134 results about "Signal perception" patented technology

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention discloses wearable equipment with body temperature measurement and body temperature early warning functions. The wearable equipment comprises a signal sensing and collecting module, a metabolism dynamic estimation module, a thermal dynamic state deduction module, an autonomic nerve stress analysis module, a multi-mode risk assessment module, a graded early warning and intervention execution module and a closed-loop optimization and self-adaptive calibration module. Compared with the prior art, the method has the following advantages and effects that through fusion of multi-modal physiological, environmental and behavior data and based on dynamic metabolism estimation, thermal dynamics deduction and autonomic nerve stress analysis, accurate and foresight evaluation and personalized grading early warning of the heatstroke risk of the user are achieved, and the early warning accuracy and intervention effectiveness are remarkably improved.

The invention discloses an intelligent hydraulic system of a roller press and a control method. The method comprises the steps that roll gap and pressure signals are collected in real time, and the average roll gap, the roll gap difference and the change rate are calculated; calculating an optimal system total pressure setting reference value in real time through a preset pressure-roll gap-material characteristic coupling model based on the target peak pressure, the current average roll gap and the material characteristic parameters; judging the working condition according to the roll gap difference and the change rate: under the stable working condition, taking the reference value as a double-side pressure set value; under the abnormal working condition, differential adjustment is carried out on double-side pressure set values on the basis of the abnormal working condition; and finally, the pressure set value is output to the hydraulic executing mechanism, and the state of the energy accumulator set is synchronously adjusted. The system correspondingly comprises a signal sensing unit, a hydraulic execution unit, an adjustable rigidity energy storage unit and an intelligent control unit. Constant-effect, stable and self-adaptive operation of the roller press under variable working conditions is achieved, and the grinding efficiency, the equipment stability and the intelligent level are remarkably improved.

The invention provides a non-circular signal sensing method based on a weighted coherence matrix, which belongs to the technical field of wireless communication, and comprises the following steps: acquiring a complex value receiving signal, and calculating a real value augmented sample covariance matrix; based on the real value augmented sample covariance matrix, constructing a real value sample coherence matrix, and calculating a corresponding weight coefficient; constructing test statistics based on the weighted sum of squares of the real value sample coherence matrix and the weight coefficient; and calculating a sensing threshold based on the false alarm probability, comparing the sensing threshold with the test statistics, and determining a spectrum sensing result. According to the method, the second-order statistical property of the non-circular signal is described by using the real-value augmented sample covariance matrix, and weighted square fusion is performed on the real-value sample coherence matrix in combination with the weight coefficient driven by the sample data, so that better sensing performance can be obtained.

The invention relates to the technical field of military simulation and training, discloses a training system for analog signal combat and a training method thereof, and aims to solve the problems of insufficient confrontation authenticity, strategy solidification, cognitive depth deficiency and weak multi-domain cooperative capability of a traditional training system. According to the method, a dynamic confrontation deduction architecture with linkage of a physical domain, an electromagnetic domain and a cognitive domain is constructed, a distributed radio frequency array, a programmable attenuator and a multipath reflection simulator are deployed to realize high-fidelity signal propagation simulation, and full-band signal perception and feature extraction are completed in combination with a broadband digital receiver cluster and a central data fusion platform. An intelligent confrontation agent cluster driven by deep reinforcement learning and a cross-domain collaborative decision center are introduced; according to the technical scheme, the authenticity of the training environment, the intelligent adaptability of confrontation, the objectivity of evaluation and the sustainable evolution ability of the system are remarkably improved, and the signal game, dynamic response and cooperative command ability of trainees are effectively hammered.

The invention provides a fast charger based on deep learning and a fast charging multi-protocol adaptive method thereof, and the fast charger comprises a signal sensing module which is used for carrying out the multi-mode sensing of an original signal of an access device, and obtaining a sensing vector; the perception decision module is used for inputting the perception vector into a lightweight deep learning model and outputting a perception decision, and the perception decision comprises protocol probability distribution, recommended power gears and safety suggestions; and the protocol matching module is used for carrying out self-adaptive matching of the fast charging protocol according to the perception decision. According to the fast charger based on deep learning and the fast charging multi-protocol adaptive method thereof, the lightweight deep learning model is adopted to adaptively output the protocol probability distribution, the method does not depend on a preset rule base and a sequence attempt mechanism, the protocol matching efficiency is higher, and in addition, the fast charging efficiency is improved. Decision factors such as recommended power gears and safety suggestions are introduced to form constraint conditions, the charging speed and the battery safety are balanced, and the suitability is better.

