Remote control method and system for intelligent hydraulic valve

Abstract

The application relates to the technical field of hydraulic valves, and discloses a remote control system of an intelligent hydraulic valve, which comprises a monitoring and instruction preprocessing module, a double-chip cooperative control module, a communication and data security module, an execution and state sensing module and a safety fault-tolerant and maintenance module. Through real-time control and complex algorithm operation of the double-chip cooperation, combined with multi-dimensional state sensing and working condition self-adaptive adjustment logic, and matched with a fast-response driving component, the control precision of the hydraulic valve is significantly improved, the fine adjustment requirement in an industrial scene is met, the communication stability in a remote and complex scene is enhanced, and the control interruption risk is reduced, relying on dynamic switching and edge relay mechanism of multi-modal communication.

Description

Technical Field

[0001] This invention relates to the field of hydraulic valve technology, and more specifically discloses a remote control method and system for an intelligent hydraulic valve. Background Technology

[0002] Intelligent hydraulic valves are automated components that combine digital hydraulic technology with intelligent control systems. They precisely control the pressure, flow rate, and direction of the hydraulic system through electrical pulse signals. Remote control of these valves enables real-time monitoring and fault warning, improves efficiency, and adapts to complex working conditions.

[0003] The prior art patent document with authorization announcement number CN118407960B discloses "a hydraulic valve intelligent control method and system with online performance monitoring". It collects parameter monitoring data in real time by setting up an information acquisition module; the data processing module compresses the real-time parameter monitoring data. In the process of compressing the transmitted data, several first and last image strings are extracted from the parameter monitoring data after binary conversion, and the compressed string of the data to be processed is selected from them. Then, according to several combinations corresponding to two, three, ..., P2-bit unsigned binary numbers, the string corresponding to the same combination is extracted from the data to be processed to determine the replacement string of the data to be processed. In this way, not only can the amount of data transmission in the hydraulic valve control system be effectively reduced, but the integrity and accuracy of the data can also be ensured.

[0004] The patent document with authorization announcement number CN114165501B discloses a "hydraulic valve control method, system and equipment", including: a controller loading a modular program; wherein, the modular program includes at least: at least one general function module, the calling order of each general function module, the input and output relationship between the general function modules, and the connection relationship between the interface of the hydraulic valve and the general function module; each general function module encapsulates multiple functions; each general function module provides at least one pin to the outside; the pin internally encapsulates the definition of multiple variables, as well as function operation operations on the variables.

[0005] While existing technologies can integrate general-purpose functional modules and pin connection logic through modular programming, encapsulate function and variable definitions, and simplify configuration processes by avoiding repetitive code writing, thereby achieving flexibility and speed in hydraulic valve control, or perform binary conversion on hydraulic system parameter monitoring data through data compression processing, extract the preceding and following strings, select the compressed string, and determine the replacement string by combining unsigned binary numbers, thus reducing data transmission volume while ensuring data integrity and accuracy and improving remote analysis speed, laying a certain foundation for the convenience of hydraulic valve control and data transmission efficiency, none of these technologies have designed a dual-core collaborative control architecture for remote control scenarios under complex working conditions (such as low temperature, high humidity, and strong electromagnetic interference). They cannot balance the resource conflicts between real-time command parsing and complex algorithm calculations, making it difficult to balance control real-time performance and adjustment accuracy. They also lack multimodal communication adaptive switching and edge relay mechanisms, making them prone to control interruptions due to weak signals in remote scenarios such as underground mines and water conservancy projects. Furthermore, they do not link fault self-diagnosis with oil self-maintenance, making it impossible to avoid secondary faults in a timely manner by locating the fault source. In addition, the emergency manual mechanism lacks position memory function, requiring recalibration after power failure. Summary of the Invention

[0006] The present invention mainly provides a remote control method and system for intelligent hydraulic valves, which can solve the problems mentioned in the background art.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution, more specifically, a remote control method for an intelligent hydraulic valve, comprising:

[0008] S1. Select the target hydraulic valve and input the control command. Verify the legality of the control command, read the locally cached historical sensor data, and if the data is abnormal, trigger a local alarm before issuing the command again. At the same time, record the command sending timestamp.

[0009] S2. After receiving the instruction, the processing task is split, the feedback pressure, temperature, displacement and vibration status data are read and fused through filtering, and the PID-fuzzy fusion algorithm is called. Combined with the oil viscosity parameters converted by temperature, the drive voltage control quantity is calculated and output and fed back.

[0010] S3. The valve core opening is adjusted according to the control quantity by the piezoelectric component, and the status data is collected and uploaded in real time. If the deviation between the actual value of the opening and the target value is detected, the deviation signal is fed back, and the control quantity is recalculated to achieve closed-loop regulation.

