362 results about "Normal range" patented technology

The invention provides an intelligent operation and maintenance method and system for a photo-thermal power generation system based on the Internet of Things, and relates to the technical field of information technologies, and the method comprises the steps: obtaining temperature, humidity and vibration environment parameter data collected by a sensor disposed on power generation equipment; and receiving the environmental parameter data, comparing the environmental parameter data with a preset threshold value, and judging whether the environmental parameters exceed a normal range or not. According to the method, environmental parameters such as temperature, humidity and vibration of the equipment are collected in real time by arranging the sensors, when the parameters exceed the preset threshold value, performance parameter collection is triggered, weighted average calculation is carried out in combination with the environmental parameters, and the equipment health index is obtained; according to the method, intelligent assessment and predictive maintenance of the health state of the power generation equipment are realized, the operation reliability and the maintenance efficiency of the equipment are improved, the equipment fault risk is reduced, and the service life of the equipment is prolonged.

The invention provides a complex task decomposition and dynamic optimization method and device based on a large language model, and relates to the technical field of artificial intelligence. A complex task decomposition and dynamic optimization method based on a large language model comprises the following steps: based on a pre-trained large language model, decomposing a to-be-executed task into a plurality of sub-tasks, and generating a task instruction queue for executing the plurality of sub-tasks; sequentially executing the subtasks in the task instruction queue, and recording information in the execution process to obtain task execution data; generating execution state feedback information according to the task execution data and a preset normal range; and generating a task decomposition strategy adjustment instruction according to the execution state feedback information, wherein the task decomposition strategy adjustment instruction is used for adjusting the task instruction queue and / or the execution main body of the subtask. According to the technical scheme of the embodiment of the invention, the task process is adjusted in real time in the task execution process, the task processing adaptability is enhanced, the change of a complex environment can be flexibly coped with, and the task completion success rate and the overall efficiency are improved.

A control method for fire-fighting false alarm of an energy storage system comprises the following steps of: calculating a fluctuation period, a derivative and a probability density function by using fast Fourier transform and the like by acquiring data streams such as current, voltage, power and heat energy of the energy storage system, determining a system state and predicting a related trend. And predicting power by using an autoregressive moving average algorithm and a support vector machine, and setting a threshold value according to a probability density function to judge abnormity. For heat energy data, through derivative calculation and signal analysis, states are identified and early warning is carried out in combination with models such as a deep convolutional adversarial neural network and a support vector machine. The normal range of monitoring data is determined by means of the long-short-term memory neural network, the change trend and physical quantity relation of abnormal data are analyzed and predicted through a cubic exponential smoothing method and Gaussian process regression, potential risks are judged through weighted fusion and a self-adaptive Simpson algorithm, fire-fighting false alarms are effectively reduced, fire risks are accurately recognized, and the safety of an energy storage system is guaranteed.

The invention provides a personalized anesthesia management method and system. The method comprises the following steps: collecting real-time physiological data flow and previous medical record information of a patient to preliminarily evaluate operation requirements, and generating a preliminary anesthesia scheme; performing simulation prediction processing on the individualized reaction mode through a state-action value function updating mechanism by applying a deep Q network algorithm, and optimizing anesthetic selection and dosage by adopting a dynamic adaptive dosage adjustment technology to generate an optimized anesthetic scheme; the method comprises the following steps: extracting continuous physiological state parameters of a patient by applying an adaptive feedback control algorithm, presetting a safety threshold value, performing real-time comparison and analysis through a real-time parameter adjustment mechanism, performing automatic early warning processing on a trend deviating from a normal range by adopting an abnormal trend early warning technology, and generating a real-time adjustment instruction; and based on the real-time adjustment instruction, obtaining a patient recovery trajectory and subjective comfort evaluation, performing retrospective analysis, and generating a personalized anesthesia management scheme. According to the technical scheme provided by the invention, the safety of personalized anesthesia is remarkably improved.