The invention belongs to the related technical field of sensors, and provides a multi-parameter integrated acquisition optical fiber sensor and a monitoring system thereof in order to solve the problem that high-precision monitoring of a static signal and a dynamic signal is difficult to consider by an existing sensor at the same time, a sensing cavity is sequentially divided into a static signal sensing area and a dynamic signal sensing area in the axial direction; the static signal sensing area adopts a multi-core fiber, fiber gratings are respectively engraved on the multi-core fiber, and the wavelength demodulation of the static signal is realized through the central wavelength drift distance of the fiber gratings; the dynamic signal sensing area is provided with a dynamic signal sensing unit, the dynamic signal sensing unit comprises F-P air microcavities formed between the dynamic signal sensing unit and the end faces of all fiber cores of the multi-core optical fiber, and intensity demodulation of dynamic signals is achieved through cavity length changes of the F-P air microcavities. According to the invention, a wavelength modulation and intensity modulation parallel composite demodulation mechanism is adopted, and decoupling acquisition of static strain, temperature and dynamic vibration is realized in a single sensor.

The invention relates to a measurement and control signal intelligent sensing method based on a deep neural network, and belongs to the technical field of radio signal processing and artificial intelligence crossing. According to the method, a time-frequency graph data set of broadband measurement and control signal simulation and actual measurement data is firstly constructed, and then a target detection model is trained based on the mixed simulation and actual measurement time-frequency graph data set. In the reasoning stage, based on a bounding box obtained by a target detection model, the confidence coefficient of sub-signals and time-frequency parameters such as the signal starting moment, the duration, the bandwidth and the center frequency in original record data can be obtained, and intelligent sensing of broadband measurement and control signals is achieved. According to the method, the time-frequency sub-signal bounding boxes which are not overlapped with each other are randomly generated, and the bandwidth, the center frequency and the time domain position of the sub-signals are reversely controlled, so that the generation of the time-frequency graph target labeling information is more accurate and efficient.

The invention provides a bearing load real-time monitoring and hydraulic pressure self-adaptive adjusting system and method, and the system comprises a signal sensing unit which is used for collecting a bearing vibration signal, a hydraulic system pressure signal and a main shaft rotating speed signal; and the signal processing and calculating unit is connected with the signal sensing unit and is used for processing the collected signals, estimating the bearing load based on a pre-trained soft measurement model and generating a hydraulic control instruction. The dependence of traditional bearing load monitoring on an expensive force sensor is broken through, and by integrating operation signals such as vibration, hydraulic pressure and rotating speed which are easy to obtain, the bearing load monitoring precision is improved. According to the method, the incidence relation between signals and loads is established through off-line modeling, non-intrusive load real-time estimation is achieved, meanwhile, feed-forward and feedback composite control is adopted, hydraulic pressure is adjusted in a self-adaptive mode according to the estimated loads, traditional empirical fixed setting is replaced, monitoring, adjusting, early warning and maintenance prompting functions are integrated, and real-time monitoring is achieved. The technical problems that a traditional scheme is high in cost, blind in adjustment and dispersed in function are effectively solved.

The invention relates to the technical field of health data processing, in particular to a handlebar-based health data real-time analysis and prompt system, which comprises a physiological signal sensing module, a trend feature extraction module, a state level identification module, a prompt scheme selection module and a multimode channel prompt module. According to the method, a multi-source signal sequence is constructed for trend characteristic analysis through synchronous acquisition and time window alignment of four types of signals including pulse beat cycle, grip rate, blood oxygen decreasing time period and contact area frequency, the health state level is judged according to a trend cross-coincidence relation, and a corresponding prompt scheme is matched; through cooperation of the signal change trend, real-time perception and grade classification of the physiological abnormal state of the rider are realized, the pertinence and effectiveness of prompt response are improved, the precision and stability of a continuous prompt intervention mechanism are enhanced, and the problems of potential health risk identification delay and lack of personalized regulation and control of prompt are effectively solved.

