Remote control method and system for intelligent hydraulic valve

Abstract

The invention 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 dual-chip cooperative control module, a communication and data security module, an execution and state sensing module and a security fault tolerance and maintenance module. Real-time control and complex algorithm operation are carried out through cooperation of double chips, multi-dimensional state perception and working condition self-adaptive adjustment logic are combined, a quick-response driving assembly is matched, the control precision of the hydraulic valve is remarkably improved, the fine adjustment requirement in an industrial scene is met, and by means of dynamic switching of multi-mode communication and an edge relay mechanism, the control precision of the hydraulic valve is greatly improved. The communication stability in a remote complex scene is enhanced, and the control interruption risk is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic valves, and more particularly discloses a remote control method and system for an intelligent hydraulic valve. BACKGROUND

[0002] An intelligent hydraulic valve is an automated component combining digital hydraulic technology and an intelligent control system, which can accurately control the pressure, flow rate and flow direction of a hydraulic system through an electric pulse signal, and can realize real-time monitoring and fault early warning, improve efficiency, and adapt to complex working conditions through remote control.

[0003] The patent document with the authorization announcement number CN118407960B discloses an intelligent control method and system for a hydraulic valve with online performance monitoring, which acquires parameter monitoring data in real time through an information acquisition module; a data processing module compresses the real-time parameter monitoring data, extracts a number of pre-image strings and post-image strings from the parameter monitoring data after binary conversion, and selects a compression string of data to be processed from them, and sequentially extracts a string identical to the corresponding combination from the data to be processed according to a number of combinations corresponding to two, three,..., P2-bit unsigned binary numbers, and determines the replacement string of the data to be processed, which can effectively reduce the amount of data transmission in the hydraulic valve control system, and can ensure the integrity and accuracy of the data are not affected.

[0004] The patent document with the authorization announcement number CN114165501B discloses a hydraulic valve control method, system and device, which includes a controller loading a modular program; the modular program at least includes: at least one general function module, the calling sequence of each general function module, the input-output relationship between the general function modules, and the connection relationship between the interface of the hydraulic valve and the general function modules; each general function module encapsulates a plurality of functions; each general function module provides at least one pin externally; the pin internally encapsulates the definition of a plurality of variables, and 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 the operating state of the hydraulic valve, identifying faults and locating the fault source through vibration harmonic analysis, and periodically collecting oil parameters, and starting filtration when the parameters exceed the standard, and adjusting the driving voltage when the parameter changes exceed the range, to achieve viscosity compensation.

[0013] Further, in S1, the target hydraulic valve is selected through a WebGIS interface or a control instruction is issued through a voice command, and the control instruction validity verification includes whether the opening degree is within the range of 0-100%, and the local cache sensor historical data is the pressure and temperature average value in the last 10s, and the data abnormality determination standard is temperature>60℃.

[0014] Further, in S2, the processing task is split into logic chip processing switch logic and power management, and the algorithm chip processes the opening degree adjustment parameter, and then the state data is fused through Kalman filtering, and the called PID-fuzzy fusion algorithm is divided into working conditions according to the pressure change rate, temperature and vibration frequency.

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

[0016] According to another aspect of the application, a remote control system for an intelligent hydraulic valve is provided, which is realized based on the above-mentioned remote control method for an intelligent hydraulic valve, and specifically includes: the monitoring and instruction preprocessing module collects device operating state data and performs legality verification and noise filtering preprocessing on the issued instruction, and constructs the basis for real-time monitoring of device state and accurate issuance of instructions; the dual-chip collaborative control module adopts a dual-chip architecture, a logic processing chip is responsible for real-time instruction analysis and device power management, and an algorithm operation chip runs an adaptive control algorithm and multi-source data fusion; the communication and data security module monitors the signal strength, packet loss rate and delay of each communication link in real time, and encrypts the transmission data using a national encryption algorithm, and each frame of data is attached with a device unique code and a time stamp for verification; the execution and state sensing module drives the device to execute actions according to the control instruction, and a multi-dimensional sensing array collects device operating state data in real time, providing real-time data support for control decision and fault diagnosis; the safety fault-tolerant and maintenance module seamlessly switches to the standby power supply when the main power supply fails, and is equipped with an emergency manual mechanism with position memory, which does not need to be recalibrated after power recovery, and a device self-maintenance unit locates the fault source through a fault self-diagnosis algorithm, and links the maintenance components to realize device state repair.

[0017] Further, the monitoring and instruction preprocessing module includes: a geographic information module, a local edge preprocessing module, and an interaction module.