The invention discloses a primary and secondary fusion pole-mounted intelligent circuit breaker. The primary and secondary fusion pole-mounted intelligent circuit breaker comprises a fault detection module, a protection action module, a reclosing module and an information reporting module, the fault detection module collects multi-dimensional electrical quantity through a current transformer and a voltage transformer, and judges a fault and identifies the type in combination with a dynamic normal range and a duration threshold. The protection action module calculates the fault distance based on the correction impedance, judges whether to act or not by combining the current flow direction and the protection range, and selects a protection level according to the fault severity; the reclosing module dynamically sets an observation time window, distinguishes instantaneous or permanent faults through a retest verification mechanism, and adaptively adjusts a reclosing strategy; and the information reporting module uploads fault related information. According to the method, the problems of high misjudgment rate, poor protection selectivity, rigid reclosing logic and the like of a traditional circuit breaker are solved, and the accuracy, quickness and self-healing capability of power distribution network fault processing are remarkably improved.

The invention relates to the technical field of metering pumps, in particular to an external meshing gear metering pump flow calculation method based on linear regression and curve fitting, which comprises the following steps: generating an integrated data set by monitoring the flow rate, pressure, temperature and viscosity in real time, setting flow rate, pressure and temperature thresholds based on the data set, judging the flow state and recording abnormity, and calculating the flow rate of the external meshing gear metering pump. Flow velocity and viscosity are selected for flow calculation, a corrected flow value is generated, whether flow is normal or not is analyzed and determined, a flow stability evaluation result is generated, and a visual interface is created to display parameters and a calculation result. According to the method, the multiple sensors are installed at the inlet and the outlet of the metering pump, the flow speed, the pressure, the temperature and the viscosity are monitored in real time, an integrated data set provides a precise basis for flow calculation, a threshold value is set, data are monitored, it is ensured that parameters exceeding the normal range can be recorded and analyzed in time, and the process improves the accuracy of flow calculation and improves the working efficiency. And flow evaluation is optimized, and the operation safety and efficiency are improved.

The invention discloses an AI nursing whiteboard system and method based on a large model agent, the system comprises a nursing whiteboard terminal, an AI intelligent chain platform and a database, the nursing whiteboard terminal comprises an AI nursing assistant, and a whiteboard control system is arranged in the nursing whiteboard terminal. Through the functions of integration of multi-source data, intelligent early warning, accurate question and answer and the like, various diagnosis and treatment data, such as vital signs, medical advices, nursing records and the like, of a patient can be rapidly integrated and presented in a clear and visual mode, a large amount of time and energy are saved, the patient can rapidly know the condition of the patient, and the diagnosis and treatment efficiency is improved. Vital signs and illness state changes of a patient can be monitored in real time, early warning can be immediately given out once abnormal conditions such as heart rate and blood pressure exceeding normal ranges occur, a nurse can know the abnormal conditions in the first time and take corresponding measures, the response speed to emergency situations of the patient is greatly increased, and when the nurse encounters a question in the aspect of medical knowledge, the nurse can timely take corresponding measures. Inquiry may be made by an AI care assistant.

The invention relates to the technical field of remote monitoring, in particular to an urban house safety remote intelligent monitoring system and method, and the system comprises a data collection module, a data processing module, an event-driven analysis module, an early warning notification module, a safety control module and a maintenance scheduling module. According to the invention, by comprehensively collecting temperature, humidity, pressure and photoelectric data, the environment can be monitored more comprehensively, real-time data stream comparison can not only accurately identify tiny changes in the environment, but also rapidly respond through abnormal environment indexes, and when the environment data deviates from a normal range, the system can give an alarm in time, so that the environment is protected. The early warning efficiency and accuracy are improved, the measures of emergency response are automatically adjusted according to risk assessment of different levels, the system can take the most appropriate emergency measures according to the actual situation, for example, a water spraying system is automatically started in the case of a fire, the effectiveness and accuracy of house safety monitoring are effectively improved, and the life and property safety of residents is ensured.