The invention relates to an intelligent monitoring system and method for deformation of a long tunnel, and the system comprises a multi-dimensional optical fiber sensing network, a data collection and demodulation unit, a data processing and intelligent analysis center, and a safety early warning module, and the multi-dimensional optical fiber sensing network comprises a distributed linear optical fiber sensing system, an optical fiber grating sensing element, and an optical fiber inertia measurement unit. The distributed linear optical fiber sensing system is used for tunnel longitudinal strain distribution measurement and vibration and acoustic signal sensing; the fiber bragg grating sensing element is used for monitoring circumferential strain and longitudinal strain of a key point and longitudinal differential settlement of a tunnel; the optical fiber inertial measurement unit measures overall axis deflection and torsional deformation; the data acquisition and demodulation unit is used for acquiring and demodulating the sensing signal; the data processing and intelligent analysis center is used for deformation category identification, trend prediction and graded early warning; and the safety early warning module sends early warning information to operation management personnel. According to the invention, full-section, high-precision and real-time online monitoring and intelligent early warning of the long tunnel can be realized.

The invention relates to the technical field of intelligent cabins, in particular to a vehicle-mounted active noise reduction and variable privacy glass integrated regulation and control system and method.According to the system, vehicle environment and state signals are collected through a signal sensing module, fusion analysis is conducted through a cooperative control module, and a current operation scene is judged based on a safety priority rule; and a cooperative control instruction is generated to drive the integrated execution module. The execution module integrates active noise reduction, glass dimming and partition acoustic isolation functions, and intelligent linkage of optical regulation and control, acoustic noise reduction and voice privacy can be achieved in different scenes such as parking, high speed and VIP. According to the method, the problem that all functions run independently and cannot respond cooperatively in the prior art is solved, and quietness, privacy and overall comfort in the vehicle are remarkably improved.

This invention provides a method and apparatus for identifying raw wireless signal targets based on model fusion decision-making, relating to the field of intelligent signal sensing technology. The method involves inputting raw wireless signal data into the signal sensing and preprocessing layer of a signal target identification model to obtain dimensional representation information output by the signal sensing and preprocessing layer. Each signal representation data in the dimensional representation information is then input into the parallel detection and feature extraction layer of the signal target identification model to obtain structured detection evidence corresponding to each signal representation data output by the parallel detection and feature extraction layer. All structured detection evidence is then input into the intelligent fusion and decision-making layer of the signal target identification model to obtain the fusion decision result output by the intelligent fusion and decision-making layer. Finally, the fusion decision result is input into the result output layer of the signal target identification model to obtain the target identification result of the raw wireless signal data output by the result output layer. The technical solution of this invention improves the accuracy of signal identification.

The present application relates to the technical field of medical monitoring and nursing, in particular to a kind of anesthesia perioperative patient emotion real-time monitoring and active pacification system, including multidimensional biological signal perception module, space-time associated emotion prediction engine, neural feedback type active pacification module, surgical event linkage unit and medical intelligent decision terminal.Multidimensional biological signal perception module fuses electromyogram subconscious emotion capture, saliva cortisol microfluidic rapid detection and non-contact physiological monitoring technology, realize the synchronous acquisition of subjective emotion and objective biomarker;Space-time associated emotion prediction engine is based on the time sequence characteristics of surgical event and patient historical data, advance prediction emotional fluctuation risk;Neural feedback type active pacification module is through transdermal electrical stimulation and multi-sensory collaborative output, forms emotion regulation closed loop;Surgical event linkage unit and operating room equipment real-time interaction, automatically trigger pre-pacification mechanism at key operation node.

The embodiment of the invention discloses a signal transmission method, a communication device and a storage medium, which are used for transmitting and receiving a sensing signal and determining information of a sensing target. The method comprises the following steps: determining M1 phases and / or M2 frequencies of a first sensing signal, wherein the first sensing signal is used for sensing a sensing target; and sending the first sensing signal. In the embodiment of the invention, in the sensing process, the sensing signal with obvious amplitude, signal intensity and / or phase change always exists, so that the M1 phases and / or M2 frequencies of the sensing signal are / is determined, the sensing result can be used for better receiving the signal to sense the target motion in the subsequent signal processing, and the sensing precision and reliability are improved.

