2988results about How to "Improve operational reliability" patented technology

The invention provides a hydropower station auxiliary machine equipment real-time monitoring method and system based on the Internet of Things, and relates to the technical field of information, and the method comprises the steps: constructing a pressure oil pump fault diagnosis model for a temperature and vibration related feature subset, and training a temperature and vibration feature sample of a preset fault type, establishing a mapping relation between the temperature and vibration characteristics of the pressure oil pump and the fault type; on the basis of the pressure oil pump fault diagnosis model, typical faults of bearing abrasion and poor gear meshing of the pressure oil pump are classified and recognized, and the temperature and vibration fault diagnosis result of the pressure oil pump is determined through the mapping relation between the temperature and vibration characteristics of the pressure oil pump and the fault type; and according to the fault diagnosis results of the temperature and vibration of the pressure oil pump, carrying out comprehensive decision making on a plurality of diagnosis results, according to the preset confidence of different diagnosis results, giving corresponding weight coefficients, and generating a final health state evaluation result of the pressure oil pump. The operation reliability and the maintenance efficiency of the pressure oil pump are improved.

The invention relates to a real-time fault detection and automatic repair system for a power grid. The real-time fault detection and automatic repair system comprises a multi-level sensing module, a data processing module, an automatic repair module and a monitoring and logging module. The multi-level sensing module collects data such as voltage, current, frequency and temperature in operation of a power grid by deploying multiple types of sensors, and sends the data to the data processing module. And the data processing module analyzes the collected data through an embedded intelligent algorithm, identifies potential faults and generates fault diagnosis results. And the automatic repair module executes repair operations of isolating a fault area, reconstructing a power grid topological structure and recovering normal power supply according to a fault diagnosis result. The monitoring and log module records the operation state of the power grid and the fault diagnosis and repair process, and provides fault analysis and performance evaluation functions through a user interface. According to the system, real-time detection, accurate positioning and automatic repair of power grid faults and comprehensive monitoring of operation data are realized, and the safety and reliability of power grid operation are remarkably improved.

The invention provides a prediction model construction method and system for personalized equipment operation and maintenance, and relates to the technical field of data processing, and the method comprises the steps: collecting equipment operation data and environment data, extracting the change characteristics of the equipment operation state, carrying out the correlation analysis of the environment data based on historical equipment fault records, and obtaining a prediction model. Performing normalization processing, performing time sequence analysis, generating a joint feature matrix, segmenting the joint feature matrix into time slices of a fixed window according to a time sequence, classifying the time slices into a normal state and an abnormal state, performing feature contribution degree analysis, generating a feature weight matrix, and performing feature extraction; re-combining the equipment features in the time slice feature data set with the environment factors according to feature weights, generating fusion feature data layer by layer, performing supervised learning training on the fusion feature data, constructing an equipment prediction model, and outputting the equipment prediction model as a prediction model of personalized equipment operation and maintenance; according to the invention, the operation and maintenance effect of personalized equipment is improved.

The invention provides a power grid fault detection and positioning method and system for an intelligent power grid system. The method comprises the following steps: collecting a fault traveling wave signal when a power distribution line has a fault; preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; carrying out traveling wave front edge detection on the target fault traveling wave signal, and determining the signal arrival time of the fault traveling wave signal; fault signal features are extracted from the target fault traveling wave signals through a frequency domain analysis method and a time domain analysis method; estimating the fault line characteristics of the fault line with the fault in combination with the fault signal characteristics and the signal arrival time; outputting the line type and the fault type of the fault line through a feature recognition model; selecting an optimal fault positioning algorithm by using a decision tree algorithm based on the line type and the fault type; and determining the line position of the fault line according to the target fault traveling wave signal by adopting an optimal fault positioning algorithm. The method has the effect of intelligently selecting the optimal positioning algorithm according to the line type.

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 discloses a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate in combination with a space-time diagram neural network and reinforced learning, and relates to a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate. The method comprises the following steps of: 1, collecting and cleaning data; 2, constructing a graph data set; 3, training the ST-GNN model; 4, constructing a deep Q reinforcement learning model; 5, the trained ST-GNN model is embedded into an automatic control system of a sewage treatment plant, and the model outputs the optimal aeration rate of the current time step and predicts the micro-aerobic hydrolytic acidification effect through interaction of the current working condition and historical data; and adjusting operation parameters of aeration equipment according to the output of the model, and carrying out hydrolytic acidification treatment. The method is high in regulation and control efficiency and low in operation error, and can be applied to the field of digital and intelligent management of sewage treatment plants.