[0018] Geographic Information Module: Based on WebGIS geographic information system, support hydraulic valve position visualization annotation;

[0019] Local Edge Preprocessing Module: Filter and preprocess the vibration noise data collected by the sensor;

[0020] Interaction Module: Contains 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 synchronous interaction module.

[0022] Logic Chip Module: Processes switch valve logic and real-time device power management instructions.

[0023] Algorithm Chip Module: Runs PID-fuzzy fusion algorithm and Kalman filter data fusion, supporting sensor data parallel processing.

[0024] Synchronous Interaction Module: Achieves dual-chip data interaction through 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 different signal requirements in different scenarios.

[0027] Solar Edge Relay Module: Uses a solar edge relay unit with an output power of 10W and a battery life of 72h to receive hydraulic valve signals and forward them to the cloud.

[0028] Channel Assessment and Switching Module: Real-time monitors channel signal strength, packet loss rate, and delay, and outputs priority through a fuzzy comprehensive evaluation model.

[0029] Encryption and Verification Module: Uses the national SM4 algorithm to encrypt transmission data, and adds device unique code, 1ms precision timestamp, and CRC32 verification to each frame of data.

[0030] Furthermore, the execution and state awareness module includes a piezoelectric drive module and a sensor array module.

[0031] Piezoelectric Drive Module: Uses a piezoelectric ceramic stack drive unit with a response speed ≤10µs and an opening control accuracy of 0.01mm, replacing traditional electromagnetic coil drive.

[0032] Sensor Array Module: Includes pressure sensors, temperature sensors, displacement sensors, and vibration sensors to collect real-time hydraulic valve operation state data.

[0033] Further, the safety fault-tolerant and maintenance module comprises a power management module, a magnetic attraction and emergency manual module, a hydrophobic protection module, and an oil maintenance module.

[0034] The power management module comprises an industrial-grade switching main power supply and a 10Ah lithium-sulfur backup battery, and when the main power supply fails, it is seamlessly switched to the backup battery through a DC / DC converter, and early warning of battery attenuation is provided.

[0035] The magnetic attraction and emergency manual module adopts a magnetic attraction positioning assembly with a suction force of ≥50N and a Hall position sensor, and manually adjusts the valve core when power is off, and automatically returns to the recorded position after power is restored.

[0036] The hydrophobic protection module adopts a double-layer honeycomb-shaped staggered hole plate, and a fluororubber baffle with a Φ2mm pressure balance hole, to prevent liquid intrusion and negative pressure moisture absorption, and assist in motor heat dissipation.

[0037] The oil maintenance module comprises a miniature oil sensor and a 5µm precision ceramic filter valve, and automatically filters when the pollution exceeds the standard, adjusts the driving voltage when the viscosity changes, and compensates.

[0038] The beneficial effects of the remote control method and system of the intelligent hydraulic valve are as follows: through real-time control and complex algorithm operation of double-chip cooperation, combined with multi-dimensional state perception and working condition self-adaptive adjustment logic, and matched with a fast-response driving assembly, the control precision of the hydraulic valve is significantly improved, the fine adjustment demand in the industrial scene is met, relying on the dynamic switching and edge relay mechanism of multi-modal communication, the communication stability in remote complex scenes is enhanced, the risk of control interruption is reduced, at the same time, through multi-parameter correlation analysis, the fault source is accurately located, the fault processing speed is accelerated, and the system operation reliability is improved, in addition, through the design of a low-temperature adaptive redundant power supply and a pressure-proof hydrophobic protection structure, the adaptability of the system in harsh environments is enhanced, the stable operation in extreme temperature and humidity scenes is ensured, and with the help of real-time monitoring and automatic maintenance of the oil state, the frequency of manual detection is reduced, and the position memory mechanism of emergency operation is matched, so that the repeated calibration step after power failure is saved, the operation and maintenance cost is effectively reduced, and the efficiency and economy of unmanned operation and maintenance of the industrial hydraulic system are realized. BRIEF DESCRIPTION OF DRAWINGS

[0039] The present application will be further described in detail below in combination with the drawings and specific implementation methods.

[0040] Fig. 1 It is a system framework schematic diagram.

[0041] Fig. 2 It is a method flowchart. DETAILED DESCRIPTION

[0042] Hereinafter, the present application will be described in detail with reference to the accompanying drawings and embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0043] According to one aspect of the present application, as shown in Figs. 1-2 A remote control method and system of an intelligent hydraulic valve are provided, comprising:

[0044] Step 1, instruction generation and preprocessing

[0045] The target hydraulic valve is selected and the control instruction is inputted, the control instruction is verified for legality, the historical data of the sensor in local cache is read, if the data is abnormal, local alarm is triggered before the instruction is issued, and the instruction sending timestamp is recorded;

[0046] Specifically, the target hydraulic valve is selected through a WebGIS interface or the control instruction is issued through a voice instruction, the control instruction legality verification includes whether the opening degree is within the range of 0-100%, the historical data of the sensor in local cache is the average value of pressure and temperature in the last 10s, and the data abnormality determination standard is temperature>60℃.