A system, computer program product, and method are provided for probing model signal awareness. An iterative process is employed to systematically isolate one or more relevant tokens of an input sequence to generate a reduced input sequence. The reduced input sequence is validated and presented to a trained artificial intelligence (AI) model and prediction output is generated. The reduction process is continued while the prediction output stays the same as that of the input sequence, and until a minimal sub-sequence is identified. A signal existence in the minimal sub-sequence is verified and signal awareness of the trained AI model is evaluated. The evaluation includes measuring the verified signal existence against an original signal from the input sentence.

The invention relates to the technical field of nondestructive testing of low-carbon steel, and particularly discloses a thermal double-sensitive composite coating as well as a preparation method and application thereof. The thermal double-sensitive composite coating comprises a stress sensitive fluorescent layer and a thermochromic layer, the stress sensitive fluorescent layer adopts core-shell hybrid particles with a force-induced fluorescence enhancement effect as key functional filler, and the thermochromic layer adopts an IPN-based irreversible thermochromic material as a core functional material. The stress-sensitive fluorescent layer and the thermochromic layer are prepared from novel self-made functional materials, so that the thermal double-sensitive composite coating has the functions of high-sensitivity signal sensing, optical enhancement and permanent recording, and is assisted by a load excitation and digital image analysis process; stress-temperature coupling field information excited by the deep defect under the load is converted into a visual signal which is high in strength, high in anti-interference performance and capable of being recorded permanently, and accurate positioning, quantification and classification of the defect are achieved.

The invention belongs to the technical field of director, and particularly relates to an AI director assistant method and system capable of implementing an intelligent interaction effect. The method comprises the following steps: synchronously accessing multiple paths of audio and video signals and carrying out space-time alignment to generate a synchronized audio and video stream set and space mapping parameters; and carrying out hierarchical analysis and semantic feature extraction on the audio-visual content to form an individual behavior portrait. And identifying a social function role and predicting a behavior intention based on the individual behavior portrait. A scene significance scoring system is constructed according to social function roles and behavior intentions, and a key scene with a propagation value is automatically identified. And finally, candidate lens schemes are generated and sorted based on the key scene, and lens switching and dynamic composition adjustment are executed. According to the method, automatic closed loop from signal perception to behavior understanding to picture output is realized, the event response time is remarkably shortened, the accuracy of wonderful instant capture and the continuity of picture presentation are improved, and the problems of response delay and one-sided judgment existing in manual director are effectively solved.

This invention provides a non-circular signal sensing method based on a weighted coherence matrix, belonging to the field of wireless communication technology. The method includes: acquiring a complex-valued received signal and calculating a real-valued augmented sample covariance matrix; constructing a real-valued sample coherence matrix based on the real-valued augmented sample covariance matrix and calculating corresponding weighting coefficients; constructing a test statistic based on the weighted sum of squares of the real-valued sample coherence matrix and the weighting coefficients; calculating a sensing threshold based on the false alarm probability and comparing the sensing threshold with the test statistic to determine the result of the spectrum sensing. This invention utilizes the real-valued augmented sample covariance matrix to describe the second-order statistical properties of non-circular signals and combines sample data-driven weighting coefficients to perform weighted square fusion of the real-valued sample coherence matrix, achieving superior sensing performance.

A memory access conflict detection method based on cloud service, a medium, a device and a program product relate to the technical field of computers, and acquire a first count value of a performance monitoring counter of each physical central processing unit core according to a binding relationship between a virtual central processing unit and the physical central processing unit core and the first count value. According to the embodiment of the invention, the second count value representing the number of times that the virtual machine has access to the memory scrambling event is determined, the first count value corresponding to the global access to the memory scrambling event can be acquired by reading the performance monitoring counter, and the physical access to the memory scrambling event can be accurately associated to the specific virtual machine. According to the method, hardware signal sensing and virtual machine level memory access scrambling event detection are realized, non-intrusive memory access scrambling event detection can be realized, and accurate data support can be provided for inhibition of subsequent memory access scrambling events and / or display of the trend of the memory access scrambling events of the virtual machine.