[0011] S4. Real-time monitoring of signal strength, packet loss rate and delay of each communication channel, switching and adapting to the appropriate channel according to parameters, receiving status data in the relay unit, caching data when the cloud connection is lost, uploading in batches after recovery, and attaching a unique device code and timestamp to all transmitted data, which is then encrypted and verified by CRC32.

[0012] S5. Real-time monitoring of hydraulic valve operation status, identification of faults and location of fault sources through vibration harmonic analysis, and periodic collection of oil parameters. If the parameters exceed the standard, filtration is activated. If the parameter changes exceed the range, the drive voltage is adjusted to achieve viscosity compensation.

[0013] Furthermore, in S1, the target hydraulic valve is selected through the WebGIS interface or a control command is issued through voice command. The legality verification of the control command includes whether the opening degree is within the range of 0-100%. The locally cached historical sensor data is the average pressure and temperature of the past 10 seconds. The data anomaly judgment standard is temperature >60℃.

[0014] Furthermore, in S2, the processing task is split into a logic chip for handling switching logic and power management, an algorithm chip for handling opening adjustment parameters, and then the state data is fused through Kalman filtering. The PID-fuzzy fusion algorithm is called to divide the working conditions based on the pressure change rate, temperature, and vibration frequency.

[0015] Furthermore, in S4, the communication channel is constructed based on 4G / 5G / NB-IoT / BLE, and the channel switching is based on the 5G signal packet loss rate >1% to switch to NB-IoT+BLE. The relay unit is a solar edge repeater, and the data encryption adopts the national cryptographic SM4 algorithm.

[0016] According to another aspect of the present invention, a remote control system for an intelligent hydraulic valve is provided. This system is based on the aforementioned remote control method for an intelligent hydraulic valve, specifically comprising: a monitoring and command preprocessing module that collects equipment operating status data and performs legality verification and noise filtering preprocessing on issued commands, establishing a foundation for real-time equipment status monitoring and accurate command issuance; a dual-chip collaborative control module employing a dual-chip architecture, with a logic processing chip responsible for real-time command parsing and equipment power management, and an algorithm processing chip running adaptive control algorithms and multi-source data fusion; and a communication and data security module that monitors the signal strength and packet loss of each communication link in real time. The system minimizes latency and employs national cryptographic algorithms to encrypt transmitted data, adding a unique device code and timestamp verification to each data frame. In the execution and status perception module, intelligent actuators drive the device to perform actions according to control commands, while a multi-dimensional sensor array collects real-time device operating status data, providing real-time data support for control decisions and fault diagnosis. In the safety fault tolerance and maintenance module, an intelligent redundancy fault-tolerant unit seamlessly switches to backup power in the event of a main power failure and is equipped with an emergency manual mechanism with position memory, eliminating the need for recalibration after power outage recovery. The device self-maintenance unit locates the fault source through a fault self-diagnosis algorithm and coordinates with maintenance components to restore device status.

[0017] Furthermore, the monitoring and command preprocessing module includes: a geographic information module, a local edge preprocessing module, and an interaction module;

[0018] Geographic Information Module: Based on the WebGIS geographic information system, it supports visual annotation of hydraulic valve locations;

[0019] Local edge preprocessing module: performs filtering and preprocessing on the vibration and noise data collected by the sensors;

[0020] Interactive module: includes touch screen, voice command recognition, and emergency physical buttons.

[0021] Furthermore, the dual-chip collaborative control module includes: a logic chip module, an algorithm chip module, and a synchronization interaction module;

[0022] Logic chip module: processes valve switching logic and real-time commands for device power management;

[0023] Algorithm chip module: Runs PID-fuzzy fusion algorithm and Kalman filter data fusion, supports parallel processing of sensor data;

[0024] Synchronous Interaction Module: Enables dual-chip data interaction through a custom CANopen sub-protocol.

[0025] Furthermore, the communication and data security module includes: a communication module, a solar edge relay module, a channel assessment and switching module, and an encryption and verification module;

[0026] Communication module: integrates 4G / 5G / NB-IoT / BLE communication units to cover signal requirements in different scenarios;

[0027] Solar edge repeater module: It adopts a solar edge repeater unit with an output power of 10W and a battery life of 72h, which receives hydraulic valve signals and forwards them to the cloud;

[0028] Channel evaluation and handover module: Real-time monitoring of signal strength, packet loss rate and delay of each channel, and outputting priority through fuzzy comprehensive evaluation model;

[0029] Encryption and verification module: Data transmission is encrypted using the national standard SM4 algorithm, and each data frame is appended with a unique device code, a 1ms precision timestamp, and a CRC32 checksum.

