1301 results about "Current sensor" patented technology

The invention provides a transformer substation residual current intelligent monitoring method and device based on multi-parameter fusion, and relates to the technical field of power system monitoring, and the method comprises the steps: collecting the residual current value of each loop through a residual current sensor, and collecting environment parameters at the same time; performing dynamic confidence fusion on the residual current values of the plurality of residual current sensors through a confidence function and the consistency factor to obtain a fused residual current value; calculating self-adaptive weights of the environmental parameters, and calculating a dynamic threshold value through the nonlinear mapping function of the environmental parameters and the self-adaptive weights; calculating a multi-parameter fusion evaluation index, combining the fusion residual current value, the dynamic threshold value and the multi-parameter fusion evaluation index, calculating a comprehensive fault risk index through a fault membership function, and obtaining a fault diagnosis result according to the risk index value; and outputting early warning information according to the fault diagnosis result. According to the invention, intelligent monitoring and fault early warning of the residual current of the transformer substation can be realized, and the monitoring precision and the reliability of fault diagnosis are improved.

Disclosed are an online fault identification method for a current sensor, a diagnosis apparatus, and a fault-tolerant control system, relating to the technical field of diagnosis of faults of current sensors. The method includes defining a fault of the current sensor, and setting a corresponding label; then simulating a motor drive system, so as to obtain operation data in a normal mode and a fault mode, and making a data set; designing a neural network, and performing optimization processing on the neural network through the data set, so as to obtain an intelligent diagnosis model; and finally deploying the intelligent diagnosis model in an edge apparatus, so as to perform real-time online diagnosis on the fault of the current sensor; where the fault of the current sensor includes a saturation fault and a noise fault.

The invention provides a current sensor. The current sensor adopts a magnetic-core-free design, and adopts a diversion row group with a special structure to guide a target current so as to enhance the electrifying capability of the current sensor and reduce the adverse effect of a high-frequency current skin effect on measurement. Based on the structural characteristics of the diversion row group, the magnetic sensitive component for measuring the target current and the corresponding sensing signal processing method are arranged in a matched manner, so that the measurement precision of high-frequency large current is improved, the suppression capability of an interference magnetic field is improved, and the signal-to-noise ratio of an effective measurement signal is increased. The current sensor provided by the invention is compact in structure, simple in manufacturing process, strong in electrifying capability and high in signal-to-noise ratio of a sensing signal, not only widens the measuring range of the current sensor, but also effectively improves the measurement precision of high-frequency large current, and expands the application range of the magnetic-core-free current sensor.

The invention provides a vehicle driving state estimation method and system in a centralized driving mode, and relates to the technical field of electric automobiles, and the method comprises the following steps: determining wheel dynamic parameters based on sensor detection information and a seven-degree-of-freedom dynamic model related to wheels, and combining a preset Dugoff tire model to obtain initial tire force parameters; a vehicle-mounted infrared camera is used for obtaining road surface image information, the road surface image information is analyzed through a deep convolutional neural network based on semantic segmentation and a vehicle dynamics inversion method, an adaptive fusion mechanism is introduced for adjustment, and a target road surface adhesion coefficient is determined; correcting the initial tire force parameter by using the target road adhesion coefficient to obtain a target tire force parameter; inputting the current sensor detection information and the target tire force parameter into a self-adaptive unscented Kalman filter to obtain vehicle driving state information; the vehicle driving state information comprises the longitudinal speed, the transverse speed, the yaw velocity and the side slip angle of each tire.

