367 results about "Equipment temperature" patented technology

The invention discloses a real-time temperature measurement and optimal scheduling method for a rail-mounted intelligent inspection robot, and the method comprises the steps: obtaining the temperature of equipment along a rail in real time through a temperature sensor on the inspection robot, analyzing the working state of the equipment along the rail, and recognizing a potential overheating risk region; establishing a multi-robot collaborative inspection task scheduling model according to the position information and the emergency degree of the potential overheating risk area; planning an inspection path for the multi-robot collaborative task scheduling data by taking the shortest time as an index, optimizing the inspection path by adopting a fusion improved ant colony algorithm and combining a TEB strategy, and obtaining a global planning path; a trajectory optimization strategy based on Minimum Snap is adopted for the global planning path, a Bezier curve is utilized to optimize the path trajectory, a multi-robot collaborative task scheduling model is combined with an auction algorithm to dynamically allocate tasks, efficient utilization of resources and priority processing of emergency tasks are ensured, the inspection efficiency and safety are improved, and the operation cost is reduced.

The invention discloses a charging and moisture regaining equipment temperature prediction method based on a time sequence fusion network model, and relates to the field of production process control, and the method comprises the steps: collecting time sequence data in a charging and moisture regaining equipment production environment, and carrying out the preprocessing; dividing the data set into a training set, a verification set and a test set, and injecting Gaussian noise into the training set; a prediction model for predicting the outlet temperature is constructed, and the prediction model is a time sequence fusion network model and comprises a residual TCN time sequence convolutional network, an SK-Net multi-scale attention network and a BiLSTM bidirectional circulation network; pre-training the prediction model by using the training set; utilizing the trained prediction model to predict the outlet temperature of the feeding and moisture regaining equipment; and evaluating a prediction result, and if an evaluation index is greater than a threshold value, starting an incremental training process to re-train the prediction model. According to the invention, through a multi-module combined deep learning model, the prediction accuracy of the outlet temperature of the charging and moisture regaining equipment can be improved in a complex and changeable industrial environment.

The invention discloses an equipment temperature adjusting method fusing a long short-term memory network, and particularly relates to the technical field of temperature control, which comprises the following steps: determining the deployment position of a sensor through thermal simulation, collecting multi-source heterogeneous data, dynamically adjusting the sampling frequency in combination with the temperature and the load current change rate, and adjusting the temperature of the sensor; after data preprocessing, an attention mechanism enhanced LSTM prediction model is constructed, a time sequence sample data set is divided according to equipment thermal response characteristics, training is carried out, and an optimal model is obtained through early stop mechanism optimization; predictive feedback double-closed-loop regulation and control is achieved based on the optimal model, outer loop PI control is combined with an integral separation mechanism to generate a basic control quantity, an inner loop outputs a correction quantity through fuzzification, reasoning and defuzzification, an actuator is driven after superposition, and safety linkage is triggered synchronously; constructing an incremental data buffer pool to screen effective samples, and adaptively updating model parameters by adopting a layered fine tuning strategy; the temperature regulation and control precision and the long-term self-adaptive capability are obviously improved, and the over-temperature risk of equipment is reduced.

The invention provides a transformer substation fault intelligent analysis and diagnosis method and system based on big data, and the method comprises the steps: obtaining the surface and internal temperature data flows of all power equipment in a transformer substation, and the environment temperature and humidity data flow of the transformer substation in the operation environment of the transformer substation; performing cross-time-scale dynamic coupling on the temperature data stream and the environment temperature and humidity data stream to generate a heterogeneous temperature characteristic stream; based on the heterogeneous temperature characteristic flow, dynamically adjusting an association rule of the temperature anomaly of the power equipment and the operation state of the transformer substation; performing cross-device coupling analysis on the heterogeneous temperature characteristic flow through a deep belief network to generate a dynamic conduction topological structure; and generating a transformer substation fault diagnosis result in combination with the adjusted fault association rule and the dynamic conduction topological structure. According to the invention, the early warning accuracy and diagnosis efficiency of the substation fault are improved.

