Remote control system of electromagnetic directional valve and data processing method

Abstract

The invention relates to the technical field of electromagnetic directional valve control, and discloses an electromagnetic directional valve remote control system and a data processing method, and the system comprises a control instruction receiving and analyzing module, a dynamic response parameter determining module, a state data collecting and uploading module, a state data analyzing module and a control signal executing module. The method comprises the following steps: receiving a remote terminal reversing control instruction, and analyzing the instruction into adjustment parameters such as action times, switching directions and pressure thresholds; determining dynamic response parameters of a control node based on the parameters, executing adjustment, and collecting state data such as current and magnetic flux; uploading the data to an edge calculation unit, and generating a pulse width modulation correction value of the first control node through data fusion, frequency domain correlation analysis, wavelet domain dynamic compensation aggregation and other processing; and the parameters are transmitted to the fuzzy controller to calibrate the driving circuit parameters. The method improves the remote control precision and stability, and is suitable for industrial automatic intelligent control.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic directional valve control, and specifically to a remote control system and data processing method for electromagnetic directional valves. Background Art

[0002] Under the traditional control mode, the parameter adjustment of electromagnetic directional valves depends on local hardware or on-site manual operation, and cannot meet the requirements of remote real-time control in complex industrial scenarios. For example, in the fields of petrochemical industry, intelligent equipment manufacturing, etc., the equipment is usually distributed in a vast area or a high-risk environment, and it is difficult for operators to obtain the operating status of each node in real time and adjust the parameters, resulting in low efficiency of working condition switching, and even production accidents caused by control lag. At the same time, the traditional system lacks a multi-device collaborative control mechanism. When multiple electromagnetic directional valves need to work together, it is difficult to achieve action synchronization through unified remote instructions, affecting the overall coordination of the system.

[0003] The acquisition and analysis of the state data of electromagnetic directional valves by traditional control technologies remain at a single dimension, and only basic physical quantities such as current and pressure can be obtained. Moreover, the data processing method is simple, and the implicit correlations between different parameters cannot be mined. For example, current fluctuations may have complex relationships with magnetic flux changes, spool wear degrees, etc. However, the traditional system only judges the equipment status by setting a single threshold, which is likely to miss early faults or false trigger alarms. In addition, the state data is usually only stored or displayed locally, and no closed-loop feedback is formed with the control strategy, and the control parameters cannot be dynamically adjusted according to real-time data, resulting in a decrease in control accuracy as the equipment operation time increases. Especially in complex working conditions such as high-frequency commutation and variable load, the problem of parameter drift is more significant.

[0004] Traditional control algorithms (such as PID control) are based on fixed parameter logic and are difficult to adapt to the non-linear characteristics of electromagnetic directional valves in different working stages. For example, the spool is greatly affected by inertial forces during the start-up stage, and it also needs to cope with interference factors such as changes in the viscosity of hydraulic oil and temperature drift during steady-state operation. Traditional algorithms cannot compensate for these dynamic errors in real time, which may lead to problems such as action delay, excessive impact current, or system oscillation. At the same time, when multiple control nodes work together, there is a lack of effective frequency domain feature analysis and interference suppression mechanisms, and signal coupling between nodes is likely to cause resonance or inconsistent responses, further reducing the system stability.

[0005] Under the industrial Internet architecture, due to poor protocol compatibility and insufficient edge computing capabilities, the traditional electromagnetic directional valve control system is difficult to achieve seamless connection with cloud platforms and upper management systems. For example, device status data cannot be efficiently uploaded to the cloud for big data analysis, which restricts the implementation of intelligent applications such as predictive maintenance and energy consumption optimization. In addition, the hardware architecture and software modules of traditional systems have poor scalability. When facing the flexible production requirements of multi-variety and small-batch, it is difficult to quickly reconstruct control strategies or adapt to different models of devices, increasing the cost of upgrading and transforming production lines.

[0006] In the prior art, although some research has tried to introduce Internet of Things technology and machine learning algorithms into the control of electromagnetic directional valves, most of them stay at the level of single-point data monitoring or offline model training, and a complete closed-loop from data collection, multi-dimensional analysis to dynamic control has not been constructed. For example, the remote instruction issuing technology based on wireless transmission fails to solve the computational complexity problem brought by multi-node data fusion, and the fault diagnosis model based on deep learning cannot achieve real-time online inference due to limited edge computing resources. Therefore, there is an urgent need for a new control system and method with the capabilities of remote instruction parsing, multi-dimensional data correlation analysis, and dynamic parameter calibration to improve the adaptability and reliability of electromagnetic directional valves in industrial intelligent scenarios. Summary of the Invention

[0007] The purpose of the present invention is to provide a remote control system and data processing method for electromagnetic directional valves to solve the problems proposed in the above background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A remote control system and data processing method for electromagnetic directional valves, the method includes: Receiving a commutation control instruction sent by a remote terminal; Parsing the commutation control instruction to obtain the adjustment parameters of the electromagnetic directional valve, where the adjustment parameters include the number of actions, switching direction, and pressure threshold; Based on the adjustment parameters, determining the dynamic response parameters of multiple control nodes of the electromagnetic directional valve; Performing real-time adjustment actions based on the dynamic response parameters of the multiple control nodes, and synchronously collecting the status data of the multiple control nodes, where the status data includes current data and magnetic flux data; Uploading the status data of the multiple control nodes to the edge computing unit; In the edge computing unit, performing multi-dimensional correlation analysis on the status data of the multiple control nodes to generate a control signal, where the control signal is the pulse width modulation correction value of the first control node; Transmitting the control signal to a fuzzy controller to calibrate the parameters of the drive circuit corresponding to the first control node.

[0009] Preferably, in the edge computing unit, multi-dimensional correlation analysis is performed on the status data of the multiple control nodes to generate a control signal, and the control signal is the pulse width modulation correction value of the first control node, including: Performing data fusion on the status data of the multiple control nodes to generate frequency domain sequences of the status data of the multiple control nodes; Performing frequency domain implicit correlation analysis on the frequency domain sequences of the status data of the multiple control nodes to generate frequency domain correlation feature vectors of the multiple control nodes; Performing wavelet domain dynamic compensation aggregation on the frequency domain correlation feature vectors of the multiple control nodes to generate a frequency domain aggregation characterization matrix of the control nodes; Extracting a first frequency domain correlation feature vector from the frequency domain correlation feature vectors of the multiple control nodes, and calculating the matching degree of the first frequency domain correlation feature vector relative to the frequency domain aggregation characterization matrix of the control nodes to generate a first frequency domain matching feature matrix; Generating the control signal based on the first frequency domain matching feature matrix.