[0030] Furthermore, the execution and state perception module includes: a piezoelectric drive module and a sensor array module;

[0031] Piezoelectric drive module: It adopts a piezoelectric ceramic stacked drive unit with a response speed of ≤10µs and an opening control accuracy of 0.01mm, replacing the traditional electromagnetic coil drive;

[0032] Sensor array module: includes pressure sensor, temperature sensor, displacement sensor, and vibration sensor, which collects hydraulic valve operating status data in real time.

[0033] Furthermore, the safety fault tolerance and maintenance module includes: a power management module, a magnetic attraction and emergency manual module, a hydrophobic protection module, and an oil maintenance module;

[0034] Power management module: Includes an industrial-grade switching main power supply and a 10Ah lithium-sulfur backup battery. In the event of a main power failure, it seamlessly switches to the backup battery via a DC / DC converter and provides early warning of battery degradation.

[0035] Magnetic and Emergency Manual Module: It adopts a magnetic positioning component with a magnetic force of ≥50N + Hall position sensor. When the power is off, the valve core is manually adjusted and it automatically returns to the recorded position after the power is restored.

[0036] Hydrophobic protection module: It adopts a double-layer honeycomb staggered perforated plate, with a fluororubber baffle with Φ2mm pressure balance hole to prevent liquid intrusion and negative pressure moisture absorption, and assist in motor heat dissipation;

[0037] Oil maintenance module: Includes a miniature oil sensor and a 5µm precision ceramic filter valve. It automatically filters when contamination exceeds the standard and adjusts the drive voltage to compensate when viscosity changes.

[0038] The beneficial effects of the remote control method and system for intelligent hydraulic valves of this invention are as follows: By using dual chips to perform real-time control and complex algorithm calculations, combined with multi-dimensional state perception and adaptive adjustment logic, and equipped with fast-response drive components, the control accuracy of hydraulic valves is significantly improved, meeting the fine adjustment needs in industrial scenarios. Relying on the dynamic switching and edge relay mechanism of multi-modal communication, the communication stability in remote and complex scenarios is enhanced, reducing the risk of control interruption. At the same time, through multi-parameter correlation analysis, the fault source is accurately located, the fault handling speed is accelerated, and the system's operational reliability is improved. In addition, through the low-temperature adapted redundant power supply design and pressure-resistant and water-draining protection structure, the system's adaptability in harsh environments is enhanced, ensuring stable operation in extreme temperature and humidity scenarios. With the help of real-time oil status monitoring and automatic maintenance functions, the frequency of manual inspection is reduced. Combined with the position memory mechanism for emergency operation, the repeated calibration steps after power failure are eliminated, effectively reducing operation and maintenance costs and achieving high efficiency and economy in unmanned operation and maintenance of industrial hydraulic systems. Attached Figure Description

[0039] The present invention will now be described in further detail with reference to the accompanying drawings and specific implementation methods.

[0040] Figure 1 This is a schematic diagram of the system framework;

[0041] Figure 2 This is a flowchart illustrating the method. Detailed Implementation

[0042] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0043] According to one aspect of the invention, such as Figures 1-2 As shown, a remote control method and system for an intelligent hydraulic valve is provided, including:

[0044] Step 1: Instruction Generation and Preprocessing

[0045] Select the target hydraulic valve and input the control command. Verify the legality of the control command, read the locally cached historical sensor data, and if the data is abnormal, trigger a local alarm before issuing the command again. At the same time, record the command sending timestamp.

[0046] Specifically, the target hydraulic valve can be selected through the WebGIS interface or control commands can be issued via voice commands. The validity verification of the control commands includes whether the opening degree is within the range of 0-100%. The locally cached historical sensor data is the average pressure and temperature of the past 10 seconds. The data anomaly judgment standard is temperature >60℃.

[0047] The selection of target hydraulic valves is achieved through a geographic information module. This module uses a WebGIS geographic information system to visualize and annotate the locations of distributed hydraulic valves (e.g., map annotations of underground pipelines in mines and valve groups in water conservancy projects). Operators can intuitively view the real-time operating status indicators (e.g., normal, abnormal, offline) of each valve in the annotation interface, quickly locate the target valve to be adjusted, and avoid operational errors caused by ambiguous valve locations in distributed scenarios. Meanwhile, the input of control commands is achieved through an interactive module. This module integrates a touch screen, voice command recognition, and emergency physical buttons. It supports manual input of specific parameters such as opening value and pressure threshold through the touch screen, and can also issue control requirements through voice commands (e.g., "adjust the opening of hydraulic valve No. 3 to 50%). It is suitable for scenarios where the touch screen cannot be easily operated in harsh environments such as high humidity, dust, and low temperature. The emergency physical buttons can trigger basic commands such as emergency valve closure and pressure holding when the touch screen and voice functions fail.