The invention relates to a charging pile multi-mode vehicle security monitoring method and system, and the method comprises the steps: collecting vehicle contour point cloud, environment parameters and voltage fluctuation data through a stereoscopic vision sensor, a temperature and humidity sensor and a current sensor, and generating a multi-mode fusion data set through time-space synchronization; comprehensive scores of vehicle deformation, environment sudden change and voltage abnormity are calculated based on a dynamic weight fusion model, low-risk, medium-risk and high-risk three-level safety judgment results are generated in combination with a quantile threshold interval, and the environment adaptability defect of a traditional fixed rule is eliminated through a dynamic weight and an adaptive threshold; triggering a hierarchical control instruction according to a judgment result, and synchronously constructing a credible evidence chain containing a timestamp, an operation log and an encryption check code to ensure that the security event is traceable and the data is tamper-proof; and finally, aggregating multi-node user feedback through a federated learning framework, updating a model weight and a threshold interval in combination with a differential privacy technology, and realizing dynamic optimization of a false alarm rate while protecting user privacy.

The invention discloses a doubly-fed induction generator fault detection method combining a sliding-mode observer and a bilateral cumulative sum algorithm, and the method comprises the following steps: designing a doubly-fed induction generator rotor current sliding-mode observer based on the combination of a novel reaching law and a doubly-fed induction generator state equation; carrying out stability and convergence analysis on the designed novel reaching law by adopting a Lyapunov stability theory; and the fault detection method of the doubly-fed induction generator is designed in combination with the optimized bilateral accumulation sum algorithm. A rotor current sensor fault, an input interference voltage fault, a voltage drop fault and a stator turn-to-turn short circuit fault are introduced. The rotor current tracking performance of the designed sliding-mode observer under different doubly-fed induction generator faults is verified by using a residual value obtained by comparing a doubly-fed induction generator rotor current value estimated by the sliding-mode observer with an actual rotor current output value; and meanwhile, the reliability of the designed doubly-fed induction generator fault detection method based on the optimized bilateral cumulative sum algorithm is verified. The doubly-fed induction generator fault detection method combining the sliding-mode observer and the bilateral cumulative sum algorithm has the advantages of being high in tracking precision and accurate in fault state characterization, meanwhile, weak fault features can be effectively converted into digital signals to be output, and faults of the doubly-fed induction generator are detected.

The invention discloses a short-circuit current limiting and short-circuit recovery transient voltage control device and method for an aviation three-stage power generation system, and the device comprises a three-stage power generator, a power generator controller, a current sensor, a voltage and current collection module, and an asymmetric half-bridge excitation control circuit. Collecting the output current iL and voltage UL of the generator through a digital processor of the generator controller; when short circuit occurs, the given voltage USet of the voltage regulating loop is linearly reduced in a piecewise mode according to the relation between the iL and a preset threshold value, so that the generator works in a constant power interval; and when the short circuit is recovered, closing the upper / lower power tubes of the asymmetric half-bridge excitation control circuit according to the relationship between UL and a preset threshold value in a grading manner, and triggering a negative-pressure follow-current mode of a main excitation winding of the generator controller to quickly extinguish the field. The method can adapt to different generators without adjusting hardware circuit parameters, has no motor ablation risk during short circuit, has transient voltage less than 350V after short circuit recovery, has no power supply interruption in the whole process, has response delay less than 1ms, and greatly improves the safety and robustness of an aviation power supply and distribution system.

The invention relates to a harmonic reducer fault diagnosis method based on double-branch feature fusion. The harmonic reducer fault diagnosis method comprises the following steps: synchronously acquiring multi-sensor data of a harmonic reducer acquired by vibration, acoustic emission and current sensors; processing the multi-sensor data through a one-dimensional convolutional neural network to obtain data in a two-dimensional matrix form; the data in the two-dimensional matrix form is input into a pre-trained model, and the processing process of the model is as follows: feature extraction is performed on the data in the two-dimensional matrix form through an MCWGraphKAN branch feature extraction module and a GBiMama branch feature extraction module to obtain a first feature and a second feature, the first feature and the second feature are fused and then mapped to a sample space through a full-connection layer, and the data in the two-dimensional matrix form are obtained; and fault classification is realized. The method achieves the fault diagnosis of the harmonic reducer through a deep learning method, and is used for solving the problems that a single sensor cannot fully express the operation state of the harmonic reducer, and the fault feature extraction of a single-branch network is insufficient.