The invention relates to an optical storage cooperative control optimization method, electronic equipment and a medium. The method comprises the steps of collecting historical energy consumption and photovoltaic data of a park, constructing a photovoltaic electronic model based on the data, performing day-ahead prediction by adopting CNN-BiGRU, and generating a load power and photovoltaic generation power sequence; calculating the electricity consumption and the photovoltaic generating capacity of each time period; setting the capacity of an energy storage module, and calculating the energy storage charging and discharging capacity based on an SOC formula; according to the electricity consumption, the generating capacity and the energy storage capacity, determining a peak section discharge demand; photovoltaic fluctuation constraint is combined, and energy storage charging and discharging power is adjusted to stabilize fluctuation; optimizing load power utilization, photovoltaic power generation and energy storage power scheduling based on the power balance relation; and constructing a revenue maximization objective function, establishing a light storage collaborative optimization model in combination with constraints, and solving an optimal operation scheme of a prediction day. According to the method, photovoltaic power generation errors are reduced, power output is balanced, power grid requirements are met, the photovoltaic consumption rate is improved, healthy operation of the system is guaranteed, and operation benefits are improved.

The invention discloses a non-energy-storage off-grid photovoltaic hydrogen production system and a control method. The control method comprises five steps of environmental data acquisition and preprocessing, photovoltaic cell output power estimation, electrolytic cell power management, converter control parameter optimization and system performance evaluation and adaptive adjustment. The method comprises the following steps: firstly, processing environmental data by adopting an isolation forest algorithm, Kriging interpolation and an empirical mode decomposition method, and extracting time sequence features; and then combining with the estimated photovoltaic output power. And then the genetic algorithm and dynamic programming are utilized to realize electrolytic cell power optimization distribution. And then converter control parameters are optimized through subspace identification, pole assignment and a hybrid optimization algorithm. And finally, system performance evaluation and adaptive adjustment are realized by adopting a sliding time window method, singular spectrum analysis and a fuzzy prediction control method. The technical problems of the energy-storage-free photovoltaic hydrogen production system in the aspects of environmental data processing, power estimation, energy management, control optimization and the like are solved, and the operation efficiency and reliability of the system are improved.

The invention relates to the technical field of power grid fault repair, in particular to a power grid fault emergency rescue method. The method comprises the following steps: acquiring topological structure data, key equipment parameters and real-time operation state data of a power grid; constructing a power grid digital twin model according to the power grid topological structure data, the key equipment parameters and the real-time operation state data, and performing real-time state evaluation to obtain fault risk early warning data and real-time fault condition data; when the real-time fault condition data shows that a fault occurs, performing fault point positioning and fault type determination based on deep learning according to the fault risk early warning data and the real-time operation state data to obtain fault diagnosis data; and constructing a fault influence range model according to the fault diagnosis data and a preset FACTS and HVDC characteristic model. According to the method, deep learning, a graph theory and a multi-objective optimization technology are fused, and intelligent fault identification, influence range modeling and accurate emergency disposal scheme optimization are realized.

The invention provides an electric power multi-modal data perception and digital twinborn simulation cooperative interaction method and system, and relates to the field of electric power equipment management and maintenance, and the method comprises the steps: carrying out the electric power multi-modal data perception of data collected by edge equipment, and obtaining the state characteristics of single equipment; constructing a digital twinborn model based on the state features of all devices in the to-be-monitored power system; constructing a power equipment graph knowledge base by using a digital twinborn model, and extracting global state features of the equipment from the power equipment graph knowledge base through a graph neural network so as to construct a global state feature time sequence of the equipment; inputting the global state feature time sequence into the trained time sequence prediction model, and predicting the equipment state at the future moment; according to the multi-modal data, parameters of the digital twin model are adjusted in real time, environmental changes are flexibly adapted, time sequence modeling and fault trend analysis functions are combined, the running state of the equipment is predicted, and the comprehensiveness and foresight of equipment monitoring are further improved.