The invention discloses a cancer early warning management method and system based on a physical examination report, and belongs to the field of medicines.The method comprises the steps that health data of individuals are collected, and unstructured texts in the physical examination report are processed through combination of optical character recognition and a natural language processing technology; calculating a cancer risk based on a single physical examination report, evaluating a specific cancer by using a cancer risk scoring system, and analyzing whether an index exceeds a normal range or not through a rule engine; based on historical data trend prediction risks, index change rates are calculated, and an abnormal trend is analyzed and predicted in combination with a time sequence; multi-modal deep learning is utilized to predict individual cancer risks, and a Bayesian network is combined to perform joint analysis on multiple factors to generate a health management scheme; further examination is arranged according to the early warning level, and a screening strategy is provided for specific cancers. According to the method, the OCR + NLP technology is adopted, and the data quality is improved. And in combination with a multi-modal deep learning model, abnormal changes are found in advance, and early warning is realized.

The invention provides an AGV control method, an AGV control device and an AGV, and the AGV control method comprises the steps: obtaining the stability parameters of the AGV during working, the stability parameters including but not limited to the gravity center position parameter, the inclination angle parameter and the vibration frequency parameter of the AGV; sequentially carrying out gravity center stability analysis, inclination stability analysis and vibration stability analysis according to the stability parameters, and generating corresponding analysis results; and the stability state of the AGV is determined according to the analysis result, and the response action of the AGV is determined according to the stability state of the AGV. The stability parameter of the AGV during working is obtained, the stability state of the AGV is judged according to the stability parameter, and when it is determined that the stability of the AGV is poor according to the stability state of the AGV, the stability parameter corresponding to the AGV is intelligently adjusted, so that the stability parameter of the AGV is adjusted to be within the normal range, normal operation of the AGV is ensured, and the working efficiency of the AGV is improved. And the stability, the reliability and the safety of the AGV are improved.

The invention relates to the technical field of air leakage big data analysis, in particular to a compressed air pipeline air leakage intelligent analysis system and method based on LLM. A plurality of monitoring nodes in a compressed air pipeline are analyzed, and data normal range parameters and adaptive acquisition frequency are preset for each node in the pipeline operation stage; the clear definition standard avoids misjudgment of original data due to fuzzy threshold values, then data noise is effectively reduced by analyzing the data synchronization degree of each monitoring node and intelligently repairing the problems of missing, deviation and the like, the repaired data better fits the actual operation state of the compressed air pipeline, the missed detection and mispositioning risks are reduced, and the reliability of the compressed air pipeline is improved. And finally fusing the data of the gas leakage node and the adjacent node to supplement multi-dimensional information, and optimizing the fusion data quality. The LLM model depends on high-quality fusion data, deep analysis is carried out in combination with compressed air pipeline multi-monitoring-node correlation characteristics and historical experience, deviation caused by data defects is reduced, and the accuracy of an intelligent air leakage analysis result is greatly improved.

The invention relates to a pressure detection method, system and device for a breather valve of a storage tank. The method comprises the steps that key parameters used for representing the current state in the storage tank are obtained, and the key parameters are stored in a preset data record table to obtain new historical key data records; at least part of the historical key data records are input into a preset breather valve performance evaluation relation, and the abnormal degree used for representing the breather valve performance is obtained through calculation; and judging whether the abnormal degree exceeds a preset normal range, and if yes, outputting an early warning signal. By the adoption of the method, the historical key data records stored in the preset data record sheet are substituted into the preset breather valve performance evaluation relation to calculate the parameter result, and the parameter describes the current working state of the breather valve in a numerical value mode; and the abnormal degree of the breather valve is monitored by setting a warning threshold value about the abnormal degree, so that the function of monitoring whether the breather valve works normally or not in real time is realized.

The invention relates to the technical field of remote monitoring, in particular to a critically ill patient remote monitoring system and method, and the system comprises a life signal collection module, a physiological state analysis module, a remote monitoring response module and a remote intervention operation module. According to the method, the data analysis and real-time feedback process is optimized, the efficiency and accuracy of medical response are improved, the key physiological indexes deviating from the normal range can be quickly and accurately recognized and analyzed by comprehensively analyzing the instant vital sign data and the historical medical records of the patient, the prediction accuracy is improved, and the prediction efficiency is improved. Medical personnel are allowed to receive clear health risk warnings at critical moments, direct communication with a medical network is enhanced through a rapid grading and alarm system, a faster medical intervention response is achieved, meanwhile, a treatment plan can be remotely and dynamically adjusted according to the current physiological state of a patient, the treatment process is more accurate, and the treatment efficiency is improved. And the survival rate and the treatment quality of patients are greatly improved.