The invention provides an electric power circuit fault point positioning method and device based on voiceprint recognition, and relates to the technical field of electric power circuit detection, and the method comprises the steps: laying a voiceprint sensor for collecting a sound signal; preprocessing the first sound signal at the first position and then decomposing the first sound signal to obtain a first sound spectrum; traversing the first sound spectrum in a power line fault sound spectrum database to obtain a first most matched fault type corresponding to the first most matched sound spectrum; and when a first matching coefficient of the two positions accords with a preset matching threshold value, a first fault early warning instruction is sent, and fault early warning is carried out on the first position. The technical problem that the fault point positioning precision and the fault type identification accuracy are low due to the fact that the prior art depends on electrical signal perception and is easily influenced by electromagnetic interference is solved, and the technical effects that the fault positioning precision and the fault type identification capability are improved, and the fault early warning accuracy is remarkably improved are achieved.

The application provides a clamping prevention folding furniture based on wave signal sensing, a clamping prevention folding method and related device. The clamping prevention folding furniture based on wave signal sensing comprises a furniture fixing part, a furniture folding part, a processor, a folding driving mechanism and a clamping prevention detection device. The application uses wave signal detection technology for double inspection. Once it is detected that there is a space occupying object in the folding space during the folding process of the furniture folding part, the folding action of the furniture folding part is directly stopped to avoid the situation of injury caused by delay and complex judgment, and the safety of the furniture is improved. After stopping, the intrusion state of the space occupying object is specifically judged according to the reflection parameters of the reflection signals of the wave signals, the motion state of the furniture folding part is flexibly controlled according to the intrusion state, the false triggering of the stoppage is reduced, the safety and the operation convenience are balanced, and the accuracy of the clamping prevention control is further improved.

The invention discloses a fire-fighting equipment identification method based on tactile array signal perception. The method comprises the following steps of: acquiring hand tactile array signals when different fire-fighting equipment is held by using independently researched and developed flexible tactile gloves, constructing the hand tactile array signals into gray images, and taking the gray images as a data set; a deep learning method is used for identifying the corresponding fire-fighting equipment from the tactile array signals, so that the environmental limitation that an industrial site is easily shielded is overcome, and the fire-fighting equipment is identified by collecting the tactile array signals which are not shielded. And meanwhile, a traditional camera is not needed, so that the cost of industrial field fire safety detection is reduced. According to the method, the fire-fighting equipment is identified by using the tactile array signal, and the method not only obtains a more reliable result than other traditional camera-based identification methods, but also is a first attempt of the tactile array signal in the field of fire-fighting equipment identification.

This invention belongs to the field of nasal alar pulse wave signal processing technology, and provides a method, apparatus, medium, and wearable device for nasal alar pulse wave signal processing. The method includes: extracting the respiratory cycle based on the respiratory signal; determining the length of the mean sliding filter window to filter out the DC component of the original nasal alar pulse wave signal, obtaining a filtered nasal alar pulse wave signal; extracting the amplitude of the AC component of the filtered nasal alar pulse wave signal; adjusting the current of the light source element corresponding to the pulse wave signal sensing element to make the amplitude of the AC component of the filtered nasal alar pulse wave signal greater than or equal to a preset amplitude threshold; further filtering the nasal alar pulse wave signal using an adaptive weighted moving average filtering algorithm; and determining whether a nasal alar pulse wave signal meeting preset quality requirements is obtained based on a comparison between the noise figure of the further filtered nasal alar pulse wave signal and a preset noise figure threshold.

Disclosed in embodiments of the present application are a signal transmission method, a communication apparatus, and a storage medium, for use in implementing the transmission and reception of a sensing signal and determining information of a sensing target. The method comprises: determining M1 phases and / or M2 frequencies of a first sensing signal, the first sensing signal being used for sensing a sensing target; and sending the first sensing signal. In the embodiments of the present application, during a sensing process, there are always sensing signals with significant changes in amplitude, signal strength, and / or phase. Therefore, by determining M1 phases and / or M2 frequencies of each sensing signal, a received signal having a better sensing result can be used in subsequent signal processing to sense the motion of a target, thereby improving the accuracy and reliability of sensing.