[0010] Preferably, performing data fusion on the status data of the multiple control nodes to generate frequency domain sequences of the status data of the multiple control nodes includes: Performing data reduction on the status data of the multiple control nodes according to the frequency domain dimension and the adjustment parameter sample dimension to generate current data frequency domain sequences of the multiple control nodes and magnetic flux data frequency domain sequences of the multiple control nodes; Adding the corresponding magnetic flux data after each current data in the current data frequency domain sequences of the multiple control nodes to generate the frequency domain sequences of the status data of the multiple control nodes.

[0011] Preferably, performing frequency domain implicit correlation analysis on the frequency domain sequences of the status data of the multiple control nodes to generate frequency domain correlation feature vectors of the multiple control nodes includes: inputting the frequency domain sequences of the status data of the multiple control nodes into a frequency domain encoder based on a wavelet transform-Fourier hybrid model to generate the frequency domain correlation feature vectors of the multiple control nodes.

[0012] Preferably, performing wavelet domain dynamic compensation aggregation on the frequency domain correlation feature vectors of the multiple control nodes to generate a frequency domain aggregation characterization matrix of the control nodes includes: Inputting the frequency domain correlation feature vectors of the multiple control nodes into a wavelet domain feature extraction network to generate a frequency domain wavelet feature matrix of the control nodes; Based on the frequency domain wavelet feature matrix of the control nodes, performing fuzzy membership modulation on the frequency domain correlation feature vectors of each control node in the frequency domain correlation feature vectors of the multiple control nodes to generate a fuzzy frequency domain node-wavelet complementary information embedding coding matrix of the multiple control nodes; Fuse the frequency-domain wavelet feature matrix of the control node and the fuzzy frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes to generate the frequency-domain aggregation characterization matrix of the control node.

[0013] Preferably, based on the frequency-domain wavelet feature matrix of the control node, perform fuzzy membership modulation on the frequency-domain correlation feature vectors of each control node in the frequency-domain correlation feature vectors of the multiple control nodes to generate a fuzzy frequency-domain node-wavelet complementary information embedding coding matrix, including: Calculate the complementary information of each control node's frequency-domain correlation feature vector in the frequency-domain correlation feature vectors of the multiple control nodes relative to the frequency-domain wavelet feature matrix of the control node to generate a frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes; Input the frequency-domain node-wavelet complementary information embedding coding matrix of each control node in the frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes into a complementary information saliency identification module based on fuzzy rules to generate the frequency-domain complementary information fuzzy weights of the multiple control nodes; Based on the frequency-domain complementary information fuzzy weights of the multiple control nodes, perform membership modulation on the frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes to generate the fuzzy frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes.

[0014] Preferably, extract a first frequency-domain correlation feature vector from the frequency-domain correlation feature vectors of the multiple control nodes, and calculate the matching degree of the first frequency-domain correlation feature vector relative to the frequency-domain aggregation characterization matrix of the control node to generate a first frequency-domain matching feature matrix, including: Extract the frequency-domain correlation feature vector corresponding to the first control node from the frequency-domain correlation feature vectors of the multiple control nodes to generate the first frequency-domain correlation feature vector; Calculate the covariance matrix between the first frequency-domain correlation feature vector and the frequency-domain aggregation characterization matrix of the control node to generate the first frequency-domain matching feature matrix.

[0015] Preferably, based on the first frequency-domain matching feature matrix, generate the control signal, including: passing the first frequency-domain matching feature matrix through a control signal generator based on fuzzy inference to generate the control signal.

[0016] Preferably, the present invention further includes an electromagnetic reversing valve remote control system, and the system includes: A control instruction receiving module, configured to receive a reversing control instruction sent by a remote terminal; A control instruction parsing module, which is used to parse the commutation control instruction to obtain the adjustment parameters of the electromagnetic commutation valve, and the adjustment parameters include the number of actions, the switching direction, and the pressure threshold; A dynamic response parameter determination module, which is used to determine the dynamic response parameters of multiple control nodes of the electromagnetic commutation valve based on the adjustment parameters; A state data acquisition module, which is used to perform real-time adjustment actions based on the dynamic response parameters of the multiple control nodes and synchronously acquire the state data of the multiple control nodes, wherein the state data includes current data and magnetic flux data; A state data uploading module, which is used to upload the state data of the multiple control nodes to the edge computing unit; A state data analysis module, which is used to perform multi-dimensional correlation analysis on the state data of the multiple control nodes in the edge computing unit to generate a control signal, and the control signal is the pulse width modulation correction value of the first control node; A control signal execution module, which is used to transmit the control signal to a fuzzy controller to calibrate the parameters of the drive circuit corresponding to the first control node.

[0017] Preferably, the state data analysis module includes: A state data fusion unit, which is used to perform data fusion on the state data of the multiple control nodes to generate a state data frequency domain sequence of the multiple control nodes; A frequency domain correlation analysis unit, which is used to perform frequency domain implicit correlation analysis on the state data frequency domain sequence of the multiple control nodes to generate a frequency domain correlation feature vector of the multiple control nodes; A wavelet domain compensation aggregation unit, which is used to perform wavelet domain dynamic compensation aggregation on the frequency domain correlation feature vectors of the multiple control nodes to generate a frequency domain aggregation characterization matrix of the control nodes; A matching feature calculation unit, which is used to extract a first frequency domain correlation feature vector from the frequency domain correlation feature vectors of the multiple control nodes and calculate the matching degree of the first frequency domain correlation feature vector relative to the frequency domain aggregation characterization matrix of the control nodes to generate a first frequency domain matching feature matrix; A control signal generation unit, which is used to generate the control signal based on the first frequency domain matching feature matrix.

[0018] Compared with the prior art, the beneficial effects of the present invention are: By integrating remote instruction interaction, multi-dimensional data fusion analysis, and dynamic control technologies, a high-precision closed-loop control system is constructed, which significantly improves the control performance and reliability of the electromagnetic commutation valve in industrial automation scenarios, as specifically shown below: The system supports receiving the reversing control instructions sent by the remote terminal, and parsing them into adjustment parameters such as the number of actions, switching direction, and pressure threshold, thus realizing remote dynamic configuration of the working mode of the electromagnetic reversing valve. This feature breaks through the geographical limitations of traditional local control and is particularly suitable for the coordinated control of multiple devices in distributed industrial systems. For example, in smart workshops or energy pipelines, operators can issue instructions in real time through remote terminals to quickly complete parameter adjustment and working condition switching of multi-node equipment, reduce manual intervention costs, and improve production efficiency and operation and maintenance flexibility.