The invention relates to the field of anesthetic concentration regulation and control, in particular to a method for analyzing and evaluating the administration concentration of an inhalation type anesthetic. The method comprises the following steps: firstly, according to characteristic data such as basic physiological data, previous anesthesia sensitivity and operation information of a patient, applying a fuzzy matching algorithm to obtain an initial administration concentration; physiological sign data such as blood pressure, heart rate, pulse oxyhemoglobin saturation and end-expiratory carbon dioxide partial pressure of the patient are monitored in real time in the anesthesia process and compared with a normal range, whether the physiological sign data are abnormal or not is judged, and an adjustment scheme of the administration concentration is determined according to the administration concentration-physiological sign correlation model; after the administration concentration is adjusted, the electroencephalogram double-frequency index is monitored to evaluate the anesthesia depth, the anesthesia depth matching degree is analyzed to judge the adjustment effect, and therefore closed-loop control over the anesthetic administration concentration is achieved. According to the method, the administration concentration can be accurately regulated and controlled, the concentration regulation mechanism is optimized, the safety and effectiveness of the anesthesia process are effectively improved, and a reliable guarantee is provided for clinical anesthesia.

The invention discloses a power system intelligent monitoring method based on artificial intelligence, and the method comprises the steps: carrying out the real-time prediction and analysis of newly-added data through an adjusted model parameter set in combination with the core knowledge representation of historical data, obtaining an operation state evaluation value of a current time period, and judging whether a system has an abnormal fluctuation trend or not; after the abnormal fluctuation trend is obtained, extracting a key mode from the existing knowledge by utilizing a pre-established knowledge distillation framework for the part of the evaluation value exceeding the normal range, and obtaining a refined knowledge subset; according to the refined knowledge subset and the feature vector set of the newly added data, performing fine adjustment on the model, and determining an updated monitoring model; according to the method, repeated calculation of total data in a traditional method is avoided, the data processing efficiency is greatly improved, the time requirement of real-time monitoring is met, the problem that model updating is lagged in the traditional method is solved, and the problem that precision is difficult to maintain after newly-added data is introduced is solved.

The invention discloses an electric power communication network fault prediction and diagnosis system. The system comprises a data acquisition module, a fault diagnosis module and a prediction analysis module. Wherein the data acquisition module is used for acquiring monitoring data in real time and then managing the transmission rate and priority of the data by using a sliding window protocol; the fault diagnosis module is used for detecting abnormal data deviating from a normal range through an adaptive data analysis algorithm; and the prediction analysis module is used for predicting faults occurring in the system through a dynamic prediction algorithm. According to the invention, a sliding window protocol is adopted to optimize data transmission, the depth and breadth of fault diagnosis are improved through an adaptive data analysis algorithm, and the prediction accuracy is enhanced by using a dynamic prediction algorithm, so that the system response speed is accelerated, the fault diagnosis coverage is expanded, and the prediction precision is improved. And the safe and stable operation capability of the electric power communication network is obviously improved.

The invention relates to the technical field of transformers, and particularly discloses a transformer monitoring data processing method and system, and the method comprises the steps: obtaining the monitoring data of each part in a target transformer and a corresponding transformer state correlation graph; preprocessing the monitoring data; associating the preprocessed monitoring data to data nodes in a transformer state association graph; when abnormal data nodes occur, associated data nodes are tracked and acquired along causal influence edges based on the abnormal data nodes, and the abnormal data nodes are data nodes with monitoring data deviating from a preset normal range; analyzing the change characteristics of the monitoring data of the abnormal data nodes and the associated data nodes, and obtaining an abnormal mode; generating early warning information according to the abnormal mode, component nodes in the transformer state association graph and physical connection edges; according to the method, timely early warning is realized, the equipment damage risk is reduced, and the safe and stable operation level of a power system is improved.