The invention discloses an electronic control method of a medical laser positioning system, which comprises the following steps: a signal acquisition and conversion step: acquiring an optical signal reflected by positioning laser on the body surface of a patient and modulated by respiratory movement, and converting the optical signal into a corresponding sensing electric signal; phase identification: processing the sensing electric signal to identify whether the current respiratory cycle is in a preset stable phase or not; a laser gating step of controlling the output mode of the positioning laser according to the identification result of S2; when the positioning laser is identified to be in the preset stable phase, the positioning laser is controlled to work in a first output mode, and the laser positioning system is upgraded from a passive projection tool to an active system with the physiological signal sensing capacity. By extracting the breathing phase in real time and accurately controlling the laser output mode according to the breathing phase, the system can automatically provide a clear positioning mark at the most stable moment in the breathing cycle.

According to the distance measuring sensor and the production system of the distance measuring sensor provided by the embodiment of the invention, the movable receiving lens base comprising the signal receiving lens is arranged on the shell of the distance measuring sensor, and the signal receiving lens is used for refracting the laser reflection signal to the light sensing surface of the signal sensing processing chip; photosensitive information is generated based on the reflected laser signal, and the upper computer generates mirror base adjustment data by sending the photosensitive information to the upper computer, so that automatic receiving and transmitting alignment of the distance measuring sensor can be realized, an echo light spot can be adjusted to a proper position of a photosensitive surface, and the adjustment direction of the movable receiving mirror base can be determined based on the photosensitive information without blind adjustment. In the calibration process, only the posture of the movable receiving mirror base needs to be adjusted, the position of the whole receiving circuit board does not need to be moved, the calibration efficiency is high, and therefore the production speed can be increased, and the cost of the whole machine can be reduced.

The embodiments of the present application relate to the technical field of communications. Provided are a communication method and a communication device. The method can avoid link cross-interference between sensing communication and service communication. The method should comprise: sending a first message, wherein the first message comprises a sensing measurement parameter, and the sensing measurement parameter is determined on the basis of a configured resource for a first task and / or a first sensing task; sending a sensing measurement signal, wherein the sensing measurement signal is used by a first terminal device to perform measurement on the basis of the sensing measurement parameter; receiving a second message, wherein the second message comprises a first measurement report, and the first measurement report is generated by the first terminal device when a measurement result meets a condition corresponding to at least one threshold value indicated by the sensing measurement parameter; and on the basis of the second message, adjusting a resource for performing the first task with the first terminal device, or, instructing a second device to adjust a resource for the sensing task corresponding to the sending of a first signal.

The invention discloses an insulation degradation positioning device of a cable terminal and a control method, and relates to the technical field of cable terminal insulation detection and fault positioning. A signal sensing unit adopts a single built-in ultrahigh-frequency sensor to capture a high-frequency harmonic signal generated at the initial stage of insulation aging of the cable terminal to be detected; the signal acquisition and processing unit pre-processes the signal, then the pre-processed signal is injected into a preset cable terminal digital twin electromagnetic model by means of the digital twin and inversion analysis and calculation unit to carry out electromagnetic wave back propagation simulation, and total-space electromagnetic field distribution data is obtained based on simulation. The space coordinate of the insulation degradation point is determined through the preset positioning criterion, the positioning precision equivalent to that of a traditional multi-sensor system is achieved through a single-sensor scheme, the cost and the deployment difficulty are remarkably reduced, deep integration with a digital twin platform is achieved, and efficient and accurate technical support is provided for intelligent operation and maintenance of the cable terminal.

This invention discloses a safety processing algorithm and system for dual-Hall collaborative verification position sensing of an electric wheelchair joystick, relating to the field of electromechanical control technology. The algorithm includes acquiring a first output signal from a triaxial Hall sensor and a second output signal from a linear Hall sensor, and performing dynamic baseline calibration to obtain two anti-drift position signals. Co-occurrence difference calculation and consistency determination are performed on the two anti-drift position signals to obtain a sensor cluster health status value. Signal fusion and fault reconstruction are performed based on the sensor cluster health status value and the two anti-drift position signals to obtain a fused position command. A confidence-based safety joystick command is generated based on the fused position command and the sensor cluster health status value, and simultaneously sent to the microcontroller module. This provides complete information, including reliability assessment, for subsequent control decisions, thereby constructing a full-link safety protection system from signal perception to control decision-making.