundancy Power Supply Guarantee: This step utilizes a dual redundancy structure for the power supply module. The main power supply unit connects to the external power grid to provide conventional power, while the backup power supply unit adopts a piezoelectric self-powered structure, utilizing rail vibration to generate and store electricity. It automatically starts when the external power grid is interrupted, ensuring the continuous operation of core functions, while optimizing power supply control to reduce power consumption.

[0048] Stable power supply is fundamental to system operation. External power grids are prone to outages and voltage fluctuations. Existing technologies often rely on a single external power source, lacking backup options and exhibiting high power consumption, failing to meet the requirements for high-reliability operation. Therefore, a dual-redundant power supply structure needs to be constructed, combined with piezoelectric self-powering technology to ensure core functions and reduce power consumption. Specific technical methods are as follows: The power supply module includes dual redundant power supply units. The main unit is connected to the external 10kV power grid and outputs 24V DC voltage after step-down rectification to power all modules, with a power supply stability of over 99.9%. The backup unit adopts a piezoelectric self-powered structure and is integrated in the area of ​​frequent guide rail vibration. The piezoelectric ceramic material converts mechanical energy into electrical energy (conversion efficiency of over 80%), which is then rectified and stored in the lithium battery energy storage module. The capacity can guarantee the power supply of the core module for no less than 4 hours.

[0049] The built-in power supply monitoring unit monitors the main power supply status in real time (voltage fluctuation threshold ±5%). In the event of a power grid interruption or excessive fluctuations, it immediately switches to backup power to supply power to core modules while non-core modules enter a dormant state to reduce power consumption. A low-power control strategy is employed, adjusting the power supply according to the module's operating status. Excess piezoelectric self-powered energy is used for low-power operation of non-core modules, achieving energy recovery. The power supply module communicates bidirectionally with the control and fault monitoring modules, pushing power supply status data.

[0050] Example: During normal operation, the main power supply powers all modules, while the piezoelectric self-powered system generates electricity through equipment vibration, supplementing approximately 5 kWh of power daily and reducing grid pressure. If the external power grid fails at 10 PM, the power monitoring unit immediately switches to backup power to supply the core modules, while non-core modules enter sleep mode. The backup power supply power is controlled below 50W to ensure continuous power supply for at least 4 hours, preventing system failure. Two hours later, when the grid is restored, the system automatically switches back to the main power supply, with the piezoelectric self-powered system continuing to supplement the backup power. Excess energy powers idle data acquisition modules, saving approximately 3 kWh of grid energy daily and reducing power supply costs.

[0051] This step can refer to the relevant documents on the Sailin SL-80 series piezoelectric drive technology and dual-redundant power supply for industrial equipment. The former does not use piezoelectric technology for power supply redundancy, while the latter lacks piezoelectric self-powering and energy recovery functions. The unique feature of this solution is that it combines external power supply with piezoelectric self-powering from guide rail vibration to construct a dual-redundant structure. It designs intelligent switching and low-power control strategies to achieve energy recovery, improve power supply reliability, prevent system failure, and reduce costs and energy consumption.

[0052] Step 7: System Operation and Remote Management: This step utilizes the control module, communication module, and remote operation and maintenance platform to achieve daily system operation and maintenance and remote management, including status monitoring, fault handling, parameter adjustment, firmware upgrades, and preventive maintenance through fault trend prediction, reducing manual operation and maintenance, improving efficiency, and ensuring long-term stable operation of the system.

[0053] Industrial plant systems are widely distributed and numerous, resulting in a large workload and low efficiency for manual on-site maintenance. Existing remote management technologies are weak, failing to achieve remote control and preventative maintenance, leading to high maintenance costs. Therefore, an integrated maintenance system needs to be built to improve efficiency and reduce costs. Specific technical solutions are as follows: The control module receives real-time operating data from each module, comprehensively monitors the system status, issues alarms and pushes them to the remote operation and maintenance platform when anomalies occur, and supports local adjustment of control parameters and fault monitoring thresholds. The communication module enables data transmission between the system and the remote platform via 4G / 5G (latency ≤50ms), allowing operation and maintenance personnel to remotely view the operating status, handle faults, accurately locate severe faults, and notify on-site personnel for handling.

[0054] The remote platform supports remote batch firmware upgrades, during which core functions operate normally. It generates maintenance plans based on fault trend prediction data to achieve preventative maintenance. The control module records operation, fault, and parameter adjustment logs to form operation and maintenance files, which are pushed to the remote platform for easy traceability analysis, optimization of operation and maintenance strategies, and generation of operation and maintenance analysis reports to provide a basis for system optimization.