The invention relates to the technical field of data processing, in particular to an intelligent analysis method and system for clinical test data, and the method comprises the steps: obtaining a data sequence of each physiological index of a patient, and calculating the deviation degree through quantifying the difference between a target data point and a corresponding physiological index normal range, calculating an abnormal index based on the deviation degree and the distribution characteristics of the corresponding data in the data sequence of the corresponding physiological indexes, and obtaining a fitting curve of each physiological index by fitting the abnormal index, so as to screen out time periods in which all the fitting curves are in a rising trend; and taking the physiological index pairs of which the correlation is greater than a preset value in the time period meeting the screening condition as target index pairs, constructing a causal graph by using a PC algorithm based on the target index pairs, and identifying the causal relationship of each physiological index. According to the method, the complexity of constructing the causal graph by the PC algorithm can be reduced while the important causal relationship among the physiological indexes is reserved.

The invention discloses a boric acid production material scheduling method and system based on the Internet of Things, and the method comprises the steps: obtaining multi-source data from a boric acid production line, carrying out the preprocessing of the multi-source data, and mapping the preprocessed multi-source data to a digital twinborn model; and extracting the scheduling demand of the current workshop from the initial parameters of the digital twin model and the preprocessed multi-source data vector, and constructing a multi-target scheduling model when the scheduling demand meets the workshop production demand. The turbidity, crystal particle characteristics and color change of a solution on a production line are monitored in real time through an optical system, optical monitoring data are obtained, the crystallization state and the impurity content in the solution are determined in combination with multi-source data, and scheduling execution is carried out when the crystallization state or the impurity content deviates from a normal range. The optical monitoring data and the scheduling execution condition are fed back to the digital twinborn model based on the abnormal indication, so that the scheduling strategy and the process parameters of the boric acid production line are corrected, and the intelligence and the production efficiency of boric acid production are improved.

The invention relates to the technical field of artificial intelligence, and discloses a blood glucose management system based on artificial intelligence. According to the system, blood glucose monitoring is reminded through the blood glucose monitoring terminal, a blood glucose curve is drawn every week, abnormal blood glucose conditions are judged for reminding and alarming, the diet terminal is used for guiding low-sugar diet recommendation, high-sugar food intake is avoided, the mastering degree of a patient and family members for food classification is improved, and the exercise terminal determines the exercise time and exercise amount of the patient according to the blood glucose value. Parameters are input into an intelligent system, the system converts and selects a motion mode, time and intensity, a drug treatment terminal calculates the insulin injection amount of a patient every day and every meal according to the blood glucose level and food intake, the insulin dosage is adjusted according to the calculated numerical values, blood glucose fluctuation is reduced, and the treatment effect is improved. The education and self-management terminal guides a patient to improve the cognitive degree of diabetes and provides personalized propaganda and education guidance for the patient, and the complication reminding terminal controls the blood sugar of the patient within a normal range, guides a good lifestyle of the patient and guides behavior specifications.

The invention discloses a sensor clamping mechanism and a mutual inductor crimping fault detection device and method, and relates to the technical field of mutual inductor detection. In a project verification process of an existing voltage-withstanding table and an error table, abnormal conditions such as inclination of a mutual inductor body, inaccurate tray positioning, abnormity of a magnetic switch, air leakage of an air cylinder air pipe and the like occur, so that a terminal stud of a test table cannot be in contact with a secondary side terminal of a mutual inductor to be tested; and detection interruption or false detection of a corresponding mutual inductor exists, so that the verification efficiency is reduced. According to the frame body, the measuring rod, the elastic piece and the sensor, the sensor can detect the relative displacement between the measuring rod and the frame body, if the relative displacement is within a normal range, the terminal stud is in successful crimping contact, and if the relative displacement exceeds the normal displacement range, the terminal stud has a crimping fault. Therefore, the system judges that the wiring terminal of the to-be-detected mutual inductor is failed in crimping with the wiring end, automatically identifies the crimping fault of the wiring end, and improves the operation efficiency of an assembly line.