The invention discloses a rectifier bridge arm fault identification method and system circuit based on multi-characteristic current waveform analysis, and relates to the technical field of rectifier bridge arm fault identification, and the method comprises the steps: collecting a current signal of an AC input side of a rectifier bridge through a current sensor, and obtaining an original current time domain waveform after filtering conditioning; performing period division on the original current time domain waveform based on a voltage synchronization signal, and outputting a positive half-cycle conduction interval and a negative half-cycle conduction interval in each power frequency period; in each power frequency period, performing square integral operation on the current waveforms of the positive half-cycle conduction interval and the negative half-cycle conduction interval to obtain a positive half-cycle energy value and a negative half-cycle energy value, and calculating an energy unbalance degree; and windowing fast Fourier transform is carried out on the original current time domain waveform, amplitudes and phases of third, fifth and seventh characteristic harmonics are extracted to form a harmonic characteristic vector, the vector is matched with a pre-stored bridge arm fault harmonic fingerprint template, and the matching degree of each fault type is obtained.

The invention discloses a sensor signal prediction method, system and equipment for a humanoid robot. The method comprises the following steps: acquiring a multi-source sensor training sample of the humanoid robot; training the heterogeneous neural network fusion model according to the multi-source sensor training sample through a signal amplitude loss function, a frequency loss function and a transient characteristic loss function to obtain a signal prediction model; obtaining current sensor signals of the humanoid robot, wherein the current sensor signals comprise at least two of a joint angle signal, a motor current signal or an acceleration signal; and inputting the current sensor signal into the signal prediction model, and outputting a predicted sensor signal which is a force sense signal or a torque signal. Through multi-source sensor signal fusion and multi-domain feature learning, accurate prediction of key sensor signals is realized, the number of deployed sensors is reduced, the cost is reduced, the system reliability is improved, and the method can be widely applied to the technical field of robots.

A control method for a flame retardant system of an energy storage battery pack, comprising: for each battery module, when a fire occurs in the battery module, on the basis of sensor data provided by a sensor module corresponding to the battery module, determining the fire development stage of the battery module and, on the basis of the fire development stage, controlling the initial degree of opening of a gas release module corresponding to the battery module, so as to activate flame retardation; during the flame retardation, on the basis of the sensor data provided by the sensor module, updating the fire development stage of the battery module and, on the basis of the sensor data provided by the sensor module and the degree of opening of the gas release module, calculating a pressure relief rate of the battery module; and, if the current pressure relief rate falls outside of a target rate range corresponding to the current fire development stage, on the basis of the current sensor data, adjusting the degree of opening of the gas release module.

The present invention relates to a device for measuring a line current. Said device comprises at least three sensor elements having paramagnetic centres, a pump-radiation source, an optical system, and at least three photodetectors which function as part of receiving systems and are each associated with a sensor element. In addition, the device contains two magnetic field sources which generate different bias magnetic fields for each sensor element. The pump-radiation source emits pump radiation into the optical system, which distributes the pump radiation to the sensor elements and thereby irradiates the paramagnetic centres with the pump radiation. These centres emit fluorescence radiation, the intensity and phase shift of which depend on the AC drive signal of the evaluation and control device and on the magnetic flux density acting upon them. The fluorescence radiation is separated from the pump radiation by the optical system and guided onto the photodetectors. The evaluation and control device generates a single-frequency sinusoidal AC drive signal having a frequency of 22 MHz (-4 MHz / +7 MHz), or within a range of 18 MHz to 29 MHz. The lock-in amplifiers are coupled to the sensor elements and evaluate the output signal of the photodetectors with respect to the AC drive signal in order to produce measurement values. Based on these measurement values, the evaluation and control device determines an estimated value for the magnitude of the line current in the line.