[0019] The system realizes closed-loop feedback from data acquisition to control signal generation by synchronously collecting status information such as current data and magnetic flux data from multiple control nodes and combining it with the multi-dimensional correlation analysis capability of the edge computing unit. Specifically, the frequency domain sequence of status data is generated by data fusion, and the frequency domain correlation feature vector is extracted using the wavelet transform-Fourier hybrid model. This can deeply explore the implicit correlation between current and magnetic flux changes and accurately identify abnormal trends in equipment operation (such as frequency component changes in the early stage of valve core wear). This multi-dimensional analysis mechanism breaks through the limitations of traditional single-point threshold detection and improves the system's perception of complex working conditions and fault warning capabilities.

[0020] Based on the wavelet domain dynamic compensation aggregation algorithm, the system performs fuzzy membership modulation and information fusion on the frequency domain correlation feature vectors of each control node to generate a global frequency domain aggregation representation matrix, thus optimizing the collaborative work of multiple control nodes. By calculating the matching degree between the frequency domain characteristics of the target node and the global matrix, and combining fuzzy reasoning to generate pulse width modulation (PWM) correction values, the parameter deviation of the drive circuit can be compensated in real time to improve the control accuracy. This mechanism can adapt to the nonlinear characteristics of the electromagnetic reversing valve in different working stages (such as inertial force in the startup stage and temperature drift in the steady state stage), reduce action delays or system oscillations caused by parameter drift, and enhance the robustness of the control strategy.

[0021] The system uses edge computing units to achieve efficient local data processing and control signal generation, reducing dependence on cloud servers, shortening data processing and response time, and ensuring real-time control. At the same time, it supports uploading status data, control logs, etc. to the cloud for long-term storage and big data analysis, providing data support for predictive maintenance of equipment, optimization of operating efficiency, etc., and promoting the upgrade of industrial automation systems to intelligent and refined management. The modular system architecture design (such as control instruction receiving module and status data analysis module) facilitates docking with different industrial protocols, improves the compatibility and expansion capabilities of the system, and can flexibly adapt to various types of electromagnetic reversing valves and industrial application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1This is the working principle diagram of the remote control system and data processing method for the electromagnetic reversing valve of the present invention; Figure 2 This is the working principle diagram for the edge computing unit to generate control signals through multi-dimensional correlation analysis; Figure 3 This is the working principle diagram for generating the frequency domain sequence of status data through status data fusion; Figure 4 This is the working principle diagram for generating the frequency domain aggregation characterization matrix through wavelet domain dynamic compensation aggregation; Figure 5 This is the working principle diagram for generating the fuzzy frequency domain node-wavelet complementary information embedding coding matrix through fuzzy membership modulation. Specific implementation manners

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0024] Please refer to Figures 1-5 , an electromagnetic reversing valve remote control system and data processing method involved in the present invention, and the specific implementation steps are as follows: The system, through the control instruction receiving module, monitors and obtains in real time the reversing control instruction sent by the remote terminal. This instruction contains the operation intention for the electromagnetic reversing valve and is the starting input of the entire control process.

[0025] The control instruction parsing module parses and processes the received reversing control instruction. Through the preset instruction format and protocol, the adjustment parameters of the electromagnetic reversing valve are extracted therefrom, specifically including the number of actions, switching direction, and pressure threshold. These parameters are the core basis for subsequent control operations and determine the working mode and performance requirements of the electromagnetic reversing valve.

[0026] Based on the above adjustment parameters, the dynamic response parameter determination module calculates and determines the dynamic response parameters of multiple control nodes of the electromagnetic reversing valve in combination with the structural characteristics and working principle of the electromagnetic reversing valve. These dynamic response parameters characterize the response characteristics that each control node should have under specific adjustment parameters, such as response time, action amplitude, etc., and provide specific control objectives for subsequent real-time adjustment.

[0027] The status data acquisition module drives the electromagnetic directional valve to perform real-time adjustment actions according to the dynamic response parameters of multiple control nodes. During the adjustment process, the status data of each control node is synchronously acquired, where the status data includes current data and magnetic flux data. These data reflect the working status of the control nodes in real time and are the basis for subsequent analysis and optimization of control.

[0028] The status data upload module uploads the status data of multiple control nodes collected to the edge computing unit through network transmission and other means for further analysis and processing.

[0029] In the edge computing unit, the status data analysis module performs multi-dimensional correlation analysis on the status data of multiple control nodes uploaded. Through specific analysis algorithms and models, the internal correlations and laws between the data are mined, and finally a control signal is generated, which is the pulse width modulation correction value of the first control node.

[0030] The control signal execution module transmits the generated control signal to the fuzzy controller, and the fuzzy controller calibrates the parameters of the drive circuit corresponding to the first control node according to the control signal, thereby realizing the optimization and adjustment of the control performance of the electromagnetic directional valve.

[0031] Embodiment 1: In the process of the edge computing unit performing multi-dimensional correlation analysis on the status data of multiple control nodes to generate a control signal (the pulse width modulation correction value of the first control node), the following steps are specifically included: Perform status data fusion. The status data (current data and magnetic flux data) of multiple control nodes are respectively subjected to data reduction according to the frequency domain dimension and the adjustment parameter sample dimension. In the frequency domain dimension, the time-domain data is converted into a frequency-domain representation through methods such as Fourier transform, and features such as the amplitude and phase of each frequency component are extracted; in the adjustment parameter sample dimension, the data is grouped and screened according to parameters such as the number of actions, switching direction, and pressure threshold, redundant samples are removed, and the valid data matching the current adjustment parameters is retained, so as to generate the current data frequency-domain sequence and magnetic flux data frequency-domain sequence of multiple control nodes. Subsequently, each data point in the current data frequency-domain sequence is associated with the corresponding magnetic flux data, that is, the magnetic flux data of the same node and the same frequency point is appended after each current data, forming a status data frequency-domain sequence containing dual physical quantity information, realizing the fusion of current and magnetic flux data at the frequency domain level, and providing composite feature data for subsequent analysis.