[0047] The selection of the target hydraulic valve is realized by relying on a geographic information module, which completes the position visualization labeling of the distributed hydraulic valve (for example, map labeling of mine underground pipe network and water hub valve group) based on a WebGIS geographic information system. The operator can directly view the real-time running state identification (for example, normal, abnormal, offline) of each valve body in the labeling interface, quickly locate the target valve body to be adjusted, avoid operation errors caused by ambiguous valve body position in a distributed scene, and input the control instruction through an interactive module. The module integrates a touch screen, voice instruction recognition and emergency physical buttons, supports manual input of opening degree value, pressure threshold and other specific parameters through the touch screen, and can also issue control requirements through voice instructions (for example, "adjust the opening degree of No. 3 hydraulic valve to 50%"), which is suitable for scenes where the touch screen cannot be conveniently operated in harsh environments such as high humidity, dust and low temperature. The emergency physical button can trigger basic instructions such as emergency valve closing and pressure maintaining when the touch screen and voice functions fail;

[0048] The legality verification of the control instruction is completed by the local edge preprocessing module, which is equipped with an ARM Cortex-A72 chip and verifies the effectiveness of the input control parameters. On the one hand, it verifies whether the opening degree parameter is within the mechanical stroke range of 0-100% and whether the pressure threshold exceeds the rated working pressure of the hydraulic system (for example, the common 31.5 MPa of metallurgical hydraulic systems), to avoid valve core sticking or valve set seal damage due to parameter overrun. On the other hand, it verifies the logical consistency of the instruction, such as whether the combination of "opening degree 100%" and "pressure threshold lower than the current system pressure" is contradictory. If there is parameter overrun or logical contradiction, a local audible and visual alarm (such as a red warning light flashing and a buzzer) is triggered, the cause of the alarm (such as "opening degree out of effective range") is displayed on the touch screen, and the instruction issuance is blocked, forming the first safety protection.

[0049] In addition, the local edge preprocessing module reads the historical data of the sensors cached in the past 10 seconds. These data come from real-time data collected by the sensor array module (such as pressure, temperature, etc.). The module performs vibration noise filtering and preprocessing on the raw data (using a sliding mean filter algorithm to filter out interference data caused by valve core vibration and pipeline pulse), and finally outputs accurate pressure and temperature averages. When the temperature average is detected to be >60°C (or the pressure average exceeds the normal working condition range, such as normal working pressure 10-15 MPa, current average 20 MPa), the instruction issuance process is suspended first. In addition to triggering a local alarm, it also synchronizes the abnormal data to the WebGIS interface, marks the abnormal valve position and displays the abnormal parameters. After the on-site operation and 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 confirms "allow issuance" through the interactive module, and the module will continue to issue control instructions to the dual-chip collaborative control module, avoiding executing adjustment actions in abnormal system state and causing secondary failure.

[0050] Step 2, control algorithm operation and data fusion

[0051] After receiving the instruction, the task is split and processed, the feedback pressure, temperature, displacement, and vibration state data are read and filtered and fused, and the PID-fuzzy fusion algorithm is called, combined with the oil viscosity parameter converted by temperature, to calculate and output the drive voltage control quantity and feedback.

[0052] Specifically, the split processing task is to process the switch logic and power management for the logic chip, and to process the opening degree adjustment parameter for the algorithm chip. Then, the state data is fused through Kalman filtering, and the PID-fuzzy fusion algorithm is called according to the pressure change rate, temperature, and vibration frequency to divide the working conditions.

[0053] The logic chip adopts an ARM Cortex-M4 chip and focuses on processing tasks with high real-time requirements. On one hand, it analyzes switch valve logic (such as timing control of valve core opening / closing and sequence of multi-valve group linkage) to ensure that the response time of instructions is ≤1ms, avoiding flow regulation lag caused by delay. On the other hand, it is responsible for power management related instructions (such as monitoring main power voltage fluctuations and determining whether to trigger backup power switching), and outputs power status signals to the algorithm chip in real time to ensure stable system power supply.