The invention relates to the technical field of automatic control, in particular to an intelligent equipment management method and system, and the method comprises the following steps: obtaining a control cabinet log, extracting an equipment temperature load pressure sequence, comparing a set offset to generate a deviation label, collecting an operation time length and load change mapping early warning table, and generating an abnormal record; and extracting task configuration and equipment state re-marking alternating points to generate load configuration, resetting an instruction sequence to generate a scheduling sequence, extracting an instruction time-consuming marking interference section, and switching a standby link to generate an evasion list. According to the invention, by collecting the temperature, the load rate and the pressure sequence in the operation cycle of the equipment, the dynamic state identification capability is improved, the scheduling path is optimized, the resource contention is relieved, the delay path is marked in combination with the original response data, and the standby link is switched to avoid interference, so that the flexibility and stability of linkage scheduling are improved, and the task execution beat accuracy is enhanced; the instruction blocking and interruption risks are effectively reduced, and the operation toughness and response efficiency of a production line are improved.

The invention relates to the technical field of industrial equipment temperature control, and discloses a temperature control system, which comprises a multi-source state sensing module for acquiring a power load, temperature, humidity and feedback temperature; the feed-forward prediction and analysis module is used for generating a feed-forward signal based on the load and the temperature; the closed-loop feedback and safety guarantee module is used for calculating a feedback signal according to the temperature deviation; the cooperative control decision module combines the feed-forward signal and the feedback signal to generate a fan instruction, and generates a baffle instruction for balancing heat dissipation and regeneration according to the feed-forward signal and humidity; the self-adaptive heat dissipation execution module is used for executing a fan instruction to generate cooling airflow; and the dynamic energy regeneration module executes a baffle instruction to distribute the waste gas flow direction. According to the method, the power load is introduced to serve as a prospective index, correction is conducted in combination with the environment temperature, a predictive heat dissipation control model is constructed, the system can start heat dissipation adjustment before a large amount of heat is generated, and therefore smooth and stable control over the equipment temperature is achieved.

The invention discloses a power equipment overheating fault diagnosis method, system, equipment and medium, and relates to the field of fault diagnosis. According to the method, a visible light image and an infrared image are enhanced through a preset double-flow generative adversarial network to obtain a target visible light image and a target infrared image, and a device semantic mask graph is generated according to the target visible light image; projecting temperature data in the target infrared image to each segmented power device according to the device semantic mask map to form a device temperature distribution map; determining static temperature characteristics and dynamic temperature characteristics of each power device according to the device temperature distribution diagram, and generating a space-time temperature evolution diagram according to the static temperature characteristics, the dynamic temperature characteristics and the physical connection relationship among the power devices; and predicting an overheating fault probability, a fault type and a potential fault source of each power device according to the space-time temperature evolution diagram and the electrical parameters. By implementing the technical scheme provided by the invention, the overheating fault prediction accuracy of the power equipment is improved.

The invention relates to the technical field of temperature control of high-precision industrial heating equipment, in particular to an anti-interference precise temperature control method and system for heating an upper die, and the method comprises the steps: starting a multi-region heating array, heating the upper die to a target temperature according to a preset temperature rise curve, and building a uniform thermal field; before the high-pressure medium is introduced, heat load change caused by the high-pressure medium is predicted based on medium process parameters; in the medium introduction process, dynamic anti-interference compensation control is executed, a feedforward power compensation instruction based on a thermal load and a feedback power fine tuning instruction based on temperature deviation are generated respectively, and the feedforward power compensation instruction and the feedback power fine tuning instruction are weighted and fused into a composite power driving signal for adjusting a heating array; when the temperature fluctuation converges to a stable threshold value, the feedforward compensation weight is gradually reduced, smooth switching to steady-state control is achieved, transient thermal shock caused by a high-pressure medium is effectively restrained through feedforward and feedback cooperative compensation and dynamic weight adjustment, the temperature fluctuation is reduced, and the temperature control precision and stability are improved.