[0048] The validity verification of control commands is completed collaboratively by the local edge preprocessing module. This module is equipped with an ARM Cortex-A72 chip and verifies the validity of the input control parameters: on the one hand, it verifies whether the opening parameter is within the mechanical stroke range of 0-100% and whether the pressure threshold does not exceed the rated working pressure of the hydraulic system (e.g., 31.5MPa, which is common in metallurgical hydraulic systems), to avoid valve core jamming or valve group seal damage due to parameter over-limit. On the other hand, it verifies the consistency of command logic, such as whether the command combination of "opening 100%" and "pressure threshold is lower than the current system pressure" is contradictory. If there is parameter over-limit or logical contradiction, a local audible and visual alarm is triggered (e.g., red warning light flashing + buzzer), and the alarm reason is displayed on the touch screen (e.g., "opening exceeds the effective range"), while blocking the issuance of commands, forming the first line of safety protection.

[0049] In addition, the local edge preprocessing module reads the historical sensor data cached within the last 10 seconds. This data comes from real-time data collected by the sensor array module (such as pressure and temperature sensors). The module performs vibration and noise filtering preprocessing on the raw data (filtering out interference data caused by valve core vibration and pipeline pulses through a sliding mean filtering algorithm), and finally outputs accurate pressure and temperature averages. When the average temperature is detected to be >60℃ (or the average pressure exceeds the normal operating range, for example, the normal operating pressure is 10-15MPa, and the current average is 20MPa), the command issuance process will be paused. In addition to triggering local alarms, the abnormal data will also be synchronized to the WebGIS interface, marking the location of the abnormal valve body and displaying the abnormal parameters. After the on-site maintenance personnel confirm the cause of the abnormality (such as oil circulation failure causing temperature rise) and eliminate the risk (such as starting the oil cooling system), the operator will confirm "allow issuance" through the interactive module. Only then will the module continue to issue control commands to the dual-chip collaborative control module to avoid secondary failures caused by performing adjustment actions in the abnormal state of the system.

[0050] Step 2: Control Algorithm Calculation and Data Fusion

[0051] After receiving the instruction, the processing task is split, the feedback pressure, temperature, displacement and vibration status data are read and fused through filtering, and the PID-fuzzy fusion algorithm is called. Combined with the oil viscosity parameters converted by temperature, the drive voltage control quantity is calculated and output and fed back.

[0052] Specifically, the processing task is split into a logic chip for handling switching logic and power management, an algorithm chip for handling opening adjustment parameters, and then the state data is fused through Kalman filtering. The PID-fuzzy fusion algorithm is called to classify the working conditions based on the pressure change rate, temperature, and vibration frequency.

[0053] The logic chip uses an ARM Cortex-M4 chip, which focuses on handling tasks with high real-time requirements: on the one hand, it parses the switching valve logic (such as the timing control of valve core opening / closing, and the sequence of multi-valve linkage) to ensure that the instruction response time is ≤1ms and avoids flow regulation lag caused by delay; on the other hand, it is responsible for power management related instructions (such as monitoring the main power supply voltage fluctuation and determining whether to trigger the backup power supply switch), and outputs the power status signal to the algorithm chip in real time to ensure the stability of the system power supply.

[0054] The algorithm chip uses an FPGAXC7K325T chip, which has parallel computing capabilities and can simultaneously read four-dimensional data from the sensor array module: pressure (e.g., ±0.1%FS), temperature (e.g., ±0.5℃), displacement (e.g., ±0.005mm), and vibration (e.g., ±0.1g). To eliminate single sensor errors and environmental interference (e.g., displacement data fluctuations caused by pipeline vibration), the algorithm chip calls the Kalman filter algorithm to fuse the data. By establishing state equations and observation equations, it filters out abnormal data such as vibration noise and electromagnetic interference, improving the accuracy of the fused state data and providing a precise data foundation for subsequent algorithm calculations. Data interaction between the two chips is achieved through a custom CANopen sub-protocol of the synchronous interaction module. Specifically, the logic chip and the algorithm chip communicate via synchronous interaction... The interaction module establishes a full-duplex communication link. The logic chip encapsulates the parsed switching logic commands (such as valve core action timing codes and power status flags) and real-time power parameters (such as main and backup power supply voltages and currents) in the "logic command frame" format of the custom CANopen sub-protocol. Each frame contains an 11-bit identifier (used to distinguish command types) and an 8-byte data segment (storing command parameters and check codes). The algorithm chip sends back the generated PID parameters, fuzzy control rule sets, and multi-sensor fusion results in the "algorithm parameter frame" format. The synchronization interaction module has a timestamp synchronization mechanism, with each frame of data having an appended timestamp (e.g., at the microsecond level). The receiving end dynamically adjusts the data transmission and reception timing by comparing the timestamp deviation to ensure that the synchronization error between the logic commands and algorithm parameters is ≤50µs, avoiding control deviations caused by data asynchrony.