[0032] Perform frequency-domain implicit correlation analysis. Input the above-mentioned frequency-domain sequence of state data into a frequency-domain encoder based on a wavelet transform-Fourier hybrid model. This encoder first performs global frequency-domain decomposition on the fused frequency-domain sequence through Fourier transform to obtain the energy distribution characteristics of each frequency component; then, using the multi-resolution analysis ability of wavelet transform, it performs hierarchical processing on the frequency-domain signal to extract the detailed characteristics within different frequency sub-bands and capture the dynamic change rules of the data in the local frequency band. Through the collaborative processing of the hybrid model, the correlation characteristics between current and magnetic flux in the frequency-domain sequence are transformed into frequency-domain correlation feature vectors of multiple control nodes, and each vector represents the coupling relationship between current and magnetic flux in the frequency-domain space of the corresponding control node and the potential correlation between nodes.

[0033] Perform wavelet-domain dynamic compensation aggregation. In the first step, input the frequency-domain correlation feature vectors into a wavelet-domain feature extraction network. Through the scale decomposition of wavelet transform, extract the frequency-domain wavelet feature matrix of the control nodes. This matrix contains the frequency-domain detailed characteristics of each node at different wavelet scales and reflects the local characteristics of the signal at multiple scales. In the second step, based on the frequency-domain wavelet feature matrix of the control nodes, calculate the complementary information between each frequency-domain correlation feature vector and this matrix to generate a frequency-domain node-wavelet complementary information embedding coding matrix. The complementary information is obtained by comparing the differences between the feature vectors and the wavelet feature matrix and represents the frequency-domain information unique to each node that is not covered by the wavelet features. In the third step, input the complementary information embedding coding matrix into a complementary information saliency identification module based on fuzzy rules. The module performs fuzzy quantization on the complementary information in each matrix according to the preset fuzzy rules (such as setting the weight intervals corresponding to different complementary information values) to generate the fuzzy weight of the frequency-domain complementary information. The weight value reflects the importance of the complementary information for node state analysis. In the fourth step, use the fuzzy weight to perform membership modulation on the complementary information embedding coding matrix, that is, multiply each element in the matrix by the corresponding weight to highlight the key complementary information and suppress the secondary information, generating a fuzzy frequency-domain node-wavelet complementary information embedding coding matrix. Finally, fuse the frequency-domain wavelet feature matrix of the control nodes and the fuzzy frequency-domain node-wavelet complementary information embedding coding matrix. Through methods such as matrix splicing or weighted summation, generate the frequency-domain aggregation representation matrix of the control nodes. This matrix synthesizes the global frequency-domain characteristics, multi-scale detailed characteristics, and personalized complementary information of the nodes to form a comprehensive representation of the node state.

[0034] Next, perform matching feature calculation. Extract the frequency-domain correlation feature vector corresponding to the first control node from the frequency-domain correlation feature vectors of multiple control nodes, that is, the first frequency-domain correlation feature vector, which represents the frequency-domain state characteristics of the target node. Then calculate the covariance matrix between the first frequency-domain correlation feature vector and the frequency-domain aggregation representation matrix. The elements of the covariance matrix reflect the linear correlation between each dimension of the first frequency-domain correlation feature vector and the corresponding dimensions of the node feature vectors in the frequency-domain aggregation representation matrix, thereby generating the first frequency-domain matching feature matrix, which quantifies the matching degree and correlation strength between the target node and other nodes in the frequency-domain feature space.

[0035] Finally, generate the control signal. Input the first frequency-domain matching feature matrix into the control signal generator based on fuzzy inference. There is a preset fuzzy rule base inside the generator, and the rule base is established based on the control logic of the electromagnetic directional valve and engineering experience. For example, set the pulse width modulation correction direction and amplitude corresponding to different covariance value intervals. Convert the numerical values in the matrix into fuzzy linguistic variables (such as "high matching degree", "medium matching degree", "low matching degree") through fuzzy processing, perform reasoning according to the fuzzy rules, and then output the specific control signal through defuzzification calculation, that is, the pulse width modulation correction value of the first control node. This correction value is used to adjust the pulse width parameter of the target node drive circuit to achieve real-time calibration of the drive circuit, so as to optimize the control accuracy and dynamic response characteristics of the electromagnetic directional valve.

[0036] Embodiment 2: In the process of performing data fusion on the state data of multiple control nodes to generate the state data frequency-domain sequence, first perform data reduction in the frequency domain. For the current data of each control node, use the Fourier transform to convert it from the time domain to the frequency domain. The Fourier transform decomposes the time-domain signal into the superposition of sine waves of different frequencies, and obtains the amplitude spectrum and phase spectrum of the current data in the frequency domain, thereby generating the current data frequency-domain sequence. In this process, according to the working frequency range of the electromagnetic directional valve (such as the preset effective frequency band of 10Hz - 1000Hz), filter out the high-frequency noise and low-frequency interference outside this range, and only retain the frequency components related to the dynamic characteristics of the valve parts. For example, for the current time-domain data with a sampling frequency of 2000Hz, calculate the amplitude of each frequency point through the discrete Fourier transform (DFT), eliminate the low-frequency interference of mechanical vibration below 10Hz and the electronic noise frequency band above 1000Hz, and only retain the frequency-domain data points within 10Hz - 1000Hz to form a refined current data frequency-domain sequence.

[0037] Perform dimensional reduction on the magnetic flux data in the frequency domain. The magnetic flux data reflects the changes in the internal magnetic field of the electromagnetic directional valve. The time-domain signal of the magnetic flux collected by devices such as Hall sensors is converted into a frequency-domain representation through Fourier transform. Since the magnetic field change is directly related to the current drive and its effective frequency range is the same as that of the current data, the same frequency interval filtering rule is adopted to generate the frequency-domain sequence of the magnetic flux data. The frequency-domain sequence of the magnetic flux data of each control node corresponds one-to-one with the frequency-domain sequence of the current data at the frequency points, ensuring the dimensional consistency during subsequent fusion.