The invention discloses a dynamic digital compensation method for the metering error of wide-temperature-interval electric energy meter equipment, and the method comprises the steps: collecting the metering error data of the equipment at different temperatures through a temperature sensing module, so as to construct a historical temperature-error relation library; training an adaptive compensation model based on a historical temperature-error relation library to establish a nonlinear mapping relation between the temperature and the error; in the real-time operation process of the equipment, the current equipment temperature is collected through a temperature sensing module, and the current equipment temperature and the real-time load rate are input into the self-adaptive compensation model to obtain error correction; and the metering result is dynamically adjusted in real time according to the current equipment temperature and the error correction amount so as to output a compensated accurate metering value. According to the method, a temperature sensor does not need to be newly added, dynamic error correction is realized only by utilizing the existing temperature sensing capability of equipment, the metering precision and stability in a wide-temperature environment can be improved, the comprehensive cost is reduced, and the high-precision metering requirements in special environments such as extremely cold and high temperature environments are met.

The invention provides an operation fault monitoring method and equipment of an HVAC system based on data analysis, and relates to the technical field of data analysis. The method comprises the following steps: firstly, based on harmonic semantic information in equipment vibration time sequence data, carrying out semantic enhancement on the equipment vibration time sequence data to form an equipment vibration semantic vector; then, based on surge semantic information in the equipment sound time sequence data, performing semantic enhancement on the equipment sound time sequence data to form an equipment sound semantic vector; then, semantic mining is carried out on the equipment temperature time sequence data, and an equipment temperature semantic vector is formed; further, fusing the equipment vibration semantic vector, the equipment sound semantic vector and the equipment temperature semantic vector to form an equipment operation semantic vector; and finally, predicting and outputting operation fault data based on the equipment operation semantic vector. Based on the method, the problem of relatively low reliability of operation fault monitoring in the prior art can be improved.

The invention discloses a semiconductor narrow linewidth laser temperature cycle test system and a test method thereof, which belong to the field of laser communication and eliminate errors caused by system loss to optical power measurement through optical power calibration compensation coefficients. The variable-temperature adjustment is controllable, the wavelength of the laser is efficiently tuned, and various indexes are measured. The device mainly comprises a plurality of drivers and a high-low temperature test box, to-be-tested laser parts of a plurality of tested lasers are placed in the high-low temperature test box, and other parts of the tested lasers are located outside the high-low temperature test box so as to avoid the influence of equipment temperature drift on measurement; the plurality of drivers are respectively and correspondingly connected with the plurality of tested lasers, optical chip driving current and TEC closed-loop temperature control are provided for the tested lasers, wavelength tuning of the lasers is realized through the TEC closed-loop temperature control system, and temperature sensors are arranged at the tested lasers in the high and low temperature test box. The invention is mainly used for the semiconductor narrow linewidth laser temperature cycle test system.

A power transformation equipment temperature anomaly identification and diagnosis method comprises the following steps: collecting state variables, performing cleaning, interpolation complementation and abnormal point elimination on multi-source data through a time synchronization mechanism, and constructing a unified data matrix; extracting statistical features and time sequence dynamic features in the time sequence based on the data matrix, and performing dimensionality reduction on redundant information in combination with a principal component analysis method to form a multi-dimensional fusion feature vector; an unsupervised learning model based on LSTM-AE is constructed, a normal working condition data learning feature reconstruction mode is utilized, and a reconstruction error is taken as a criterion to identify potential temperature anomaly; and calling a preset expert rule base and a knowledge graph, automatically analyzing dominant factors causing anomalies, and identifying typical anomaly types. According to the invention, automatic identification and classification diagnosis of the temperature abnormity of the power transformation equipment under an unsupervised condition are realized, the accuracy and response speed of fault identification are obviously improved, and the intelligence and practicability of equipment operation state monitoring are enhanced.