The invention discloses a blade vibration monitoring sensor layout optimization method and system and a storage medium. The method comprises the steps that S100, geometric constraints of sensor layout and strategy constraints of vibration evaluation are acquired; s200, generating a sensor phase sequence based on geometric constraints; s300, determining a plurality of vibration orders to be analyzed; s400, for each vibration order, constructing a coefficient matrix of vibration response estimation based on the sensor phase sequence and the total number of the periods to be calculated; s500, for each vibration order, calculating a conditional number of a product of a coefficient matrix and a transposed matrix thereof; s600, selecting the maximum value of the condition number as an evaluation parameter of the current sensor phase sequence; s700, under the geometric constraint, adjusting the sensor phase sequence based on the phase adjustment step length, and executing the steps S300-S600 again; and S800, selecting a minimum value in all evaluation parameters, and outputting a sensor phase sequence corresponding to the minimum value as an optimization result.

The invention provides an isolating switch performance monitoring system, which comprises a data acquisition module which at least comprises a current sensor and a signal conditioning circuit and is used for acquiring action current signals of a driving motor in an isolating switch in real time; the data processing module is used for performing filtering, noise reduction and time domain and frequency domain feature extraction on the received action current signal of the driving motor and establishing a corresponding waveform database according to the extracted features, and the waveform database at least comprises a normal waveform database and an abnormal waveform database; and the state evaluation module is used for comparing the real-time waveform with the reference waveform in the waveform database through a preset algorithm, identifying abnormal feature points existing in the real-time waveform and generating a corresponding performance evaluation result. Precise evaluation of the performance of the isolating switch is realized based on the current waveform of the driving motor.

The invention discloses a cable partial discharge signal noise separation mode identification method and system, and particularly relates to the technical field of cable partial discharge detection, in a multi-cable stacking environment, partial discharge signals are synchronously collected through multiple types of sensors such as a high-frequency current sensor, an ultrasonic sensor and an electromagnetic antenna; performing band-pass filtering, wavelet denoising and multi-channel synchronous alignment on the acquired signals to suppress background noise and retain key features of partial discharge pulses; separating the mixed signals by adopting independent component analysis; calculating a decision coefficient based on an inter-channel correlation coefficient, a signal-noise power ratio and an amplitude dynamic range, and judging whether to introduce a denoising method based on deep learning; for the waveform after noise separation, executing an amplitude re-calibration process so as to correct amplitude scaling uncertainty, and extracting multi-dimensional time domain, frequency domain and phase features; and a support vector machine and other machine learning algorithms are combined to realize automatic identification of partial discharge types.

The invention provides a motor stator fault detection system and method, and relates to the technical field of data processing, and the method comprises the steps: synchronously collecting a three-phase current signal and a stator winding temperature signal during the operation of a motor in real time through a current sensor and a temperature sensor; performing time-frequency domain analysis and spectrum feature extraction on the three-phase current signal to obtain a current unbalance degree and a current harmonic distortion rate, and forming a current feature parameter set; performing filtering noise reduction and trend elimination processing on the stator winding temperature signal to obtain a processed signal; on the basis of the processed signals, temperature abnormal points are recognized through a k-nearest neighbor clustering algorithm of the three-dimensional Euclidean distance, the temperature instantaneous change rate is calculated according to the temperature abnormal points, and a temperature characteristic parameter set containing the abnormal point density and the temperature rise rate is obtained. According to the invention, stator fault detection and differential early warning are realized.

The invention belongs to the technical field of circuit breaker discharge monitoring, and particularly relates to a circuit breaker partial discharge monitoring system which comprises a sensing acquisition module, a data acquisition and preprocessing module, a signal identification and diagnosis module, an alarm and decision module, a communication and visualization module and a system management and storage module. According to the circuit breaker partial discharge monitoring system provided by the invention, the high-frequency current sensor, the ultrasonic sensor, the ultrahigh-frequency electromagnetic wave sensor and the environmental parameter sensor are integrated, so that multi-dimensional detection of electric, magnetic and acoustic signals generated in a partial discharge process and an interference environment is realized, and the signal credibility and the recognition precision are effectively improved; and the problems of false alarm and missing alarm of a single sensor are avoided.