The invention belongs to the technical field of battery fault prediction, and relates to a storage battery health state assessment method, system and device and a medium. The method comprises the following steps: acquiring key operation parameters of the storage battery; calculating storage battery health state evaluation index parameters by using the key operation parameters; the storage battery health state evaluation index parameters comprise the storage battery internal resistance, the internal resistance change rate, the storage battery charging state, the storage battery operation age limit and the storage battery operation temperature; preprocessing the storage battery health state evaluation index parameters to obtain preprocessed health state evaluation data; inputting the preprocessed health state evaluation data into a constructed health degree evaluation model to obtain a storage battery health degree representing the health state of the storage battery; and performing trend dynamic prediction on the health degree of the storage battery through a time sequence analysis algorithm to obtain a trend prediction result. The problem that an existing method is not accurate enough in battery fault prediction is solved.

The invention relates to the technical field of new energy grid-connected converter control, in particular to a grid-connected converter control method based on grid-following and grid-construction hybrid control, which flexibly adjusts the characteristic proportion of grid-following and grid-construction by constructing a self-adaptive hybrid synchronous control model, ensures the smooth and efficient grid-connected process, and improves the grid-connected efficiency. A voltage and current double-loop control model is constructed to accurately regulate and control output voltage and current, the high-standard electric energy quality requirement of a power grid for new energy power generation is met, meanwhile, power grid impedance estimation is introduced for virtual impedance control, and the anti-interference capability and the response speed of the system are further enhanced. In conclusion, according to the grid-connected converter control method based on grid following and grid construction hybrid control, the adaptability and stability of the system to the complex power grid environment are remarkably improved, and the overall operation efficiency and reliability of the system are improved.

The invention belongs to the technical field of battery monitoring, and discloses a lithium battery thermal runaway early warning system and method based on multi-source parameter monitoring. The method comprises the following steps: collecting multi-source battery parameters in real time, and marking the multi-source battery parameters as real-time multi-source battery parameters; the real-time multi-source battery parameters are processed, and battery characteristic parameters are extracted; performing fusion analysis on the real-time multi-source battery parameters and the extracted battery characteristic parameters, and predicting the thermal runaway probability of the lithium battery; judging whether to generate an evaluation instruction according to the thermal runaway probability; if the evaluation instruction is generated, evaluating the thermal runaway type of the lithium battery and starting active prevention and control measures; according to the invention, high-precision and timely monitoring and early warning of lithium battery thermal runaway accidents are realized, the thermal runaway risk is reduced, and the safety and reliability of lithium battery operation are effectively improved, so that the life safety of new energy automobile users is protected.

The invention discloses an intelligent data acquisition system of a special pump, and relates to the technical field of equipment management. Comprising a data acquisition module, a data preprocessing module, a data storage module, a data analysis and prediction module, an intelligent control module, a remote monitoring module, a maintenance and optimization module, a multi-source data fusion module, a digital twinborn simulation module and a collaboration module. According to the intelligent data acquisition system of the special pump, by arranging multi-dimensional data acquisition and an intelligent optimization model based on the self-adaptive neural network and reinforcement learning, the defects that in an existing system, data integration is not comprehensive, a maintenance decision is lagged and intelligent optimization is lacked are overcome, and real-time monitoring and dynamic adjustment of the equipment state are achieved. The system can accurately predict the equipment fault, optimize the maintenance strategy, significantly reduce the operation cost, and improve the operation efficiency and reliability of the equipment.

The invention provides an online monitoring analysis method and system for hydropower station pump equipment, and relates to the technical field of information, and the method comprises the steps: judging that a bolt group has a fatigue failure risk when a fatigue damage degree D value reaches a preset critical threshold value, and comprehensively evaluating the health state of the bolt group in combination with bolt pre-tightening force monitoring data and a loosening failure critical threshold value of the bolt group, obtaining a residual service life prediction result of the bolt group; and by analyzing the stress nephogram and the strain nephogram of the stress concentration area around the bolt hole and the residual service life prediction result, a topological optimization and parameter optimization method is adopted to carry out optimization design on the structural arrangement and the pre-tightening force application scheme of the bolt group, and optimal bolt group design parameters for prolonging the fatigue life are obtained. Accurate prediction and optimal design of the fatigue life of the bolt group are realized, and the operation reliability and safety of the pump are improved.

The invention discloses a signal processing and intelligent algorithm fused main helium fan residual life prediction method, and relates to the technical field of mechanical equipment state monitoring and maintenance management, and the method comprises the steps: collecting equipment operation data through multiple sensors, carrying out the feature fusion through principal component analysis, and achieving the fault mode recognition through a deep learning algorithm; and the residual life of the equipment is predicted by utilizing transfer learning and an XGBoost algorithm, and a diagnosis and prediction model is integrated into an online monitoring system, so that real-time perception and dynamic prediction of the health state of the equipment are realized, and decision support for maintenance and spare part management is provided. According to the method, the accuracy of fault diagnosis and life prediction of the primary helium fan is effectively improved, and a new thought is provided for safe operation and intelligent maintenance of equipment.