[0054] The algorithm chip adopts an FPGA XC7K325T chip and has parallel computing capability, which can synchronously read four-dimensional data such as pressure (±0.1% FS), temperature (±0.5℃), displacement (±0.005mm), and vibration (±0.1g) feedback from the sensor array module. To eliminate single sensor error and environmental interference (such as displacement data fluctuation caused by pipeline vibration), the algorithm chip uses Kalman filtering algorithm to fuse and process the data. By establishing state equations and observation equations, it filters out abnormal data such as vibration noise and electromagnetic interference, improves the precision of fused state data, and provides an accurate data basis for subsequent algorithm operation. Data interaction between the two chips is realized through a custom CANopen sub-protocol of the synchronous interaction module. Specifically, the logic chip and the algorithm chip build a duplex communication link through the synchronous interaction module. The logic chip encapsulates the parsed switch logic instructions (such as valve core action timing code and power status flag) and real-time power parameters (such as main and backup power voltage and current) in the format of "logic instruction frame" of the custom CANopen sub-protocol. Each frame contains an 11-bit identifier (used to distinguish instruction types) and an 8-byte data segment (stores instruction parameters and check code). The algorithm chip returns PID parameters, fuzzy control rule set, and multi-sensor fusion results in the format of "algorithm parameter frame". The timestamp synchronization mechanism of the synchronous interaction module adds a timestamp (such as microseconds) to each frame of data. The receiving end dynamically adjusts the data transmission timing by comparing the timestamp deviation, ensuring that the synchronization error of logic instructions and algorithm parameters is ≤50µs, avoiding control deviation caused by asynchronous data.

[0055] Meanwhile, the call of PID-fuzzy fusion algorithm needs to complete the working condition recognition first. The algorithm chip divides the working condition according to the fused pressure rate of change, temperature, and vibration frequency. When the pressure rate of change is ≤0.5MPa / s and the temperature is between 20-40℃, it is determined as normal working condition, and PID control is used to ensure the stability of opening regulation. When the pressure rate of change is >1MPa / s or the temperature is <0℃ / >60℃, it is determined as sudden change working condition (such as sudden temperature drop causing oil viscosity to rise suddenly, pressure pulse impact), and fuzzy control is switched to quickly suppress the disturbance caused by parameter mutation. At the same time, the algorithm chip combines the oil viscosity parameter transmitted by the oil maintenance module to convert the temperature by the following formula:

[0056]

[0057] In the formula, 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 with 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 of an intelligent hydraulic valve, characterized in that, The method comprises: S1, selecting a target hydraulic valve and inputting a control instruction, verifying the legality of the control instruction, reading the historical data of the sensor cached locally, triggering a local alarm if the data is abnormal, and then issuing the instruction, and recording the instruction sending timestamp; S2, after receiving the instruction, split the processing task, read the feedback pressure, temperature, displacement, vibration state data and fuse through filtering, and call the PID-fuzzy fusion algorithm, combine the oil viscosity parameter converted by temperature, calculate and output the drive voltage control quantity and feedback; S3, adjust the valve core opening degree through the piezoelectric component according to the control quantity, real-time collect state data upload, if the deviation between the actual value and the target value of the opening degree is detected, feedback the deviation signal, recalculate the control quantity to realize closed-loop regulation; S4, real-time monitor the signal strength, packet loss rate and delay of each communication channel, switch and adapt the channel according to the parameters, the relay unit receives the state data, caches the data when the cloud is disconnected, uploads in batches after recovery, all transmission data are attached with equipment unique code and timestamp, and are encrypted and CRC32 checked; S5, real-time monitor the running state of the hydraulic valve, identify and locate the fault source through vibration harmonic analysis, periodically collect oil parameters, and start filtering when the parameters exceed the standard, adjust the drive voltage when the parameter changes exceed the range, and realize viscosity compensation.

2. The method of claim 1, wherein: In S1, the target hydraulic valve is selected through the WebGIS interface or the control instruction is issued through the voice instruction, the control instruction legality verification includes whether the opening degree is within 0-100%, the historical data of the sensor cached locally is the average pressure and temperature in the last 10s, and the data abnormality determination standard is temperature>60℃.

3. The method of claim 1, wherein: In S2, the split processing task is the logic chip processing switch logic and power management, the algorithm chip processes the opening degree adjustment parameter, then the state data is fused through Kalman filtering, and the called PID-fuzzy fusion algorithm is divided into working conditions according to the pressure change rate, temperature and vibration frequency.

4. The method of claim 1, wherein: In S4, the communication channel is built based on 4G / 5G / NB-IoT / BLE, the channel switching is based on 5G signal packet loss rate>1% to switch to NB-IoT+BLE, the relay unit is a solar edge relay, and the data encryption adopts the national SM4 algorithm.