The invention relates to the technical field of self-adaptive control, in particular to an AI-based centralized air-conditioning efficient machine room control method and system, which comprises the following steps: acquiring temperature distribution of a machine room, calling temperature data of an air supply outlet, an air outlet and equipment, identifying temperature gradient, calculating airflow direction and wind speed, screening temperature abnormal areas, and analyzing the relationship between temperature fluctuation and equipment operation. And acquiring a temperature flow state. According to the invention, through fine monitoring of local temperature changes and air flow vectors in the machine room, the recognition capability of a heat accumulation area, accurate analysis of a cold and hot air flow intersection and dynamic adjustment of an air supply angle and an air volume are improved, cold air backflow is effectively reduced, the conveying efficiency is improved, exhaust control is combined with heat diffusion path optimization, and the heat extraction efficiency is improved; real-time calculation of the short-time temperature change rate ensures accurate matching of the air supply amount and the equipment load, energy consumption is reduced, optimization of an air supply system and an air exhaust system is achieved through comprehensive state analysis, and the self-adaptive adjusting capacity and energy efficiency of the air conditioning system are improved.

The invention provides a screen scarifier key component health state management system and method, and the system employs a hierarchical modular design, and is divided into an edge calculation layer, an application layer, a data layer, and a hardware layer. According to the method, the operation states, including active power, equipment temperature, vibration, noise and other parameters, of a power system, an excavation system, a screening system, a track lifting and lining system, a conveying belt system, a frame and other key components of the screen scarifier are monitored in real time, potential faults are found and early warned in time, the stability and reliability of the screen scarifier are further ensured, and the service life of the screen scarifier is prolonged. And the intelligent operation and maintenance level of the screen scarifier is improved.

The invention relates to a power equipment inspection method, device and equipment, a readable storage medium and a product, and relates to the technical field of power. Comprising the following steps: acquiring equipment image data, equipment temperature data and equipment type of to-be-inspected power equipment; performing external health analysis on the to-be-inspected power equipment based on the equipment image data to obtain external health state information of the to-be-inspected power equipment; performing internal health analysis on the to-be-inspected power equipment based on the equipment temperature data to obtain internal health state information of the to-be-inspected power equipment; performing priority analysis on the to-be-inspected power equipment based on the equipment type to obtain an inspection priority of the to-be-inspected power equipment; according to the external health state information, the internal health state information and the inspection priority, determining an inspection strategy of the to-be-inspected power equipment; and controlling the inspection robot to inspect the to-be-inspected power equipment according to the inspection strategy. The method can improve the inspection reliability of the power equipment.

The invention relates to the field of photovoltaic equipment abnormity early warning, and provides a photovoltaic equipment temperature abnormity prediction method and device based on a digital twinborn model, and the method comprises the steps: obtaining an environment acoustic signal, meteorological data, and the actually measured temperature data of photovoltaic equipment; performing acoustic spectrum feature extraction according to the environmental acoustic signal to obtain a virtual environmental parameter set; sending the meteorological data and the virtual environment parameter set to a preset digital twinborn model for processing to obtain an initial temperature predicted value set; determining a temperature prediction value distribution diagram according to the initial temperature prediction value set and actually measured temperature data of the photovoltaic equipment, and comparing a temperature prediction result with an abnormal temperature threshold value to obtain a temperature abnormal risk point; and processing according to the temperature prediction value distribution diagram, the temperature anomaly risk points and the meteorological data to obtain temperature anomaly early warning information of the photovoltaic equipment, and improving the accuracy and foresight of temperature anomaly prediction of the photovoltaic equipment.

The invention discloses an NFC trigger control method integrating wireless charging and automatic data backup, and relates to the field of data backup. The method comprises the following steps: realizing equipment identity authentication and electric quantity information acquisition through an NFC trigger mechanism, dynamically calculating target charging power based on the current electric quantity, and monitoring the equipment temperature in real time in a wireless charging process. By calculating the difference value between the working upper limit temperature and the real-time temperature of the storage chip, a pre-trained reading frequency determiner is used for intelligently generating a real-time reading frequency parameter, and the working state of the storage chip in the data backup process is dynamically regulated and controlled. A cloud collaborative optimization mechanism is further introduced, the confidence coefficient weight of each determination unit is dynamically adjusted according to historical accuracy data, and self-adaptive optimization of the reading frequency determiner is achieved. According to the method and the device, the problem of equipment overheating during concurrent execution of wireless charging and data backup is effectively solved, the balance of charging efficiency, backup safety and equipment thermal management is realized, and the user experience is remarkably improved.