The invention belongs to the technical field of fault diagnosis, and discloses a photovoltaic power generation equipment fault diagnosis method and system based on artificial intelligence. The method comprises the following steps: acquiring a plurality of historical comprehensive monitoring data based on a cloud data center, and preprocessing the historical comprehensive monitoring data; carrying out clustering processing; using an artificial intelligence algorithm and a photovoltaic power generation equipment fault diagnosis model; using a reinforcement learning algorithm; acquiring real-time comprehensive monitoring data based on an equipment monitoring device; dividing real-time power generation time periods; performing fault diagnosis by using the matched photovoltaic power generation equipment fault diagnosis model; generating a real-time alarm signal according to the real-time fault diagnosis result; and generating an operation optimization strategy by using the photovoltaic power generation equipment operation optimization model, and returning the operation optimization strategy to the equipment control device of the corresponding photovoltaic power generation equipment. According to the method, the problems of low fault diagnosis efficiency and accuracy, insufficient real-time response capability and poor analysis comprehensiveness in the prior art are solved.

The invention relates to the technical field of industrial internet data acquisition, in particular to a data acquisition method and system based on an industrial internet operating system, and the method comprises the following steps: S1, detecting and recognizing data sources of a plurality of industrial devices and sensors; s2, collecting original data from a data source in real time, and preprocessing the original data; s3, distributing part of calculation tasks to a plurality of edge nodes; s4, storing the data in a distributed database or cloud storage in a classified manner; s5, performing fragmentation processing on the data in the cache; s6, performing data synchronization and consistency verification; and S7, converting the data into a corresponding format. According to the invention, by implementing efficient data fragmentation processing, a dynamic storage strategy and secure data consistency verification, the processing speed and storage efficiency of the industrial internet data acquisition system and the security and integrity of data are remarkably improved, and the requirements of high efficiency and high reliability of modern industrial production are effectively supported.

The invention relates to the technical field of data processing, and discloses an operation mode switching control method and device for an offshore wind turbine generator. The method comprises the following steps: carrying out model classification on historical operation data of a unit to obtain operation mode classification data and a characteristic parameter library; performing reinforcement learning analysis on the current state evaluation data and the short-term prediction data of the unit to obtain a target operation mode; performing fuzzy control processing on the target operation mode to obtain a mode switching strategy and a parameter adjustment sequence; performing multi-objective optimization and cooperative control analysis on the mode switching strategy and the parameter adjustment sequence to obtain accurate control signals of each actuator in the target offshore wind turbine generator; and generating an operation mode switching control strategy according to the accurate control signal of each actuator, and performing mode switching control on the target offshore wind turbine generator through the mode switching control strategy. According to the invention, the efficiency and accuracy of the operation mode switching control of the offshore wind turbine generator are improved.

The invention discloses a regional power grid new energy consumption prediction and decision support system, which relates to the technical field of smart power grids and new energy, and comprises a data acquisition unit, a characteristic analysis unit, a prediction model construction unit, a decision generation unit and an interactive display unit, multi-source data such as new energy power generation, meteorological information and power grid operation are collected in real time through the data collection unit, key features are extracted through the feature analysis unit, a solid foundation is provided for prediction model construction, the prediction model constructed through the quantum fusion algorithm can accurately predict the new energy power generation capacity and the power grid load, and the prediction efficiency is improved. Meanwhile, the decision generation unit can quickly generate an optimal new energy consumption decision scheme based on a dynamic fuzzy neural network technology, effectively cope with power grid fluctuation and ensure stable operation of a power grid, and the functions jointly improve the utilization rate of new energy and the overall efficiency of the power grid.