The invention relates to the technical field of permanent magnet synchronous motor current sensor fault diagnosis, in particular to a permanent magnet synchronous motor current sensor fault diagnosis method based on a lightweight double-branch feature fusion neural network. The method comprises the steps of obtaining and preprocessing motor current data, constructing a current sensor fault diagnosis model, optimizing the current sensor fault diagnosis model, and performing fault diagnosis by using the current sensor fault diagnosis model. The method can realize high-accuracy fault identification in a small-sample and multi-working-condition environment, has the advantages of low model complexity, fast reasoning speed and strong edge deployment capability, is suitable for online fault diagnosis and predictive maintenance of the current sensor in the permanent magnet synchronous motor driving system, can monitor the health condition of the motor in real time, and has a good application prospect. And the service life of the motor can be prolonged.

The invention relates to a tunnel reluctance current sensor structure and temperature parameter optimization method and system, and the method comprises the steps: arranging an annular magnetic core, forming symmetrically distributed air gaps in the annular magnetic core, placing a tunnel reluctance current sensor in each air gap, and forming a double-air-gap magnetic circuit structure; constructing a magnetic field consistency coefficient according to the average magnetic induction intensity, the minimum magnetic induction intensity and the maximum magnetic induction intensity of the air gap center area, and performing optimization solution on structural parameters of the double-air-gap magnetic circuit structure by taking minimization of the magnetic field consistency coefficient as a target; respectively establishing a measurement channel and a reference channel according to the double-air-gap magnetic circuit structure obtained through optimization solution; multiple times of voltage signal acquisition are carried out through a reference channel under different injection reference currents and different temperature environments, and a temperature compensation factor is calculated, so that gain parameters of an amplifier in a measurement channel are adjusted. Compared with the prior art, the anti-interference capability and the measurement sensitivity of the system can be greatly improved.

The invention relates to a GIS high-voltage switching station intelligent comprehensive monitoring system and a monitoring method thereof, and belongs to the technical field of power system monitoring. The system aims to solve the problem that the existing GIS high-voltage switchgear is single in monitoring means and cannot realize comprehensive real-time monitoring. According to the technical scheme, a plurality of functional modules such as a temperature monitoring module, a current monitoring module, an SF6 gas monitoring module, a lightning protection monitoring module and a video monitoring module are integrated, and key data such as temperature, current and gas pressure of the GIS high-voltage switchgear are collected in real time through equipment such as a non-contact infrared temperature measurement sensor and a leakage current sensor. And the data is analyzed and processed by the data processing module and then is transmitted to a remote monitoring terminal through the communication module, so that remote monitoring and real-time alarming of the data are realized. According to the invention, multi-dimensional and omnibearing real-time monitoring of the GIS high-voltage switch equipment is realized, the safety and reliability of equipment operation are effectively improved, the operation and maintenance cost is reduced, and the operation and maintenance efficiency is improved.

The invention provides a current sensor. According to the current sensor, four identical magnetic sensing parts are arranged at corresponding positions on the upper side and the lower side of a current guide row, the sensing centers of the four magnetic sensing parts are symmetrical about the current guide row in pairs, and the sensing direction is parallel or antiparallel to the row width direction of the current guide row. The current guide bar is a flat metal conductor so as to counteract the influence of current on any two magnetic sensitive parts symmetrical about the current guide bar. In cooperation with the hardware setting, target signals capable of representing the magnitude, the phase and the frequency of the current in the diversion bar are obtained through corresponding sensing signal processing, meanwhile, the influence of constant interference fields in all directions is eliminated, and three-dimensional interference resistance is achieved. In addition, according to the current sensor provided by the invention, the four magnetic sensing parts and the signal conditioning unit are arranged on the PCBs stacked on the upper side and the lower side of the diversion row, so that the current sensor is compact in structure and small in size.

