464results about How to "Optimize resource allocation" patented technology

The invention provides an intelligent post matching and employee growth path planning system and method and a storage medium. The system comprises a data acquisition and processing module, an employee portrait construction module, an intelligent post matching module and an occupational path planning module. The data acquisition and processing module collects employee performance data, education background, work experience and other information from internal and external data sources. The employee portrait construction module constructs a comprehensive portrait of each employee by using a clustering analysis and factor analysis technology; the intelligent post matching module performs intelligent matching by applying a recommendation system algorithm according to post demands and employee portraits, and finds the posts most suitable for the current capabilities and development desires of the employees; the occupational path planning module analyzes the industry development trend and future skill requirements based on the prediction model, and provides personalized occupational development paths and training suggestions for employees. According to the invention, historical performance evaluation and industry trend analysis of employees can be integrated, and comprehensive post matching and occupational planning solutions are provided.

The invention provides a remote control method and system for a new energy vehicle charging pile, and the method comprises the steps: collecting a vehicle battery state, a user demand, and the operation information of a power grid and the charging pile, and integrating the information into a standardized comprehensive state variable; dynamically adjusting the initial charging power of the single user according to the health state of the vehicle battery to obtain adjusted charging power; calculating the charging priority of the user by combining the distributed power with a dynamic weight adjustment model based on time decay; receiving a multi-user distribution strategy, and constructing a real-time adjustment variable set in combination with the operation information of the charging pile; and the power grid load change is monitored in real time. According to the invention, on one hand, a flexible solution is provided for personalized demands of users, and on the other hand, efficient utilization and active management of system resources are realized through energy sharing and remote dynamic control, so that main defects in the prior art are overcome, and the system has important technical value and application prospect.

The invention relates to a pavement structure damage detection method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring image detection data, radar detection data and sound wave detection data of a to-be-detected road surface; performing structural damage dual-channel identification on the image detection data, the radar detection data and the sound wave detection data to obtain initial structural damage detection data of the to-be-detected road surface; according to the preliminary structural damage detection data, determining physical model use guide information and physical model setting parameters corresponding to the to-be-detected road surface; and according to the physical model use guide information and the physical model setting parameters, performing mechanical analysis and fluctuation propagation analysis on the to-be-detected pavement by using each physical analysis model to obtain pavement structure damage detection data of the to-be-detected pavement. By adopting the method, the accuracy and reliability in pavement structure damage detection can be effectively improved.

The invention discloses an intelligent security and protection monitoring method and system based on image recognition. The method comprises the following steps: S1, acquiring multi-modal data; s2, performing preprocessing to generate complete target information; s3, performing real-time detection on the generated complete target information, and performing primary processing on a conventional scene; s4, based on an improved visual SLAM technology, obtaining three-dimensional information of multiple cameras; s5, when the edge device detects a potential abnormal condition, performing abnormal behavior analysis on the data by using a Swin Transform model, and optimizing a detection result in combination with the three-dimensional information of the multiple cameras; s6, performing adaptive learning on historical data by using a depth deterministic strategy gradient algorithm, and dynamically adjusting a threshold value of abnormal behavior detection; and S7, after an abnormal condition is detected, processing data through differential privacy protection and an end-to-end encryption technology, automatically controlling security and protection equipment, and giving a real-time alarm. According to the invention, image recognition and deep learning technologies are fully utilized, and accurate real-time monitoring in a complex scene is realized.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention discloses a terrain-level real-scene three-dimensional entity and element dynamic updating method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring data of a target area by an acquisition mechanism based on entity and element type priorities, and preprocessing the acquired data to obtain model data of the target area; s2, change detection: comparing newly collected model data with historical model data, and identifying a change region and type; s3, entities and elements are extracted and updated, wherein the entities and the elements of the change area are processed to obtain updated models; s4, model fusion and optimization: fusing the updated entity and element model with the original model, and realizing efficient acquisition and preprocessing of target area data by using the multi-source surveying and mapping equipment and an acquisition mechanism based on entity and element type priorities; by dividing the small regions according to the region characteristics and correcting the acquisition frequency, the resource allocation is optimized, and the pertinence and efficiency of data acquisition are improved.