The invention provides an outer rotor liquid cooling motor and an aircraft. The outer rotor liquid cooling motor comprises a power module, a first heat dissipation module and a second heat dissipation module. The outer rotor liquid cooling motor is provided with a first channel and a second channel, the suction pump is used for sucking cooling liquid, the cooling liquid flows to the stator and the first channel through the liquid guide pipe, and the cooling liquid is used for absorbing heat of the stator; the air outlet communicates with the second channel and is used for forming cooling airflow, and the cooling airflow sequentially flows through the converter, the second channel and the air outlet and is used for cooling the cooling liquid. The first heat dissipation module and the second heat dissipation module are arranged in the outer rotor liquid cooling motor, a cooling liquid circulation system and a cooling gas circulation system are formed, the stator is cooled through cooling liquid, the cooling liquid is cooled through cooling air flow, meanwhile, the cooling air flow can further penetrate through the interior of the outer rotor liquid cooling motor, and the cooling effect is improved. Heat energy in the outer rotor liquid cooling motor is taken away, and the heat dissipation efficiency of the outer rotor liquid cooling motor is improved.

The invention relates to the technical field of wind power integration control, and discloses a wind power integration system safe operation method aiming at subsynchronous oscillation and a system thereof. The method comprises the following steps: performing time-frequency feature extraction processing on an original data set to obtain an oscillation mode feature quantity; performing stability analysis processing on the oscillation modal characteristic quantity to obtain a subsynchronous oscillation risk early warning signal; performing multi-time-scale decoupling processing on the sub-synchronous oscillation risk early warning signal to obtain a mechanical-electrical coordination control instruction; performing task decomposition processing on the coordination control instruction to obtain a real-time control quantity of each execution mechanism; and performing evaluation processing and correction on the execution effect of the real-time control quantity of each execution mechanism to obtain a correction control parameter set, and performing real-time control on the wind power integration system through the correction control parameter set. According to the invention, coordinated control of the electrical subsystem and the mechanical subsystem of the wind power grid-connected system is realized, subsynchronous oscillation is effectively suppressed, and the operation stability of the system is improved.

The invention discloses a real-model experiment distribution line fault positioning method and system. The method comprises the following steps: acquiring a topological structure diagram of a target power distribution network in a real-model experiment; based on the topological structure diagram of the target power distribution network, combining a preset diagram input feature selection logic, and obtaining target power distribution network node and edge features as input of a power distribution network fault model to be established; according to the input of the to-be-established power distribution network fault model and the output of a preset power distribution network fault model, training the power distribution network fault model based on the improved graph neural network, the output of the preset power distribution network fault model being the probability of the line fault in the power distribution network; and according to the trained power distribution network fault model based on the improved graph neural network, real-type experiment power distribution line fault positioning is completed. The system and the method have good expansibility and adaptability, can be suitable for power distribution networks of different scales and structures, and have wide application prospects.

The invention discloses a wireless communication network data secure transmission method and system, and relates to the technical field of secure transmission. During system operation, agricultural monitoring data are collected in real time through various sensor devices arranged in an agricultural environment, encryption processing is performed by using a symmetric encryption algorithm, and a dynamic key length adjustment mechanism is supported; a random number generator is integrated, unified management is performed on key generation, distribution, updating and destruction in a system, a timestamp or a random serial number is embedded into each data packet, the uniqueness of the timestamp or the serial number is verified at a receiving end, so that replay attacks are resisted, and the data are comprehensively analyzed and evaluated according to a set algorithm, so that the replay attack is prevented. The method comprises the following steps: acquiring a security data transmission performance index SDTP through calculation, generating a monitoring report, detecting abnormal conditions in a data transmission process, including channel quality reduction, data loss or verification failure, and providing a visual monitoring result.

The invention discloses a medium and low voltage real type power distribution network defect simulation system and a test method thereof, and relates to the technical field of power system fault diagnosis and recovery, and the method comprises the steps: deploying a sensor to collect power distribution network operation data, carrying out the preprocessing, integrating a power distribution network data set, extracting a power distribution network fault feature vector from the power distribution network data set through FFT conversion, and carrying out the fault diagnosis and recovery; the method comprises the following steps: constructing a fault diagnosis model by using a support vector machine (SVM) based on a power distribution network fault feature vector, establishing a simulation model of a medium and low voltage power distribution network by using power system simulation software PSCAD, simulating a power distribution network fault in the simulation model, diagnosing the fault in the simulation model by using the fault diagnosis model, and formulating and implementing a recovery strategy according to a diagnosis result. Evaluating the effect of the recovery strategy, adjusting the recovery strategy according to the evaluation result, repeatedly testing in the simulation environment until the optimal recovery strategy is found, applying the optimal recovery strategy to the actual power distribution network, and continuously monitoring and improving the performance of the actual power distribution network.