The invention discloses a quantum double-ring cooperative current measurement device and method, and belongs to the technical field of electric measurement. The device comprises an outer ring unit used for measuring a high-frequency signal in current; the inner ring unit is used for measuring a low-frequency signal in the current; the digital signal processing unit is used for carrying out synchronous high-speed ADC sampling on the high-frequency signal measured by the outer ring unit and the low-frequency signal measured by the inner ring unit, carrying out fast Fourier transform on the sampled signal and converting the sampled signal into a frequency domain to obtain a VTMR (f) signal and a delta fNV (f) signal, and calculating the VTMR (f) signal and the delta fNV (f) signal to obtain the VTMR (f) signal and the delta fNV (f) signal. And reconstructing the full-band current waveform of the time domain. According to the invention, the measurement accuracy and reliability of the current sensor in broadband, high harmonic and strong interference scenes can be significantly improved, and the method is suitable for the fields of new energy grid connection, flexible direct current transmission, power grid protection, high-precision measurement and the like.

The invention discloses a hydropower station corridor autonomous mobile robot path navigation method, system and device and a storage medium, and the method comprises the steps: collecting hydropower station multi-source environment data, constructing a laser two-dimensional map, and fusing image semantic segmentation labels to generate a two-dimensional semantic map; the current sensor data is matched with the two-dimensional semantic map, and the initial pose of the robot is determined; a navigation path is generated on the basis of the initial pose and the target point, and the pose of the robot is dynamically adjusted and updated in the navigation process; path planning control is carried out based on the updated pose, obstacles are recognized in real time, and a driving path is optimized; recording path driving navigation data, updating the two-dimensional semantic map, and returning to the starting point based on the updated map after the task is completed. According to the method, a two-dimensional semantic map with space consistency and semantic definition is constructed, key geometric structure extraction and navigation structure index generation are combined, accurate recognition and path topology understanding of passable areas in the hydropower station gallery are achieved, and the accuracy and robustness of autonomous path navigation are improved.

The utility model provides an intelligent scheduling command platform with integrated scheduling, and effectively solves the problem that the scheduling work is influenced because an interphone and a camera in the prior art are lack of effective means of effective electric quantity. The dispatching command platform comprises an electric quantity monitoring module, the electric quantity monitoring module comprises a primary diagnosis circuit and a secondary diagnosis circuit, the primary diagnosis circuit detects a current signal of a battery in dispatching equipment during use by using a current sensor, obtains a reminding signal and an early warning signal based on the current signal, and sends the reminding signal and the early warning signal to the secondary diagnosis circuit; the reminding signal and the early warning signal are output to a command center; the primary diagnosis circuit is connected with one end of the secondary diagnosis circuit, and the other end of the secondary diagnosis circuit is connected with the command center.

The invention relates to the technical field of battery management, and discloses a sodium ion battery with a function of preventing temperature from being out of control, the battery is arranged in a battery shell, a plurality of first temperature sensors are arranged on the battery to collect sodium ion battery temperature data, and the first temperature sensors and the battery are connected with a controller; the controller is further connected with a voltage sensor and a current sensor which are used for detecting electrical parameters. The temperature control method in the controller comprises the following steps: setting an acquisition time point to obtain sodium electricity temperature data, calculating a sodium electricity temperature value, and limiting output power according to the temperature value; setting an acquisition time period to obtain multiple groups of temperature and electricity data, generating a change speed and trend curve of temperature and total power, calculating a curve matching value and a temperature power matching value, and if the matching value exceeds a reference range, performing early warning. Accurate temperature control and out-of-control early warning of the sodium-ion battery are achieved, performance degradation at the extreme temperature is reduced, use safety is guaranteed, and the method is suitable for scenes such as energy storage and electric equipment.

Technical solutions for diagnosing a sensor fault in an actuator incorporating a permanent magnet direct current (PMDC) motor are described, including: measuring, by means of a position sensor, the position of the PMDC motor; determining, based on the measured position, an estimated motor speed; measuring, by means of a current sensor, the current in the PMDC motor; determining, based on the measured current in the PMDC motor, a voltage command for the PMDC motor; determining, based on the measured current and the voltage command and using a motor speed observer, an observed speed of the PMDC motor; determining a differential speed as the difference between the estimated motor speed and the observed speed; determining, based on the differential speed, the sensor fault; and performing an action in response to the determination of the sensor fault.