[0055] Meanwhile, the PID-fuzzy fusion algorithm requires prior operating condition identification: the algorithm chip classifies operating conditions based on the fused pressure change rate, temperature, and vibration frequency—when the pressure change rate is ≤0.5MPa / s and the temperature is between 20-40℃, it is determined to be a normal operating condition, in which case PID control is used to ensure the stability of the opening adjustment; when the pressure change rate is >1MPa / s or the temperature is <0℃ / >60℃, it is determined to be a sudden operating condition (such as a sudden drop in temperature causing a sudden increase in oil viscosity, or pressure pulse impact), and fuzzy control is switched to quickly suppress the disturbances caused by parameter sudden changes. At the same time, the algorithm chip will combine the oil viscosity parameters transmitted by the oil maintenance module and convert them from temperature using the following formula:

[0056]

[0057] In the formula, This refers to the viscosity of the oil. As the reference viscosity, For real-time temperature, Using the reference temperature and k as the viscosity coefficient, PID parameters are self-tuned. For example, when the oil viscosity increases by 20% due to the decrease in temperature, the proportional coefficient is increased by 20% and the integral time is reduced by 15% to avoid opening control errors caused by viscosity changes.

[0058] Step 3: Execution and Closed-Loop Adjustment

[0059] The valve core opening is adjusted by the piezoelectric component according to the control quantity, and the status data is collected and uploaded in real time. If the deviation between the actual value of the opening and the target value is detected, the deviation signal is fed back, and the control quantity is recalculated to achieve closed-loop regulation.

[0060] Specifically, the logic chip transmits the verified drive voltage control quantity to the piezoelectric drive module of the execution and state perception module. The piezoelectric ceramic stacked assembly converts the electrical signal into mechanical displacement to drive the valve core. The sensor array synchronously collects pressure, temperature, displacement and vibration data, which are then fed back to the algorithm chip after being fused by Kalman filtering. When the displacement deviation exceeds the threshold, the algorithm chip recalculates the drive voltage to correct the deviation.

[0061] The real-time monitoring of the sensor array adopts the "high-frequency acquisition + noise filtering" mode. The sensor array acquires raw data every 10ms: the pressure sensor monitors the pressure difference change at the valve port, the temperature sensor records the real-time temperature of the oil, the displacement sensor directly feeds back the actual position of the valve core, and the vibration sensor captures the subtle vibration harmonics of the valve core movement. After these data are fused and processed by the Kalman filter algorithm, noise such as pipeline vibration and electromagnetic interference can be effectively filtered out, thereby improving the signal-to-noise ratio of the status data and providing an accurate basis for deviation judgment.

[0062] Furthermore, the core logic of closed-loop regulation lies in the dynamic parameter correction of the algorithm chip: for example, when the fusion data shows that the actual opening degree deviates from the target value by more than 5% (for example, the target is 50% and the actual value is 45%), the algorithm chip activates the deviation compensation mechanism of the PID-fuzzy fusion algorithm. If the deviation is caused by fluctuations in normal operating conditions, it is gradually corrected by fine-tuning the proportional coefficient. If the vibration sensor detects valve core jamming (abnormal vibration frequency), the drive voltage is temporarily increased (for example, by 10%-15%) to break through the jamming state. At the same time, combined with the viscosity parameters transmitted by the oil maintenance module (calculated from temperature), when the low temperature environment causes the oil viscosity to increase (for example, by 20%), the drive voltage is automatically increased (for example, by 10%) to compensate for the action lag caused by the increase in viscosity.

[0063] Finally, the corrected valve core position data is encrypted by the communication and data security module and then transmitted back to the monitoring and command preprocessing module. The WebGIS interface updates the opening status of the corresponding hydraulic valve in real time (e.g., green for normal, yellow for adjustment). At the same time, the local edge box caches the complete data chain of this adjustment (including drive voltage, valve core displacement, and deviation value), providing the original basis for subsequent fault tracing. The closed-loop link of "drive-sensing-correction" ensures the accuracy and traceability of each adjustment action.