The invention provides an AI-based servo driver fault on-line rapid diagnosis method and system, and the method achieves the quantitative evaluation of fault data through obtaining the candidate system fault monitoring data of a servo driver control system, and converting the candidate system fault monitoring data into a diagnosis difficulty index and a fault description knowledge attribute. When the diagnosis difficulty index exceeds the preset index and the fault data lacks dynamic change information, the method can further utilize a plurality of fault penetration derivation data and the associated fault knowledge networks matched with the data, and effectively identifies and integrates similar fault modes by calculating the state feature distance between the associated fault knowledge networks, thereby improving the fault diagnosis accuracy. Compared with the prior art, the method has the advantages that the accuracy and efficiency of fault diagnosis are remarkably improved, particularly in a complex and changeable fault scene, the fault reason can be quickly positioned, and data support is provided for instant maintenance and recovery of the servo driver, so that the downtime of a system is shortened, and the overall operation reliability and stability of equipment are improved.

The invention relates to the field of household kitchens, and discloses a power plant pneumatic ash conveying pipeline abrasion reduction and energy consumption optimization method based on real-time monitoring, which comprises the following steps: S1, arranging a sensor group at a node of an ash conveying pipeline; s2, preprocessing the sensor data through a data processing system; s3, constructing a wear prediction model based on the preprocessed data; s4, based on the pipeline abrasion trend, the airflow speed and pressure are dynamically adjusted; s5, adjusting the equipment power according to the influence of the speed and pressure of the adjusted airflow on the sensor data; s6, feeding back the data of each step to a control system; and S7, establishing a pipeline three-dimensional model. Through real-time collection of pipeline operation state data, data processing and a wear prediction model, the airflow speed and pressure are dynamically adjusted, and the equipment power is adaptively adjusted according to the change of the airflow, so that the wear of the pipeline can be obviously slowed down while the conveying efficiency is kept, and the service life of the pipeline is prolonged.

The invention discloses an energy-saving control method and device for a gas compressor and a medium. The method comprises the steps that historical operation data of a gas compressor are collected, and the historical operation data comprise compressor operation parameters, environment parameters, gas parameters and pipe network operation parameters; training a compressor performance model, a load prediction model and an energy efficiency optimization model according to the preprocessed historical operation data; performing operation data prediction according to the collected real-time operation data by using a compressor performance model, a load prediction model and an energy efficiency optimization model to obtain predicted operation data in a future predetermined time period; calculating the predicted operation data by adopting a non-dominated sorting genetic algorithm, and determining an optimal operation scheme; and the control parameters of the optimal operation scheme are issued to control systems of all the compressors for energy-saving control over the compressors.

The invention discloses a method, a system and equipment for monitoring weak links of a power distribution network and a storage medium. Comprises: obtaining operation related data of a target power distribution network; performing preliminary state evaluation on each node of the target power distribution network based on the operation related data to generate a preliminary state matrix; and weak link positioning is carried out based on the initial state matrix to obtain weak link data, and an emergency response strategy of the target power distribution network is generated according to the weak link data. Weak link positioning is performed through the initial state matrix to obtain weak link data, so that the part with problems in the power distribution network can be quickly and accurately found out, and the problem finding efficiency is improved. The emergency response strategy is generated according to the weak link data, so that effective countermeasures can be taken quickly when the power distribution network breaks down or is abnormal, the overall operation reliability of the power distribution network is improved, and the stability of power supply is guaranteed. Changes of the power distribution network can be dynamically tracked, and continuously changing power grid operation environments and requirements are adapted.

The invention discloses a bridge pier stud crack automatic detection system based on a wall-climbing robot, and relates to the technical field of crack detection, and the system realizes closed-loop operation from accurate crack detection to intelligent repair through a crack detection module and a repair material intelligent spraying detection module. The crack detection module uses a multispectral imaging device, a thermal imaging camera and a laser scanning technology, can collect a multispectral difference value G (x, y), surface temperature distribution T (x, y) and a depth matrix D (x, y) of a crack area, extracts a crack boundary B (x, y) through a high-precision algorithm, and calculates a crack volume Vc. And the repairing material intelligent spraying detection module dynamically calculates the volume Vm of the repairing material according to the volume of the crack, and the SLAM technology is utilized to optimize the spraying path and parameters, so that uniform coverage and efficient utilization of the repairing material are ensured. The system greatly improves the precision of crack detection and repair, and avoids material waste and repair omission.