The invention provides an open-type comprehensive current sensor data analysis method, which comprises the steps of simulating asymmetric distribution of a magnetic field according to an actual offset coordinate of a guide mass center in a clamp opening coordinate system, and generating a magnetic field compensation coefficient about a current eccentric state; compensating the real-time magnetic field intensity data according to the magnetic field compensation coefficient related to the current eccentric state, and eliminating random noise to obtain calibrated symmetrical magnetic field signal distribution; generating a reference curve of dynamic measurement current and magnetic field conversion according to the optimized conductor eccentric position state description, and determining a current measurement calibration curve; performing conversion processing on the input magnetic field signal through the current measurement calibration curve so as to correct signal distortion caused by eccentricity, and obtaining a comprehensive current reading without eccentric interference; and according to the relevance between the comprehensive current reading without eccentric interference and historical reference current data, analyzing the stability of a current measurement state, and obtaining a real-time clamp operation state feedback record.

The application discloses a hidden space social courtesy navigation method and system based on interaction point arrival time and belongs to the technical field of robot autonomous navigation. The current sensor observation data of a robot is used to generate a current time hidden state; a hidden space dynamics model is combined to deduce and generate a candidate set of multiple candidate action sequences and hidden state trajectories of the candidate set and input the candidate set to a hidden space social evaluator to obtain a social courtesy cost, a threshold trigger flag and a lower limit of execution length; each candidate action sequence is scored based on a comprehensive score function which is fused with the social courtesy cost, a trajectory smoothness cost and an immediate reward and a terminal value calculation; the candidate action sequence with the highest score is selected; and action sequence optimization is further performed according to the threshold trigger flag and the lower limit of execution length. The method can not only maintain the calculation advantage of hidden space deduction in a high-dimensional environment, but also introduce social courtesy related constraints and signals, so that local planning which takes into account short-term safety, long-term goals and social compliance is realized.

The invention belongs to the technical field of optical modules, and particularly relates to a CPO module structure which comprises an installation shell, a switching chip, an optical engine, a light source system and an auxiliary monitoring system, the switching chip and the optical engine are installed on a supporting substrate in the installation shell, an optical fiber coupling interface is arranged on the outer side of the installation shell, and the optical fiber coupling interface is connected with the optical source system. The optical fiber link is used for connecting the optical engine and external computer equipment; according to the CPO module structure, related data such as power management, temperature management and link connection signal stable state of the CPO module structure in the use process are monitored in real time through the configured auxiliary monitoring module, and the data are collected through optical power, temperature, current sensors and the like; when abnormal data occur in the monitoring process, troubleshooting and positioning and automatic maintenance and troubleshooting can be rapidly carried out, the frequency of disassembly, maintenance and repair is reduced, and the use experience is improved.

The utility model discloses a temperature monitoring device and semiconductor process equipment. The temperature monitoring device comprises an over-current control loop and an over-temperature control loop; the overcurrent control loop comprises a current sensor and a control module which are connected; the control module comprises a first contactor; the current sensor is connected in series with a first circuit for supplying power to the electromagnetic module in the semiconductor process equipment, and the current sensor generates a first electric signal and sends the first electric signal to the control module under the condition that the current on the first circuit exceeds a first threshold value; under the condition that the control module receives the first electric signal, the first circuit is disconnected through a first contactor connected with the first circuit; the over-temperature control loop comprises an over-temperature sensor and a second contactor which are connected; the over-temperature sensor is connected with the electromagnetic module, when the temperature of the electromagnetic module exceeds a second threshold value, the over-temperature sensor generates a second electric signal and sends the second electric signal to the second contactor connected with the first circuit, and the second contactor disconnects the first circuit when receiving the second electric signal.