[0038] Perform dimensional reduction on the data of the adjustment parameter samples. The adjustment parameters include the number of actions, the switching direction, and the pressure threshold, and each adjustment parameter corresponds to a different working scenario. For example, when the number of actions in the commutation control instruction sent by the remote terminal is "100 times / minute", the switching direction is "forward", and the pressure threshold is "5 MPa", the system forms a unique sample identifier according to the combination of these three parameters. During data processing, traverse the original state data of all control nodes, filter out the data samples that match the current sample identifier, and eliminate the irrelevant data under other adjustment parameters. For example, the current and magnetic flux data collected by a control node when the number of actions is "80 times / minute" will be filtered, and only the corresponding data under the "100 times / minute" sample will be retained, so as to generate a subset of the frequency-domain sequence of the current data and the frequency-domain sequence of the magnetic flux data that match the current adjustment parameters.

[0039] After completing the double dimensional reduction in the frequency domain and the adjustment parameter sample dimension, enter the data fusion stage. For each control node, associate each data point in its current data frequency-domain sequence with the corresponding magnetic flux data. The specific operation is as follows: Under the same adjustment parameter sample, for each frequency point (such as f = 50 Hz) in the current data frequency-domain sequence, find the magnetic flux value at the same frequency point in the magnetic flux data frequency-domain sequence, and use this magnetic flux value as an additional dimension to splice it after the current data to form a composite data point , where is the amplitude of the current in the frequency domain, is the amplitude of the magnetic flux in the frequency domain. For example, if the amplitude of the current in the frequency domain of control node A at f = 50 Hz is 0.8 A and the corresponding amplitude of the magnetic flux in the frequency domain is 0.05 Wb, the composite data at this point after fusion is represented as .

[0040] Through the above steps, the frequency-domain sequence of the state data of each control node is expanded from a single current frequency-domain sequence to a composite frequency-domain sequence containing two physical quantities, current and magnetic flux. This fusion method not only preserves the independent characteristics of each physical quantity in the frequency domain (such as the fundamental wave component and harmonic distribution of current, and the frequency response characteristics of magnetic flux), but also establishes the corresponding relationship between the two at the same frequency point, providing a basis for subsequent analysis of current-magnetic flux coupling characteristics (such as electromagnetic conversion efficiency and hysteresis loss characteristics). For example, at a specific frequency point, if the current amplitude increases while the magnetic flux amplitude does not increase synchronously, it may indicate the presence of magnetic saturation. This composite data can be directly used for frequency-domain implicit correlation analysis without additional data alignment processing.

[0041] The finally generated frequency-domain sequences of the state data of multiple control nodes are stored in matrix form. Each row corresponds to a control node, each column corresponds to a frequency point, and each element is the composite value of current and magnetic flux of the node at the corresponding frequency point. For example, the dimension of the frequency-domain sequence matrix containing 3 control nodes and 100 frequency points is 3×100×2, where the third dimension is the physical quantity dimension (current, magnetic flux). This structured data format facilitates subsequent input to the frequency-domain encoder for feature extraction and meets the computational requirements of multi-node parallel analysis.

[0042] The entire data fusion process ensures the effectiveness and pertinence of the input data through double-dimensional data reduction, realizes the feature enhancement of multi-source data through physical quantity association, provides a standardized composite feature input for subsequent frequency-domain implicit correlation analysis, and lays a foundation for multi-dimensional correlation analysis.

[0043] Example 3: The process of performing frequency-domain implicit correlation analysis on the frequency-domain sequences of the state data of multiple control nodes to generate frequency-domain correlation feature vectors is specifically implemented as follows: Input the frequency-domain sequences of the state data of multiple control nodes into a frequency-domain encoder based on a wavelet transform-Fourier hybrid model. The processing flow of this encoder is divided into two stages: global frequency-domain decomposition by Fourier transform and local detail extraction by wavelet transform.

[0044] In the Fourier transform stage, the encoder performs a global frequency-domain analysis on the frequency-domain sequence of the state data of each control node (including the composite frequency-domain data of current and magnetic flux). Through the mathematical principle of Fourier transform, the current amplitude, phase, magnetic flux amplitude, and phase at each frequency point in the sequence are decomposed into the superposition of different frequency components, forming a frequency-domain energy distribution map. For example, for the composite frequency-domain data of a certain control node in the frequency band of 10 Hz - 1000 Hz, Fourier transform can identify the energy proportions of the fundamental frequency (such as 50 Hz), harmonics (such as 100 Hz, 150 Hz, etc.), and the phase differences between the current and magnetic flux at each frequency point (reflecting the lag characteristics of electromagnetic coupling). The processing in this stage can capture the distribution characteristics of the state data in the overall frequency-domain range, such as the position of the main energy frequency band, the richness of harmonic components, etc., providing a frequency-domain feature basis from a global perspective for subsequent analysis.

[0045] In the wavelet transform stage, the encoder uses the multi-resolution analysis characteristic of wavelet transform to perform hierarchical processing on the frequency-domain signal after Fourier transform. Specifically, an appropriate wavelet basis function (such as Daubechies wavelet or Symlet wavelet) is selected to perform multi-layer decomposition on the frequency-domain signal (for example, decomposed into 3 layers), dividing the entire frequency-domain range into different sub-bands (such as low-frequency sub-band, intermediate-frequency sub-band, high-frequency sub-band). Each sub-band corresponds to a different frequency interval and can capture the detailed changes of the signal in a specific frequency band. For example, the low-frequency sub-band (10 Hz - 125 Hz) can reflect the fundamental frequency characteristics of the mechanical vibration of the electromagnetic directional valve, the intermediate-frequency sub-band (125 Hz - 500 Hz) corresponds to the dynamic response characteristics of electromagnetic drive, and the high-frequency sub-band (500 Hz - 1000 Hz) may contain high-frequency components such as electronic noise or local magnetic domain changes. Through this hierarchical processing, the encoder can extract detailed characteristics at different scales from the frequency-domain signal, such as the stable trend in the low-frequency band and the transient fluctuations in the middle and high-frequency bands.

[0046] Under the synergistic effect of the hybrid model, the frequency-domain encoder performs cross-physical quantity correlation analysis on the current and magnetic flux data in the frequency-domain sequence of the state data. For example, at the fundamental frequency point, analyze the proportional relationship between the current amplitude and the magnetic flux amplitude (reflecting the electromagnetic conversion efficiency); at the harmonic point, analyze the change in the phase difference between the current and the magnetic flux (reflecting the hysteresis loss characteristics). Through this analysis, the encoder can identify the coupling modes of the current and magnetic flux in the frequency-domain space of different control nodes, as well as the potential correlations between nodes (such as whether there is co-frequency interference or synchronous response between adjacent nodes).