[0055] Example: Maintenance personnel monitor the system status in real time through a remote platform. At 9:00 AM, they receive a warning about the wear trend of the guide rails in Workshop 4, generating a maintenance plan for lubrication and repair at 2:00 PM to prevent failures in advance. At 12:00 PM, they receive an alarm about an abnormal main communication link in Workshop 2, remotely switching to the backup link and adjusting the communication frequency, resolving the problem in 1 minute. At 4:00 PM, they remotely adjust the collaborative control parameters to ensure maximum energy utilization efficiency. When the system firmware is upgraded, instructions are issued in batches remotely, ensuring that core functions operate normally without affecting production. The maintenance records are regularly analyzed and optimized, adjusting the guide rail maintenance cycle to 20 days.

[0056] This step can refer to relevant documents on edge computing-enabled real-time energy management and remote operation and maintenance of industrial equipment. The former does not cover guide rail maintenance, firmware upgrades, and preventative maintenance, while the latter lacks trend prediction and a complete operation and maintenance system. The unique feature of this solution is the construction of an integrated local and remote operation and maintenance system, enabling real-time monitoring, remote handling, parameter adjustment, and firmware upgrades. Combined with trend prediction, it achieves preventative maintenance, forming a complete operation and maintenance record and analysis system, improving operation and maintenance efficiency and reducing costs.

[0057] Step 8: System Optimization and Iteration: This step collects various types of data from the long-term operation of the system, integrates and analyzes them to identify operational deficiencies, optimizes algorithms, models and strategies, and combines them with changes in scenarios to expand modules and adjust strategies, completing system iteration and upgrades, continuously improving reliability, energy efficiency and ease of operation and maintenance, and adapting to changes in scenario requirements.

[0058] Industrial plant loads, environmental and energy demands fluctuate with production plans, and module performance degrades after long-term system operation. Existing technologies lack a robust optimization and iteration mechanism, leading to decreased system efficiency and increased failures. Therefore, an iterative mechanism is needed to achieve continuous system upgrades. Specific technical solutions are as follows: The control module collaborates with the coordinated regulation and fault monitoring modules to collect various types of data, including acquisition, regulation, fault, and operation and maintenance data, and stores them to form a complete operational dataset. The edge computing unit performs fusion analysis on the dataset, mines data correlations, and identifies shortcomings in coordinated regulation algorithms, fault diagnosis models, and low-power strategies.

[0059] Targeted optimization of algorithms, models, and strategies improves control accuracy, fault identification rate, and reduces power consumption. In response to changing scenarios (new loads, photovoltaic capacity expansion, etc.), modules are expanded, layouts are adjusted, and control strategies are modified to ensure adaptation to new requirements. After optimization, parameters and algorithms are synchronized to each module via the control module and remote platform, and iteration logs are recorded for easy traceability and further optimization.

[0060] Example: After six months of system operation, various operational data were collected to form a dataset. Edge computing unit analysis revealed slow response times during sudden changes in lighting, low accuracy in identifying minor wear on the guide rails, and inaccurate power consumption control at night. Based on this, the collaborative control algorithm was optimized to speed up response, the fault diagnosis model was updated to improve identification accuracy, and low-power strategies were optimized to enhance energy saving at night. Simultaneously, two new production lines were added to the factory, the acquisition module and load control unit were expanded, and the control strategy was adjusted to adapt to the increased load requirements. After the upgrade, system performance was significantly improved.

[0061] This step can refer to relevant documents on edge computing-enabled real-time energy management and industrial system optimization and iteration. However, the former does not comprehensively optimize based on guide rail status and fault data, while the latter lacks specific optimization directions and scenario adaptation mechanisms. The unique feature of this solution is that it integrates multiple types of operational data from the entire system, mines data correlations through edge computing, and optimizes algorithms, models, and strategies in a targeted manner. It also combines scenario changes to achieve module expansion and strategy adjustment, forming a closed-loop iterative mechanism to continuously improve system performance and ensure long-term adaptability to scenario requirements.

[0062] In summary, compared to existing technologies such as single-module rail installation, single energy source acquisition, and fixed parameter control, this solution uses 35mm standard rails to achieve integrated deployment of the entire system. It establishes multiple types of acquisition units and an adaptive acquisition mechanism to ensure data quality. It combines edge computing to achieve deep collaborative control of multiple energy sources and links with fault monitoring. It constructs a fault full-cycle self-diagnosis and self-healing system and a rail status prediction mechanism. It adopts heterogeneous dual-redundant communication of Ethernet and LoRa, and dual-redundant power supply of external power grid and piezoelectric self-powered power to ensure stability. It establishes a local and remote integrated operation and maintenance and closed-loop optimization and iteration mechanism. All steps work together to significantly improve system reliability and energy utilization, reduce operation and maintenance costs, and solve the core pain point that existing technologies cannot adapt to complex industrial scenarios.