[0064] Step 4: Communication Link Management and Secure Data Transmission

[0065] Real-time monitoring of signal strength, packet loss rate, and latency of each communication channel; switching and adapting to the appropriate channel based on parameters; receiving status data from the relay unit; caching data when the cloud connection is lost; uploading in batches after recovery; attaching a unique device code and timestamp to all transmitted data; and encrypting and verifying the data using CRC32.

[0066] Specifically, the communication channel is built on 4G / 5G / NB-IoT / BLE, and the channel switching is based on the 5G signal packet loss rate >1% to switch to NB-IoT+BLE. The relay unit is a solar-powered edge repeater, and the data encryption adopts the national cryptographic SM4 algorithm.

[0067] Among them, the channel quality assessment relies on the channel assessment and switching module to realize multi-parameter dynamic monitoring, and collect key indicators such as signal strength (RSSI), packet loss rate (PLR), and latency (RTT) of each communication link in real time. A priority matrix is ​​constructed through a fuzzy comprehensive evaluation model. For example, when the 5G link meets RSSI≥-70dBm and PLR≤1%, it is determined to be the optimal channel. If the 5G packet loss rate is continuously sampled >1% or RSSI≤-90dBm, the switching mechanism is triggered to switch to the collaborative mode of NB-IoT main link and BLE auxiliary verification to ensure the communication continuity in remote scenarios (such as underground mines and water conservancy hubs).

[0068] The solar edge relay module adopts a photovoltaic power supply design with a power output of 10W, forming a communication relay network in areas without grid coverage. When a cloud connection interruption is detected, local storage is activated. After the network is restored, the cached data is uploaded in batches through the breakpoint resume mechanism to avoid data loss caused by network interruption. At the same time, short-range encrypted communication is used between the repeater and the hydraulic valve to further enhance the signal penetration capability in remote areas.

[0069] In addition, secure data transmission is achieved through a multi-layered protection mechanism of encryption and verification modules: First, the original data is encrypted with the national cryptographic SM4 algorithm in 128 bits. A 32-round encryption key is generated through a round key generation algorithm (including FK constant and CK iterative calculation) to ensure data confidentiality. Second, each frame of data is appended with a unique device code, a 1ms precision timestamp, and a CRC32 checksum. The receiving end must simultaneously verify that the timestamp deviation is ≤1s and the checksum is consistent. Otherwise, it is judged as invalid data and a retransmission mechanism is triggered, effectively preventing data tampering and replay attacks.

[0070] Step 5: Fault Diagnosis and Oil Self-Maintenance

[0071] The system monitors the operating status of hydraulic valves in real time, identifies and locates faults through vibration harmonic analysis, and collects oil parameters periodically. If the parameters exceed the standard, the filter is activated. If the parameter changes are out of range, the drive voltage is adjusted to achieve viscosity compensation.

[0072] Specifically, it receives vibration, current and oil data from the sensor array in real time, identifies fault types such as valve core jamming and oil contamination through vibration harmonic analysis and current anomaly detection, monitors the contamination level and viscosity changes of the miniature oil sensor, and automatically starts the ceramic filter valve to purify when the levels exceed the limits. After the fault is handled, a maintenance report is generated and synchronized to the monitoring and command preprocessing module.

[0073] Among them, the multi-dimensional coverage of fault diagnosis is achieved through the collaborative operation of modules: on the one hand, mechanical faults such as valve core jamming are identified by extracting vibration signal features, while the power management module monitors the voltage and current curves of the industrial-grade switching main power supply and the 10Ah lithium-sulfur backup battery in real time. When the main power supply fluctuates or is about to trigger the DC / DC converter switching, a power warning signal is pushed to the algorithm chip in advance (e.g., 10 minutes in advance). Combined with the abnormal drive current of the current sensor (e.g., a sudden increase of ≥15%), the joint location of "mechanical + electrical" faults is achieved. If the main power supply failure triggers the switching, the backup battery is seamlessly powered through the DC / DC converter, ensuring the power continuity of fault diagnosis and maintenance actions.

[0074] In addition, the oil self-maintenance strictly follows the "real-time monitoring-automatic response" logic of the oil maintenance module: the miniature oil sensor uses laser scattering to capture the oil contamination level (ISO4406 level) and viscosity deviation in real time. When the contamination level exceeds 19 / 16 or the viscosity deviation is ≥10%, the ceramic filter element valve is activated to purify the oil by intercepting particulate matter. The filter element is self-cleaned by gas-liquid backflushing. At the same time, the viscosity change data is synchronously fed back to the algorithm chip, which adjusts the drive voltage of the piezoelectric drive module to compensate for the valve core action lag caused by the change in oil viscosity and maintain the opening control accuracy.