Disclosed are a system, device, and method for predictive Artificial Intelligence (AI) warnings of likely food-induced glucose spikes, according to one embodiment. An artificial intelligence powered health system includes a continuous glucose monitoring device to capture a real-time glucose level data associated with a body of a wearer. An artificial intelligence server provides a recommendation to improve a health and a well-being of the wearer. A mobile device to generate an alert when an input of a food item consumed by the wearer and / or a behavioral activity performed by the wearer exhibits a health parameter which is greater than a predefined threshold based on a real time health information captured by the continuous glucose monitoring device. A mobile application to recommend an action to the wearer to reduce the health parameter to a normal range, according to this aspect.

The invention discloses a carbon dioxide plume monitoring-based carbon sequestration site safety assessment method. The method comprises the steps of obtaining monitoring data of each region in a carbon sequestration site in real time; monitoring data are stored in a monitoring database; when one kind of monitoring data obtained at the current monitoring moment is compared with the same kind of monitoring data at the previous monitoring moment, and the fluctuation amplitude exceeds a threshold value, the migration speed data are compared; if the migration speed data are the same, judging whether most of the other types of monitoring data do not fluctuate greatly, considering that the site is possibly abnormal when judging that most of the other types of monitoring data fluctuate greatly and the migration speed data obtained in the previous step is different, and entering the next step; acquiring permeability, porosity and bursting pressure data of the corresponding area at the current monitoring moment, judging whether the data are in a normal range or not, considering that potential safety hazards exist in the field when the data are not in the normal range, and giving an alarm. According to the invention, the site safety can be rapidly and timely evaluated accurately, the energy consumption is low, and the method is suitable for popularization.

The invention discloses a child health information tracking management method and system for a kindergarten institution, and relates to the technical field of kindergarten information management. According to the child health information tracking management method for the kindergarten institution, basic information and health data of each child are collected, the health condition of each time period is evaluated, a time period health index is generated, whether the time period health index of each child is within a normal range or not is judged in real time according to a preset health threshold value, and if the index is abnormal, the kindergarten institution is started. The method comprises the following steps of: immediately marking health abnormity, comprehensively analyzing health, intake, activity and interaction data of the whole day at the end of each day, analyzing daily health comprehensive indexes of each infant, performing health assessment, and taking corresponding health intervention measures according to a result. By continuously tracking and analyzing key health indexes such as the body temperature, the heart rate, the respiration rate and the blood oxygen saturation degree of the infant, the health index of each time period is generated in real time, and a young supporting institution is allowed to recognize potential health risks in real time in the initial stage of the problem.

The invention discloses a tooth arrangement system based on an occlusal separation angle. The tooth arrangement system comprises a registration program module, a left and right condylar process central point setting program module, an occlusal plane adjusting program module, an angle calculation program module, an opening and closing mouth movement program module, an automatic tooth arrangement program module, a dental arch line adjusting program module and a tooth movement program module, according to the tooth arrangement method based on the condylar axis occlusal separation angle, the ideal occlusal separation angle serves as the target to calculate the appropriate occlusal plane angle, the angle of the mandibular plane angle reaches the normal range, a doctor is assisted in formulating a better correction scheme, and the tooth arrangement accuracy is improved. The correction result is more stable, and the tooth occlusion function reaches the optimal state.

The invention relates to the technical field of electric digital data processing, in particular to a method, device and system for reducing the fetal heart monitoring false positive rate of a high-risk pregnant patient, and the method comprises the steps: obtaining a fetal heart rate sequential sequence and a uterine contraction pressure sequential sequence, and analyzing the degree that the fetal heart rate deviates from a normal range according to the fetal heart rate sequential sequence; determining a fetal heart abnormality index of each undetermined abnormal section; performing regularity analysis on the suspected abnormal fetal heart segments according to the uterine contraction pressure time sequence, and determining the influence degree of uterine contraction on each suspected abnormal fetal heart segment; according to the fetal heart abnormality indexes and the influence degrees of all the suspected fetal heart abnormality sections, the credibility of the fetal heart rate sequential sequence is determined by combining the ratio condition of the target moment number to the current total fetal heart monitoring moment number. By determining the credibility, the situation that the fetal heart monitoring data is misjudged to be abnormal due to interference of uterine contraction can be avoided, and then the fetal heart monitoring false positive rate of the high-risk pregnant patient is reduced.