The invention discloses a temperature measurement method and a temperature measurement nut, and belongs to the technical field of equipment temperature measurement. According to the method, the dynamic thermal radiation interference coefficient calculation model is constructed through the historical temperature measurement data and the equipment operation parameters, so that the dynamic acquisition of the dynamic thermal radiation interference coefficient can be realized based on the real-time temperature measurement data, and the real-time temperature measurement data is corrected to obtain the corrected temperature measurement data; performing spectral analysis on the real-time temperature measurement data to determine a thermal radiation change frequency correction coefficient, constructing a temperature response function based on the real-time temperature measurement data, and further determining a thermal response time constant based on the temperature response function; and then thermal inertia lag compensation processing is carried out on the corrected temperature measurement data through a thermal radiation change frequency correction coefficient, a thermal response time constant and equipment operation parameters to obtain actual temperature data of the to-be-measured equipment, so that the problem of inaccurate temperature measurement in a high-temperature environment in the prior art can be solved. And the accuracy of temperature measurement in a high-temperature environment is improved.

The invention relates to the field of special equipment safety management, and discloses a special equipment intelligent control method and system based on the Internet of Things. The method comprises the following steps: acquiring a facial image and fingerprint data of an operator through an industrial Internet of Things sensor, and matching by combining a preset biological recognition template to obtain preliminary verification information; personnel qualification grades and historical operation records are called from a database, and a comprehensive qualification score is determined through random forest algorithm processing; acquiring temperature and pressure parameters of the equipment in real time, and marking abnormity to generate a state label if the parameters exceed threshold values after detection; based on the state tag and the qualification score, verifying identity authenticity in a multi-dimensional manner, matching equipment operation qualification requirements, generating a matching degree, generating a temporary permission token when the matching degree reaches the standard, and otherwise, refusing to access; and the token is encrypted and transmitted to the equipment control module, and the operation range is adjusted in real time to complete dynamic permission configuration. According to the method, accurate authority control is realized, the operation safety and management efficiency of the special equipment are improved, and guarantee is provided for safe operation of industrial production.

The invention discloses a hardware acceleration equipment management system and method applied to a network security product, and relates to the technical field of hardware acceleration equipment management of the network security product. Comprising a PCIE to M.2 adapter plate module, a structure supporting module, a load real-time monitoring module, a resource dynamic allocation module, a cross-device task scheduling module and a device compatibility adaptation module. The adapter plate splits a PCIe X8 signal into two paths of X4 and synchronizes a clock; the structure supporting module fixes equipment; the load real-time monitoring module collects data to generate a report; the resource dynamic allocation module allocates tasks based on reports; the cross-device task scheduling module migrates tasks and switches a PCIe channel mode when the load exceeds a threshold value; the equipment compatibility adaptation module is responsible for identifying equipment, loading a driver, unifying an instruction set, detecting hot plug state update and dynamically regulating and controlling the temperature of the equipment; and all the modules work cooperatively to realize efficient management of the network security product on the M.2 equipment.

The invention discloses a continuous equipment temperature distribution optimization control method and system, and belongs to the technical field of temperature control, and the method specifically comprises the steps: monitoring equipment production process parameters in real time, and transmitting the parameters to a control center; the control center corrects the parameters by using a compensation algorithm constructed based on an ideal gas state equation, extracts furnace temperature data from the corrected parameters, calculates a control signal according to the deviation between the furnace temperature data and a preset furnace temperature target value by adopting PID (Proportion Integration Differentiation) operation, and adjusts the opening degree of a gas actuator to obtain a corrected gas flow value; calculating required combustion-supporting air flow according to the value and a preset air-fuel ratio, and adjusting a combustion-supporting air actuator to stabilize heat supply in the furnace; when the furnace temperature is stable, an online temperature measurement thermal imager is used for collecting the temperatures of five point positions of the pipe body, the average value is calculated and compared with the temperature required by the process, and then the left and right temperature adjustment areas are adjusted; the method can effectively improve the stability of equipment production and the product quality.