[0075] Finally, when a serious fault is detected (such as filter blockage, valve core jamming, and abnormal main power supply), the double-layer honeycomb staggered perforated plate of the hydrophobic protection module and the fluororubber baffle of the pressure balance hole not only prevent external liquid from entering and aggravating the fault, but also assist in motor heat dissipation to ensure the operation of the monitoring unit. If manual intervention is required, the magnetic suction and emergency manual module with a magnetic suction force of ≥50N, together with the Hall position sensor, supports manual adjustment of the valve core when power is off (e.g., the lever-telescopic rod linkage mechanism). After power is restored, it automatically returns to the recorded position without secondary calibration. All fault codes, maintenance actions (including oil purification time, power switching records) and parameter changes are encrypted and uploaded to the cloud and cached locally for ≥3 months, providing traceable evidence for the entire life cycle operation and maintenance.

[0076] Through the above five steps, precise control of the intelligent hydraulic valve is achieved throughout the entire process from command generation to maintenance closed loop, forming a complete technical link of "perception-decision-execution-feedback-optimization". Multimodal interaction and edge preprocessing ensure the accurate generation and legality verification of control commands. The dual-chip collaborative architecture achieves real-time adjustment under complex working conditions through the separation of logic and algorithm operations. The closed-loop feedback mechanism of piezoelectric drive and sensor array ensures the micron-level precision of valve core movement. Dynamic switching and encrypted transmission technology of multimodal communication overcomes the signal bottleneck in remote scenarios, achieving secure and reliable data transmission. The intelligent fault diagnosis and oil self-maintenance function, through vibration characteristic analysis, oil index monitoring and redundant power supply design, builds a comprehensive safety protection system covering mechanical, electrical and oil aspects. Ultimately, it achieves unmanned precise control, stable communication and intelligent operation and maintenance throughout the entire life cycle in complex environments, significantly improving the reliability and efficiency of equipment operation and maintenance.

[0077] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention are also within the protection scope of the present invention.

Claims

1. A remote control method for an intelligent hydraulic valve, characterized in that, The method includes: S1. Select the target hydraulic valve and input the control command. Verify the legality of the control command, read the locally cached historical sensor data, and if the data is abnormal, trigger a local alarm before issuing the command again. At the same time, record the command sending timestamp. S2. After receiving the instruction, the processing task is split, the feedback pressure, temperature, displacement, and vibration status data are read and fused through filtering, and the PID-fuzzy fusion algorithm is called. Combined with the oil viscosity parameters converted from temperature, the driving voltage control quantity is calculated and output and fed back. The processing task is split into logic chip processing switch logic and power management, and algorithm chip processing opening adjustment parameters. Then, the status data is fused through Kalman filtering. The called PID-fuzzy fusion algorithm divides the working conditions according to the pressure change rate, temperature, and vibration frequency. S3. The valve core opening is adjusted according to the control quantity by the piezoelectric component, and the status data is collected and uploaded in real time. If the deviation between the actual value of the opening and the target value is detected, the deviation signal is fed back, and the control quantity is recalculated to achieve closed-loop regulation. S4. Real-time monitoring of signal strength, packet loss rate and delay of each communication channel, switching and adapting to the appropriate channel according to parameters, receiving status data in the relay unit, caching data when the cloud connection is lost, uploading in batches after recovery, and attaching a unique device code and timestamp to all transmitted data, which is then encrypted and verified by CRC32. S5. Real-time monitoring of hydraulic valve operation status, identification of faults and location of fault sources through vibration harmonic analysis and abnormal drive current detection, and periodic collection of oil parameters. If the parameters exceed the standard, filtration is activated. If the parameter changes exceed the range, the drive voltage is adjusted to achieve viscosity compensation.

2. The remote control method for an intelligent hydraulic valve according to claim 1, characterized in that: In step S1, the target hydraulic valve is selected through the WebGIS interface or a control command is issued via voice command. The validity verification of the control command includes whether the opening degree is within the range of 0-100%. The locally cached historical sensor data is the average pressure and temperature of the past 10 seconds. The data anomaly judgment standard is temperature > 60℃.

3. The remote control method for an intelligent hydraulic valve according to claim 1, characterized in that: In S4, the communication channel is constructed based on 4G / 5G / NB-IoT / BLE. The channel switching is based on the 5G signal packet loss rate >1% when switching to NB-IoT+BLE. The relay unit is a solar edge repeater, and the data encryption adopts the national cryptographic SM4 algorithm.