The invention discloses an operation state prediction method and device of a building new energy system, and the method comprises the steps: obtaining various monitoring variables of a target building, inputting the various monitoring variables of the target building into a trained prediction model, and obtaining the prediction results of the various monitoring variables of the target building in a set prediction time range; based on the prediction result, evaluating the operation state of the target building: firstly calculating a basic threshold value, and then determining a dynamic threshold value according to the basic threshold value, an equipment aging factor and a seasonal factor; the prediction result is compared with a dynamic threshold value, and if the prediction result is smaller than or equal to the dynamic threshold value, the operation state of the building new energy system is a normal state; and if the prediction result is greater than the dynamic threshold value, indicating that the operation state of the building new energy system deviates from the normal interval and the building new energy system has an abnormal phenomenon.

The invention discloses a gas pressurization station remote operation and maintenance system for a double-chamber kiln, which relates to the technical field of double-chamber kiln operation and maintenance and comprises a rotating speed interval acquisition module, a track deviation range acquisition module, a fan blade fault signal generation module and a shutdown control module. The real-time deviation value and the rotating speed of the fan blades of the fan are obtained in real time, the fan blade fault signals are generated through comparative analysis, meanwhile, the early warning mechanism is triggered, when it is detected that the track deviation exceeds the normal range, the fan blade fault signals are generated, the system automatically triggers the early warning mechanism, operation and maintenance personnel are reminded to handle in time in the modes of sound-light alarm, short message notification and the like, and the operation and maintenance efficiency is improved. When the track deviation is detected to exceed a normal range, the system automatically triggers an early warning mechanism, so that operation and maintenance personnel can quickly respond to fan blade faults and can be timely notified to take corresponding measures, potential faults are effectively avoided, the reliability and stability of the system are improved, and the operation and maintenance efficiency is improved.

The present invention provides a method for diagnosing the faults of a board card based on the weighted sum of squares of the sensor signal residual sequence, which relates to the technical field of fault diagnosis of electronic controllers. The method includes: obtaining the original temperature process signals of each temperature sensor from a gas turbine electronic controller system, performing data preprocessing to form an original data set; inputting the original data set into a calculation model for the weighted sum of squares of the residual sequence; selecting the weighted sum of squares of all sensors associated with the IO board card and inputting it into a calculation model for the health degree of the IO board card to obtain the health degree value of the board card; calculating the difference between the health degree value of the board card and the boundary line of the preset normal range, and judging whether the board card has a fault according to the difference. This method can quickly diagnose the fault state of a thermocouple IO board card in an engineering manner, provide an engineering-applicable means for timely identifying the faults of the IO board card of an electronic controller, and provide important support for the fault diagnosis of the control system loop of a gas turbine.

The invention discloses a starting and stopping method for mine production line equipment. The starting and stopping method comprises the following steps: performing interlocking control and protection on electrical equipment from coarse crushing to finished product stacking and bulk packaging according to a reverse-sequence starting and sequential stopping principle; the material level is monitored through a material level sensor installed on the ore storage bin; fault shutdown and interlocking protection are implemented, and real-time state monitoring is carried out on each device; an in-situ starting cabinet or an in-situ control box is arranged beside each device, and a remote / in-situ control change-over switch is arranged; the operation condition of the equipment is monitored through a special liquid crystal display or an HMI (Human Machine Interface) of a PLC control level; the centralized control platform displays pictures, performs online monitoring on equipment data, monitors the temperature, the load and the rotating speed of the equipment, and triggers an alarm for the equipment of which the monitoring data exceed a normal range. The invention aims to realize automatic start-stop control of mine production line equipment so as to improve the efficiency, safety and reliability of mine production.