[0047] Finally, the frequency-domain encoder converts the state data frequency-domain sequence of each control node into a frequency-domain correlation feature vector. Each element of this vector corresponds to a specific frequency-domain feature dimension, such as the fundamental frequency current amplitude, fundamental frequency magnetic flux amplitude, fundamental frequency phase difference, proportion of harmonic energy at each order, wavelet subband energy distribution, etc. These feature dimensions numerically characterize the correlation characteristics between current and magnetic flux in the frequency domain of the control node and the implicit correlation between nodes. For example, the frequency-domain correlation feature vector of a certain control node may include the following dimensions: 50Hz current amplitude, 50Hz magnetic flux amplitude, 50Hz current-magnetic flux phase difference, proportion of 100Hz current harmonic energy, proportion of 100Hz magnetic flux harmonic energy, proportion of low-frequency wavelet subband energy, proportion of high-frequency wavelet subband energy, etc.

[0048] It should be noted that the parameters of the frequency-domain encoder (such as the number of wavelet decomposition levels, frequency resolution of the Fourier transform) can be adjusted according to the specific model of the electromagnetic directional valve, the working frequency range, and the control accuracy requirements. For example, for valve components with high requirements for high-frequency response, the frequency resolution of the Fourier transform can be increased and the number of wavelet decomposition levels can be increased to capture more subtle high-frequency features; for valve components mainly characterized by low-frequency mechanical properties, the analysis of the high-frequency band can be simplified and the feature extraction of the low-frequency subband can be focused on.

[0049] Through the above processing based on the wavelet transform-Fourier hybrid model, the frequency-domain encoder realizes multi-level feature extraction of the state data frequency-domain sequence, retains both the macroscopic features of the global frequency-domain distribution and explores the microscopic dynamics of the local frequency band. The generated frequency-domain correlation feature vector can comprehensively and accurately reflect the electromagnetic coupling state of the control node and the implicit correlation between nodes, providing key feature inputs for subsequent wavelet-domain dynamic compensation aggregation and control signal generation.

[0050] Example 4: The process of performing wavelet-domain dynamic compensation aggregation on the frequency-domain correlation feature vectors of multiple control nodes to generate the frequency-domain aggregation characterization matrix of the control node is specifically implemented as follows: Input the frequency-domain correlation feature vectors of multiple control nodes into the wavelet-domain feature extraction network. Based on the multi-scale analysis principle of wavelet transform, this network decomposes each frequency-domain correlation feature vector. By selecting a specific wavelet basis function (such as a wavelet basis with compact support properties), the frequency-domain correlation feature vector is transformed from the original feature space to the wavelet domain, generating a control node frequency-domain wavelet feature matrix. Each row of this matrix corresponds to a control node, and each column corresponds to a scale or frequency subband in the wavelet domain. The matrix element value reflects the feature intensity of the node at the corresponding wavelet scale. For example, if the wavelet domain is divided into 3 scales (low frequency, medium frequency, high frequency), then the frequency-domain wavelet feature matrix of each node contains 3 eigenvalues, corresponding to the energy distribution or detail features at different scales.

[0051] Based on the frequency-domain wavelet feature matrix of the control nodes, perform fuzzy membership modulation on the frequency-domain correlation feature vectors of each control node, which is specifically divided into the following steps: Complementary information calculation: For the frequency-domain correlation feature vector of each control node, calculate its complementary information with the frequency-domain wavelet feature matrix of the control nodes. The complementary information is obtained by comparing the difference between the original eigenvalue of this vector and the eigenvalue of the corresponding node in the wavelet feature matrix, which reflects the personalized features not included in the wavelet feature matrix. For example, if the value of the frequency-domain correlation feature vector of a certain node in the dimension of "fundamental frequency current phase difference" is 0.8, while the value of the "low-frequency scale feature" of the corresponding node in the frequency-domain wavelet feature matrix is 0.6, then the difference value of 0.2 is regarded as the complementary information of this dimension, indicating that this node has uniqueness in the fundamental frequency phase difference beyond the low-frequency features in the wavelet domain.

[0052] Generate the frequency-domain node-wavelet complementary information embedding coding matrix: Organize the complementary information of each node according to the feature dimensions to form the complementary information embedding coding matrix of this node. The dimension of this matrix is the same as that of the frequency-domain correlation feature vector, and each element corresponds to the complementary information value of a feature dimension, thus structurally embedding the complementary information into the feature representation.

[0053] Fuzzy significance identification: Input the frequency-domain node-wavelet complementary information embedding coding matrix of each node into the complementary information significance identification module based on fuzzy rules. There is a preset fuzzy rule set inside the module. For example, set fuzzy linguistic variables such as "high significance", "medium significance", and "low significance" according to the size of the complementary information value. Through fuzzy processing (such as using a triangular membership function), convert the complementary information value into the corresponding fuzzy membership value to generate the fuzzy weight of the frequency-domain complementary information. For example, if the complementary information value of a certain node in the "fundamental frequency current phase difference" is 0.2, according to the preset rules, this value may be mapped to "medium significance", corresponding to a fuzzy weight of 0.6; if the complementary information value is 0.5, it may be mapped to "high significance", corresponding to a weight of 0.9.

[0054] Membership modulation: Use the fuzzy weight of the frequency-domain complementary information to perform weighted processing on the frequency-domain node-wavelet complementary information embedding coding matrix, that is, multiply each matrix element by the corresponding fuzzy weight to generate the fuzzy frequency-domain node-wavelet complementary information embedding coding matrix. Through this operation, the complementary information with high significance is enhanced (weight close to 1), and the complementary information with low significance is suppressed (weight close to 0), thereby highlighting the personalized features that are more critical for node state analysis.

[0055] After the above processing, enter the feature fusion stage: fuse the frequency-domain wavelet feature matrix of the control nodes with the fuzzy frequency-domain node-wavelet complementary information embedding and encoding matrix of multiple control nodes. The fusion method can be matrix splicing or weighted summation. For example, use the frequency-domain wavelet feature matrix as the global feature basis and the fuzzy complementary information matrix of each node as the local feature supplement, and splice them column by column to form a new matrix. The finally generated frequency-domain aggregation representation matrix of the control nodes contains two parts of features: Global frequency-domain wavelet features: reflecting the common features of all control nodes at multiple scales in the wavelet domain, such as the overall energy distribution trend, scale characteristics of the main frequency components, etc.; Personalized fuzzy complementary features: the complementary information modulated by fuzzy weights, representing the unique frequency-domain feature differences of each node, such as abnormal phase differences at specific frequency points, atypical harmonic components, etc.