The invention discloses a substation equipment temperature intelligent sensing method and system. The content comprises data acquisition, adaptive preprocessing, graph pre-training, temperature sensing model construction and temperature intelligent sensing. The invention relates to the technical field of substation equipment management, in particular to a substation equipment temperature intelligent sensing method and system.According to the scheme, firstly, a multi-channel covariance is estimated in real time through an adaptive sliding window, a scaling factor is introduced, noise and anomaly isolation is conducted in combination with low-rank-sparse decomposition and microfilterable filtering; high-fidelity interpolation is carried out on the data with the exception removed based on a covariance structure; then, a time-varying sensor graph is constructed, structural priori is pre-trained through a graph self-encoding network, sensor deviation is dynamically compensated through residual regression in a fusion feature space, finally, a high-precision temperature sensing value and a corresponding confidence coefficient score are output, and reliable data support is provided for operation and maintenance decision making and abnormal alarm.

The invention relates to the technical field of automatic temperature calibration. The invention relates to an automatic temperature calibration method for temperature measuring equipment. The method comprises the following steps: S1, collecting historical temperature calibration records, and setting a temperature floating threshold value for each type of measured object; s2, measuring the temperature value of the object by using the NTC group, performing stable detection on multiple groups of temperature values in combination with a temperature floating threshold, and then measuring the resistance value of the NTC group with the stable temperature value; s3, obtaining standard resistance specification information of the NTC group, and then performing deviation calculation on the real-time resistance value and the standard resistance value; according to the method, aiming at the problem of temperature measurement difference caused by precision specifications of B and R values of the NTC element, a segmented compensation resistance method is utilized, the deviation between NTC resistance values and a standard resistance value at different temperature points is actually measured, a deviation array is constructed, and a temperature calculation program is imported, so that a measurement error caused by element difference is effectively compensated, and a measurement result is closer to a real temperature.

The utility model discloses a sample detection equipment temperature control device and sample detection equipment, the sample detection equipment temperature control device comprises a shell and a temperature control mechanism, the shell is provided with a socket for a reaction tube to stretch in, the temperature control mechanism comprises a temperature control assembly and a pushing module, the temperature control assembly comprises heating pieces and two heat conduction blocks arranged in the shell in a spaced mode, an inserting gap communicating with the inserting opening is formed between the two heat conduction blocks, and the heating pieces are arranged on the sides, facing the inserting gap, of the heat conduction blocks correspondingly. The pushing module is used for driving the two heat conduction blocks to move to the attaching position in the opposite direction and move to the avoiding position in the back-to-back direction. The pushing module drives the two heat conduction blocks to move back to back to the avoiding position for avoiding the reaction tube, and the insertion gap between the two heat conduction blocks is increased, so that the reaction tube does not rub with the heating sheet in the process of extending into the insertion gap, the service life of the heating sheet is prolonged, and the maintenance and replacement frequency of the heating sheet is greatly reduced.

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.

The invention relates to the technical field of production control, and discloses a printing temperature control simulation method and system based on PLC control. According to the method, infrared temperature sensors are evenly installed in a printing temperature monitoring area, historical temperature data of the printing temperature monitoring area and the surrounding environment are gathered and collected, meanwhile, the historical temperature data collected in real time are processed through a data processing method, and after processing is completed, the printing temperature monitoring area is monitored. Analyzing the processed historical temperature data through a control variable method and an environment temperature method to obtain analyzed historical temperature data, constructing a temperature prediction model based on the analyzed historical temperature data, training the temperature prediction model through a model optimization algorithm, and obtaining a trained equipment temperature prediction model; and finally, simulation control is performed through a PLC based on the trained temperature prediction model, so that the accuracy of printing temperature control simulation is improved.