Claims

1. A highly reliable rail-mounted intelligent energy acquisition and control system, characterized in that: It includes the guide rail body, collaborative control module, fault monitoring and self-healing module, acquisition module, control module and communication module; The collaborative control module and the fault monitoring and self-healing module communicate bidirectionally. The fault monitoring and self-healing module is integrated into the guide rail body and is used to collect the status data of the guide rail body and the operation data of each module in real time to complete fault diagnosis and self-healing. The collaborative control module receives data pushed by the fault monitoring and self-healing module, and combines it with the multi-source energy data collected by the acquisition module to realize multi-source energy collaborative control and adaptive adjustment of control parameters. The acquisition module, control module, and communication module are all adapted and installed to the guide rail body. The control module is electrically connected to the collaborative control module, fault monitoring and self-healing module, acquisition module, and communication module, respectively. The communication module is used to realize data interaction between the modules.

2. The highly reliable rail-mounted intelligent energy acquisition and control system as described in claim 1, characterized in that: The collaborative control module includes an edge computing unit, a fusion analysis submodule, and an adaptive control submodule. The edge computing unit is electrically connected to the fusion analysis submodule and the adaptive control submodule, respectively. The fusion analysis submodule is used to receive multi-source energy data collected by the acquisition module and perform fusion analysis. The adaptive control submodule is used to adjust the control parameters and energy allocation strategy based on the analysis results of the edge computing unit and the data pushed by the fault monitoring and self-healing module.

3. The highly reliable rail-mounted intelligent energy acquisition and control system as described in claim 2, characterized in that: The fusion analysis submodule is connected to a photovoltaic acquisition unit, an energy storage acquisition unit, and a power distribution acquisition unit. The photovoltaic acquisition unit is used to collect power generation data of photovoltaic modules, the energy storage acquisition unit is used to collect charging and discharging data of energy storage modules, and the power distribution acquisition unit is used to collect operating data of power distribution terminals.

4. The highly reliable rail-mounted intelligent energy acquisition and control system as described in claim 1, characterized in that: The fault monitoring and self-healing module includes a guide rail monitoring unit, a fault diagnosis unit, and a self-healing unit, which are electrically connected in sequence. The guide rail monitoring unit is used to collect data on the wear state, contact state, and vibration state of the guide rail body. The fault diagnosis unit is used to identify and locate faults based on the guide rail status data and the operating data of each module. The self-healing unit is used to automatically repair minor faults.

5. The highly reliable rail-mounted intelligent energy acquisition and control system as described in claim 4, characterized in that: The guide rail monitoring unit includes a vibration sensor, a contact resistance sensor, and a wear sensor. The vibration sensor, contact resistance sensor, and wear sensor are all installed on the guide rail body and are used to collect vibration data, contact resistance data, and wear data of the guide rail body, respectively.

6. The highly reliable rail-mounted intelligent energy acquisition and control system as described in claim 4, characterized in that: The fault diagnosis unit has a built-in fault feature library to store the feature parameters of various faults. By matching the collected status data with the fault feature library, the fault type is identified and the fault point is located, and a fault report is generated.

7. The highly reliable rail-mounted intelligent energy acquisition and control system as described in claim 1, characterized in that: The communication module includes a dual-redundant heterogeneous communication unit. The dual-redundant heterogeneous communication unit is configured with a primary communication link and a backup communication link. The primary communication link is used for regular data interaction, and the backup communication link is used for redundant backup in case of failure of the primary communication link, so as to achieve seamless connection of data interaction.

8. The highly reliable rail-mounted intelligent energy acquisition and control system as described in claim 1, characterized in that: It also includes a power supply module, which includes a dual redundant power supply unit. The dual redundant power supply unit adopts an external power supply and a piezoelectric self-powered structure. The piezoelectric self-powered structure is integrated into the guide rail body and is used to generate and store electrical energy using the vibration of the guide rail body as a backup power source.

9. The highly reliable rail-mounted intelligent energy acquisition and control system as described in claim 1, characterized in that: The control module includes an emergency control unit, which is used to activate an emergency control strategy when the fault monitoring and self-healing module detects a severe fault, to transfer the energy load of the fault area to the normal area and cut off the energy supply to the fault area.

Images