4. A remote control system for an intelligent hydraulic valve, characterized in that, This system is based on a remote control method for an intelligent hydraulic valve as described in any one of claims 1-3. Specifically, it includes: a monitoring and command preprocessing module that collects equipment operating status data and performs legality verification and noise filtering preprocessing on issued commands, establishing the foundation for real-time equipment status monitoring and accurate command issuance; a dual-chip collaborative control module employing a dual-chip architecture, with a logic processing chip responsible for real-time command parsing and equipment power management, and an algorithm processing chip running adaptive control algorithms and multi-source data fusion; a communication and data security module that monitors the signal strength, packet loss rate, and latency of each communication link in real time, while using national cryptographic algorithms to encrypt transmitted data, and adding a unique device code and timestamp verification to each data frame; an intelligent actuator in the execution and status perception module that drives the equipment to perform actions according to control commands, and a multi-dimensional sensor array that collects equipment operating status data in real time, providing real-time data support for control decisions and fault diagnosis; and an intelligent redundancy fault-tolerant unit in the safety fault tolerance and maintenance module that seamlessly switches to backup power in the event of a main power failure, and is equipped with an emergency manual mechanism with position memory, eliminating the need for recalibration after power failure recovery, and an equipment self-maintenance unit that locates the fault source through a fault self-diagnosis algorithm and links with maintenance components to restore equipment status.

5. The remote control system for an intelligent hydraulic valve according to claim 4, characterized in that: The monitoring and command preprocessing module includes: a geographic information module, a local edge preprocessing module, and an interaction module; Geographic Information Module: Based on the WebGIS geographic information system, it supports visual annotation of hydraulic valve locations; Local edge preprocessing module: performs filtering and preprocessing on the vibration and noise data collected by the sensors; Interactive module: includes touch screen, voice command recognition, and emergency physical buttons.

6. The remote control system for an intelligent hydraulic valve according to claim 4, characterized in that: The dual-chip collaborative control module includes: a logic chip module, an algorithm chip module, and a synchronization interaction module; Logic chip module: processes valve switching logic and real-time commands for device power management; Algorithm chip module: Runs PID-fuzzy fusion algorithm and Kalman filter data fusion, supports parallel processing of sensor data; Synchronous Interaction Module: Enables dual-chip data interaction through a custom CANopen sub-protocol.

7. The remote control system for an intelligent hydraulic valve according to claim 4, characterized in that: The communication and data security module includes: a communication module, a solar edge relay module, a channel assessment and switching module, and an encryption and verification module; Communication module: integrates 4G / 5G / NB-IoT / BLE communication units to cover signal requirements in different scenarios; Solar edge repeater module: It adopts a solar edge repeater unit with an output power of 10W and a battery life of 72h, which receives hydraulic valve signals and forwards them to the cloud; Channel evaluation and handover module: Real-time monitoring of signal strength, packet loss rate and delay of each channel, and outputting priority through fuzzy comprehensive evaluation model; Encryption and verification module: Data transmission is encrypted using the national standard SM4 algorithm, and each data frame is appended with a unique device code, a 1ms precision timestamp, and a CRC32 checksum.

8. The remote control system for an intelligent hydraulic valve according to claim 4, characterized in that: The execution and state perception module includes: a piezoelectric drive module and a sensor array module; Piezoelectric drive module: It adopts a piezoelectric ceramic stacked drive unit with a response speed of ≤10µs and an opening control accuracy of 0.01mm, replacing the traditional electromagnetic coil drive; Sensor array module: includes pressure sensor, temperature sensor, displacement sensor, and vibration sensor, which collects hydraulic valve operating status data in real time.

9. The remote control system for an intelligent hydraulic valve according to claim 4, characterized in that: The safety fault tolerance and maintenance module includes: a power management module, a magnetic attraction and emergency manual module, a hydrophobic protection module, and an oil maintenance module; Power management module: Includes an industrial-grade switching main power supply and a 10Ah lithium-sulfur backup battery. In the event of a main power failure, it seamlessly switches to the backup battery via a DC / DC converter and provides early warning of battery degradation. Magnetic and Emergency Manual Module: It adopts a magnetic positioning component with a magnetic force of ≥50N + Hall position sensor. When the power is off, the valve core is manually adjusted and it automatically returns to the recorded position after the power is restored. Hydrophobic protection module: It adopts a double-layer honeycomb staggered perforated plate, with a fluororubber baffle with Φ2mm pressure balance hole to prevent liquid intrusion and negative pressure moisture absorption, and assist in motor heat dissipation; Oil maintenance module: Includes a miniature oil sensor and a 5µm precision ceramic filter valve. It automatically filters when contamination exceeds the standard and adjusts the drive voltage to compensate when viscosity changes.

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