The invention belongs to the technical field of mining industry intelligent control, and particularly provides an underground dynamic ventilation and cooling cooperative control method and system based on digital twinning, and the method mainly comprises the steps: synchronizing an original data set collected by an underground sensor in real time through a digital twinning model; performing data division on the original data set to obtain a temperature-equipment group and a wind speed-equipment group; constructing an equipment-temperature dynamic relation graph according to the temperature-equipment group; key nodes in the equipment-temperature dynamic relation graph are extracted, and a key relation sequence is generated; and generating a ventilation equipment wind speed adjustment amount and a temperature control equipment start-stop instruction. Through collaborative analysis of the association relationship between the temperature conduction path and the equipment, the linkage regulation and control instruction of the ventilation and temperature control equipment is dynamically generated, the multi-equipment state, the temperature path and the roadway topology can be comprehensively analyzed, the heat conduction rule is accurately identified, collaborative control over ventilation and cooling is achieved, the system regulation and control efficiency is improved, local control overload is avoided, and the system reliability is improved. And resource configuration is optimized.

The invention belongs to the technical field of high-voltage switch equipment state monitoring, and particularly relates to a high-voltage switch equipment temperature field monitoring method and system based on digital twinning, and the method comprises the steps: obtaining a three-dimensional model and real-time operation data of high-voltage switch equipment based on digital twinning; obtaining a temperature field prediction result of the high-voltage switchgear according to the obtained real-time operation data and the deep learning reduced-order model; wherein the deep learning order reduction model comprises a deep feed-forward neural network used for mapping the acquired real-time operation data from a working condition parameter space to a low-dimensional feature space and a convolutional auto-encoder used for performing temperature field reconstruction on low-dimensional feature vector data output by the deep feed-forward neural network; and associating the obtained temperature field prediction result with the three-dimensional model, and performing visual interaction of the temperature field of the high-voltage switchgear to complete monitoring of the temperature field of the high-voltage switchgear based on digital twinning.

The invention relates to the technical field of power transformation equipment, in particular to a power transformation equipment temperature abnormity early warning method and system based on a time sequence attention mechanism, and the method comprises the steps: firstly, obtaining the historical and real-time temperature data of equipment; secondly, segmenting data through a sliding time window, calculating a temperature change rate and a weight by using an attention mechanism, and further obtaining a weighted average time window feature; secondly, extracting time sequence features by using a long-short term memory (LSTM) network, and calculating a temperature anomaly degree through a deep attention mechanism; and finally, generating temperature abnormity early warning information. According to the method, multiple advanced technologies are combined, the accuracy and the real-time performance of temperature anomaly detection are effectively improved, the flexibility and the interpretability of the system are enhanced, powerful technical support is provided for safe operation and preventive maintenance of power transformation equipment, and the method has important application value and popularization prospects.

The invention discloses an outdoor power equipment temperature abnormity monitoring method, device and equipment, and the method comprises the steps: carrying out the calibration processing of power equipment temperature data collected based on a dynamic collection frequency through employing a preset calibration algorithm, and obtaining the calibration temperature data; performing temperature anomaly analysis according to the calibration temperature data based on the temperature threshold, the trend threshold, the mutation mechanism and the baseline standard, and generating an anomaly analysis data packet; sending the abnormal analysis data packet to a relay gateway according to a spread spectrum technology and a check retransmission mechanism, so that the relay gateway performs data aggregation and protocol conversion processing based on an abnormal priority to obtain a transmission data packet; and triggering different temperature early warning mechanisms through the cloud platform according to the abnormality level in the received transmission data packet to realize temperature abnormality early warning. The technical problems of lack of timeliness, accuracy and remote control capability of monitoring due to lack of timeliness, weak data acquisition anti-interference capability and easy loss in the data transmission process in the prior art can be solved.