[0056] The structure of this frequency-domain aggregation representation matrix is: the number of rows is equal to the number of control nodes, and the number of columns is equal to the sum of the global wavelet feature dimension and the personalized complementary feature dimension. For example, if the global wavelet feature matrix contains 3 dimensions (low frequency, medium frequency, high frequency), and the fuzzy complementary information matrix of each node contains 5 dimensions (complementary information corresponding to 5 feature dimensions), then each row of the fused matrix contains 3 + 5 = 8 dimensions, comprehensively covering the common and individual features of the nodes.

[0057] Through wavelet-domain dynamic compensation aggregation, the system can adaptively enhance the personalized difference features while retaining the overall frequency-domain characteristics of the control nodes, avoiding information loss caused by single-feature space analysis. This processing method not only improves the richness of feature representation but also realizes the dynamic evaluation of feature importance through the fuzzy weight mechanism, laying a foundation for accurately extracting the matching features of the target node from multiple nodes subsequently.

[0058] Example 5: The specific implementation method for extracting the first frequency-domain correlation feature vector from the frequency-domain correlation feature vectors of multiple control nodes and generating a control signal is as follows: Extract the first frequency-domain correlation feature vector. According to the node identifier preset in the system (such as the control node number or physical location index), accurately screen out the vector corresponding to the first control node from the set of frequency-domain correlation feature vectors of multiple control nodes, denoted as . This vector contains the current and magnetic flux correlation features of the first control node in the frequency-domain space, such as the values of dimensions such as the fundamental frequency current amplitude, harmonic magnetic flux phase difference, and wavelet sub-band energy ratio, fully characterizing the current working state of this node.

[0059] Calculate the first frequency-domain matching feature matrix. Multiply with the frequency-domain aggregation representation matrix of the control nodes Perform correlation analysis. Frequency domain aggregation characterization matrix is a composite matrix generated by Example 4, which contains the global wavelet features and personalized complementary features of all control nodes, and its dimension is ( is the total number of control nodes, is the total number of feature dimensions). By calculating the covariance matrix between and , the first frequency domain matching feature matrix is generated. The calculation formula of the covariance matrix is: ; where: is the feature vector (row vector) of the th control node in the frequency domain aggregation characterization matrix ; is the mean value of the row vector of; is the mean value of the first frequency domain correlation feature vector ; represents vector transpose.

[0060] Each element of the covariance matrix reflects the th feature dimension of and the th node feature vector in the th feature dimension. A positive value indicates a positive correlation (e.g., the first node and the node have the same trend in this dimension), a negative value indicates a negative correlation (opposite trend), and the absolute value reflects the correlation strength. Through this matrix, the matching degree between the first control node and other nodes in the frequency domain feature space can be quantified, forming a structured matching feature representation.

[0061] Generate a control signal. Input the first frequency domain matching feature matrix into the control signal generator based on fuzzy inference. The generator internally contains a fuzzy rule base, which is constructed based on the control logic of the electromagnetic directional valve. For example: if the covariance value in a certain dimension is "significantly positive", then adjust the duty cycle of the pulse width modulation (PWM) signal of the driving circuit of the first control node accordingly to enhance the feature in this dimension; if the covariance value is "significantly negative", then adjust the duty cycle in the opposite direction to suppress the difference.

[0062] The generator processes the matrix data through the following steps: Fuzzification: Map the numerical values in the covariance matrix to a preset fuzzy language variable interval (such as "extremely high", "high", "medium", "low", "extremely low"), and each interval corresponds to a specific membership function (such as a trapezoidal function).

[0063] Fuzzy inference: According to the fuzzy rule base, the fuzzy inputs of each dimension are logically combined to derive the fuzzy output for the PWM correction value (such as "increase", "slightly increase", "remain", "slightly decrease", "decrease").

[0064] Defuzzification: The fuzzy output is converted into an exact numerical value, i.e., the pulse width modulation correction value of the first control node, by methods such as the centroid method. This correction value is used for parameter calibration of the drive circuit, such as adjusting the high-level duration of the PWM signal, thereby changing the drive energy of the control node and realizing real-time optimization of the response characteristics of the electromagnetic directional valve.

[0065] The above process quantifies the frequency-domain feature matching degree between nodes through covariance analysis, and combines fuzzy inference to achieve a non-linear mapping from feature differences to control signals, ensuring precise calibration of the drive circuit of the first control node and forming a complete link from state data acquisition to closed-loop control.

[0066] The above process realizes the closed-loop analysis from the original state data to the control signal through multi-layer processing of data fusion, feature extraction, fuzzy modulation, matching calculation and fuzzy inference, ensuring the scientificity and accuracy of the parameter calibration of the drive circuit of the first control node and forming a complete multi-dimensional correlation analysis and control signal generation link.

[0067] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0068] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for processing remote control data of an electromagnetic directional valve, characterized in that Including: Receiving a commutation control instruction sent by a remote terminal; Parsing the commutation control instruction to obtain adjustment parameters of an electromagnetic commutation valve, where the adjustment parameters include the number of actions, switching direction, and pressure threshold; Determining dynamic response parameters of multiple control nodes of the electromagnetic commutation valve based on the adjustment parameters; Performing real-time adjustment actions based on the dynamic response parameters of the multiple control nodes, and synchronously collecting status data of the multiple control nodes, where the status data includes current data and magnetic flux data; Uploading the status data of the multiple control nodes to an edge computing unit; In the edge computing unit, performing multi-dimensional correlation analysis on the status data of the multiple control nodes to generate a control signal, where the control signal is a pulse width modulation correction value of a first control node; Transmitting the control signal to a fuzzy controller to calibrate parameters of a drive circuit corresponding to the first control node.

2. The electromagnetic directional valve remote control data processing method according to claim 1, characterized in that In the edge computing unit, performing multi-dimensional correlation analysis on the status data of the multiple control nodes to generate a control signal, where the control signal is a pulse width modulation correction value of a first control node, including: Performing data fusion on the status data of the multiple control nodes to generate a frequency domain sequence of the status data of the multiple control nodes; Performing frequency domain implicit correlation analysis on the frequency domain sequence of the status data of the multiple control nodes to generate frequency domain correlation feature vectors of the multiple control nodes; Performing wavelet domain dynamic compensation aggregation on the frequency domain correlation feature vectors of the multiple control nodes to generate a frequency domain aggregation representation matrix of the control nodes; Extracting a first frequency domain correlation feature vector from the frequency domain correlation feature vectors of the multiple control nodes, and calculating a matching degree of the first frequency domain correlation feature vector with respect to the frequency domain aggregation representation matrix of the control nodes to generate a first frequency domain matching feature matrix; Generating the control signal based on the first frequency domain matching feature matrix.