5. A remote control system for an intelligent hydraulic valve, characterized in that The system is realized based on the remote control method of the intelligent hydraulic valve according to any one of claims 1-4, and specifically comprises: the monitoring and instruction preprocessing module collects device operation state data and performs legality verification and noise filtering preprocessing on the issued instructions, thereby constructing the basis for real-time monitoring of device state and accurate issuance of instructions; the dual-chip collaborative control module adopts a dual-chip architecture, wherein a logic processing chip is responsible for real-time instruction analysis and device power management, and an algorithm operation chip runs an adaptive control algorithm and multi-source data fusion; the communication and data security module monitors the signal strength, packet loss rate and delay of each communication link in real time, encrypts the transmission data using a national encryption algorithm, and adds a device unique code and a timestamp check to each frame of data; the execution and state sensing module drives the device to perform actions according to the control instructions, and a multi-dimensional sensor array collects device operation state data in real time to provide real-time data support for control decision-making and fault diagnosis; the safety fault-tolerant and maintenance module seamlessly switches to a backup power supply when the main power supply fails, is equipped with an emergency manual mechanism with position memory, and does not need to be recalibrated after power recovery; a device self-maintenance unit locates the fault source through a fault self-diagnosis algorithm, and links the maintenance components to realize device state repair.

6. The remote control system of an intelligent hydraulic valve according to claim 5, characterized in that: The monitoring and instruction preprocessing module comprises: a geographic information module, a local edge preprocessing module, and an interaction module. The geographic information module is based on a WebGIS geographic information system and supports visual labeling of the position of the hydraulic valve. The local edge preprocessing module filters and preprocesses vibration noise data collected by sensors. The interaction module includes a touch screen, voice command recognition, and emergency physical buttons.

7. The remote control system of an intelligent hydraulic valve according to claim 5, characterized in that: The dual-chip collaborative control module comprises: a logic chip module, an algorithm chip module, and a synchronous interaction module. The logic chip module processes switch valve logic and real-time instructions for device power management. The algorithm chip module runs a PID-fuzzy fusion algorithm and Kalman filter data fusion, and supports parallel processing of sensor data. The synchronous interaction module realizes data interaction between the two chips through a custom CANopen sub-protocol.

8. The remote control system of an intelligent hydraulic valve according to claim 5, characterized in that: The communication and data security module comprises: a communication module, a solar edge relay module, a channel evaluation and switching module, and an encryption and verification module. The communication module integrates 4G / 5G / NB-IoT / BLE communication units to cover different signal requirements in different scenarios. The solar edge relay module uses a solar edge relay unit with an output power of 10W and a battery life of 72h to receive hydraulic valve signals and forward them to the cloud. The channel evaluation and switching module monitors the signal strength, packet loss rate, and delay of each channel in real time, and outputs a priority through a fuzzy comprehensive evaluation model. The encryption and verification module uses the national encryption algorithm SM4 to encrypt transmission data, and adds a device unique code, a 1ms precision timestamp, and a CRC32 check to each frame of data.

9. The remote control system of an intelligent hydraulic valve according to claim 5, characterized in that: The execution and state sensing module comprises: a piezoelectric drive module and a sensor array module. The piezoelectric drive module uses a piezoelectric ceramic stack drive unit, has a response speed of ≤10µs, and an opening control precision of 0.01mm, replacing traditional electromagnetic coil drives. Sensor array module: including pressure sensor, temperature sensor, displacement sensor, vibration sensor, real-time acquisition of hydraulic valve operating state data.

10. The remote control system of an intelligent hydraulic valve according to claim 5, characterized in that: The safety fault-tolerant and maintenance module includes: power management module, magnetic attraction and emergency manual module, drainage protection module, oil maintenance module; Power management module: contains industrial-grade switching main power supply and 10Ah lithium-sulfur backup battery, when the main power fails, it will be seamlessly switched to the backup battery through the DC / DC converter, and early warning battery attenuation; Magnetic attraction and emergency manual module: uses a magnetic attraction positioning component with a suction force ≥50N + a Hall position sensor, manually adjusts the valve core when power is off, and automatically returns to the recorded position after power is restored; Hydrophobic protection module: uses double-layer honeycomb-shaped staggered hole plates, combined with fluororubber baffles with Φ2mm pressure balance holes, to prevent liquid intrusion and negative pressure moisture absorption, and assist in motor cooling; Oil maintenance module: contains a miniature oil sensor and a 5µm precision ceramic filter valve, which automatically filters when the pollution exceeds the standard, adjusts the driving voltage when the viscosity changes, and compensates.

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