3. The electromagnetic directional valve remote control data processing method according to claim 2, characterized in that Performing data fusion on the status data of the multiple control nodes to generate a frequency domain sequence of the status data of the multiple control nodes, including: Performing data reduction on the status data of the multiple control nodes according to the frequency domain dimension and adjustment parameter sample dimension to generate a frequency domain sequence of current data of the multiple control nodes and a frequency domain sequence of magnetic flux data of the multiple control nodes; Adding corresponding magnetic flux data after each current data in the frequency domain sequence of current data of the multiple control nodes to generate the frequency domain sequence of the status data of the multiple control nodes.

4. The electromagnetic directional valve remote control data processing method according to claim 3, characterized in that, Performing frequency domain implicit correlation analysis on the frequency domain sequence of the status data of the multiple control nodes to generate frequency domain correlation feature vectors of the multiple control nodes, including: inputting the frequency domain sequence of the status data of the multiple control nodes into a frequency domain encoder based on a wavelet transform-Fourier hybrid model to generate the frequency domain correlation feature vectors of the multiple control nodes.

5. The electromagnetic directional valve remote control data processing method according to claim 4, wherein Performing wavelet domain dynamic compensation aggregation on the frequency domain correlation feature vectors of the multiple control nodes to generate a frequency domain aggregation representation matrix of the control nodes, including: Inputting the frequency domain correlation feature vectors of the multiple control nodes into a wavelet domain feature extraction network to generate a frequency domain wavelet feature matrix of the control nodes; Based on the frequency-domain wavelet feature matrix of the control nodes, perform fuzzy membership modulation on the frequency-domain correlation feature vectors of each control node among the frequency-domain correlation feature vectors of the multiple control nodes to generate a fuzzy frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes; Fuse the frequency-domain wavelet feature matrix of the control nodes and the fuzzy frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes to generate the frequency-domain aggregation representation matrix of the control nodes.

6. The electromagnetic directional valve remote control data processing method according to claim 5, characterized in that Based on the frequency-domain wavelet feature matrix of the control nodes, performing fuzzy membership modulation on the frequency-domain correlation feature vectors of each control node among the frequency-domain correlation feature vectors of the multiple control nodes to generate a fuzzy frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes includes: Calculate the complementary information of the frequency-domain correlation feature vector of each control node among the frequency-domain correlation feature vectors of the multiple control nodes with respect to the frequency-domain wavelet feature matrix of the control nodes to generate a frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes; Input the frequency-domain node-wavelet complementary information embedding coding matrix of each control node in the frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes into a complementary information saliency identification module based on fuzzy rules to generate the frequency-domain complementary information fuzzy weights of the multiple control nodes; Based on the frequency-domain complementary information fuzzy weights of the multiple control nodes, perform membership modulation on the frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes to generate the fuzzy frequency-domain node-wavelet complementary information embedding coding matrix of the multiple control nodes.

7. The electromagnetic directional valve remote control data processing method according to claim 6, wherein Extract a first frequency-domain correlation feature vector from the frequency-domain correlation feature vectors of the multiple control nodes, and calculate the matching degree of the first frequency-domain correlation feature vector with respect to the frequency-domain aggregation representation matrix of the control nodes to generate a first frequency-domain matching feature matrix, including: Extract the frequency-domain correlation feature vector corresponding to the first control node from the frequency-domain correlation feature vectors of the multiple control nodes to generate the first frequency-domain correlation feature vector; Calculate the covariance matrix between the first frequency-domain correlation feature vector and the frequency-domain aggregation representation matrix of the control nodes to generate the first frequency-domain matching feature matrix.

8. The electromagnetic directional valve remote control data processing method according to claim 7, characterized in that, Based on the first frequency-domain matching feature matrix, generate the control signal, including: passing the first frequency-domain matching feature matrix through a control signal generator based on fuzzy inference to generate the control signal.

9. A remote control system for an electromagnetic directional control valve, characterized in that, Including: A control instruction receiving module, configured to receive a commutation control instruction sent by a remote terminal; A control instruction parsing module, configured to parse the commutation control instruction to obtain the adjustment parameters of the electromagnetic commutation valve, where the adjustment parameters include the number of actions, the switching direction, and the pressure threshold; A dynamic response parameter determination module, configured to determine the dynamic response parameters of multiple control nodes of the electromagnetic commutation valve based on the adjustment parameters; A state data acquisition module, configured to perform real-time adjustment actions based on the dynamic response parameters of the multiple control nodes and synchronously acquire the state data of the multiple control nodes, where the state data includes current data and magnetic flux data; A status data upload module, configured to upload the status data of the multiple control nodes to the edge computing unit; A status data analysis module, configured to perform multi-dimensional correlation analysis on the status data of the multiple control nodes in the edge computing unit to generate a control signal, where the control signal is a pulse width modulation correction value of a first control node; A control signal execution module, configured to transmit the control signal to a fuzzy controller to calibrate parameters of a drive circuit corresponding to the first control node.

10. The remote control system for electromagnetic directional valve according to claim 9, characterized in that, The status data analysis module includes: A status data fusion unit, configured to perform data fusion on the status data of the multiple control nodes to generate a status data frequency domain sequence of the multiple control nodes; A frequency domain correlation analysis unit, configured to perform frequency domain implicit correlation analysis on the status data frequency domain sequence of the multiple control nodes to generate a frequency domain correlation feature vector of the multiple control nodes; A wavelet domain compensation aggregation unit, configured to perform wavelet domain dynamic compensation aggregation on the frequency domain correlation feature vectors of the multiple control nodes to generate a frequency domain aggregation characterization matrix of the control nodes; A matching feature calculation unit, configured to extract a first frequency domain correlation feature vector from the frequency domain correlation feature vectors of the multiple control nodes, and calculate a matching degree of the first frequency domain correlation feature vector relative to the frequency domain aggregation characterization matrix of the control nodes to generate a first frequency domain matching feature matrix; A control signal generation unit, configured to generate the control signal based on the first frequency domain matching feature matrix.

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