The invention provides a decision-making large model-oriented multi-level heterogeneous memory collaborative scheduling method, which comprises the following steps of: acquiring hardware configuration information and initial state information of a heterogeneous memory system, constructing a burst memory access model based on an Erlang distribution family, and acquiring dynamic memory access characteristics in combination with self-adaptive stratified sampling; representing the state of the memory system by adopting multi-dimensional efficiency measurement and a hierarchical affinity matrix; predicting load characteristics based on a generalized extreme value theory, and constructing an augmented Lagrange optimization model to generate a resource allocation scheme; formulating a data migration strategy through a Pareto optimization algorithm; and dynamic parameter adjustment of Thompson sampling and an exception handling mechanism based on a combined early warning index are introduced. According to the method, memory access features can be accurately described, resource allocation is optimized, migration overhead is reduced, and large decision model training efficiency is improved.

The invention relates to the technical field of electric power measurement and control instruments, in particular to an operation method and system based on an electric power measurement and control instrument, and the method comprises the following steps: based on the electric power measurement and control instrument, remotely collecting real-time operation data of a power grid, screening and removing abnormal data, integrating historical operation data, analyzing data collected from a sensor, and obtaining an equipment fault probability index. According to the invention, real-time data remotely collected by the electric power measurement and control instrument is combined with historical data for analysis, so that fault prediction is more accurate, the probability of power grid interruption is reduced, the accuracy of real-time determination of key monitoring points and fault prediction is improved, resource allocation is optimized, the fault processing speed is accelerated, and intelligent comparison with maintenance period data is realized. The method optimizes the maintenance schedule, guarantees the efficient operation of the power equipment, effectively predicts the future load change through analyzing the difference between the current load and the predicted load, guarantees that the power grid can adapt to different operation conditions, and improves the reliability and efficiency of the whole power grid.

The invention discloses an unmanned aerial vehicle airport multi-target constraint planning and site selection method and system and electronic equipment. The planning and site selection method comprises the following steps: S1, obtaining geographic information of a target site selection area; s2, determining performance parameters of the unmanned aerial vehicle, and obtaining a flight coverage range of the unmanned aerial vehicle in combination with geographic information; s3, constructing a dynamic task point model, and determining an unmanned aerial vehicle task point set through the dynamic task point model; s4, obtaining an optimization target of airport site selection, establishing a target function according to the optimization target, constructing a site selection optimization model through the target function, and setting constraint conditions for the site selection optimization model; and S5, performing multi-target optimization calculation based on the site selection optimization model by adopting a heuristic algorithm to obtain a target layout of the unmanned aerial vehicle airport. According to the technical scheme disclosed by the invention, the navigation path can be precisely planned, the accurate navigation of the unmanned aerial vehicle in a communication limited or silent environment is realized, and the navigation reliability and the operation capability of the unmanned aerial vehicle are improved.

The invention relates to the technical field of data transmission, in particular to a meteorological data compression and transmission intelligent control system which comprises a network state evaluation module, a data priority evaluation module, a data compression and encryption module and a data transmission control module. According to the invention, network conditions can be effectively predicted and key data can be transmitted preferentially by evaluating network volatility and data emergencies, the reliability and efficiency of data transmission are improved, delay and packet loss in the data transmission process are reduced through targeted data compression and encryption processing, and the data transmission efficiency is improved. Meanwhile, the safety of data in the transmission process is guaranteed, especially in a bandwidth-limited network environment, quick and accurate transmission of key meteorological information can be guaranteed by adjusting data transmission bandwidth allocation and selecting a proper transmission protocol, resource allocation is optimized, the adaptive capacity of the system to complex network conditions is enhanced, and the service life of the system is prolonged. Therefore, the system can work efficiently under the condition that the network load is large or unstable.

The invention relates to the technical field of equipment risk management and control, and particularly discloses an equipment risk management and control system based on model prediction control, which comprises a data acquisition processing module, a data analysis module, a model construction prediction module and an analysis regulation and control module, obtaining an equipment health condition score through standardization processing and weighted calculation; the data analysis module is used for distinguishing linear and nonlinear characteristic signals according to the score change trend; the model construction and prediction module selects corresponding algorithms to establish a prediction model according to different types of signals, so as to predict the health state of the equipment in the future; the analysis regulation and control module formulates a reasonable equipment maintenance plan based on the prediction result, and dynamically adjusts the production arrangement, the equipment state is continuously monitored and intelligently analyzed, faults can be effectively prevented, the probability of occurrence of non-planned shutdown events is reduced, and finally the purpose of improving production safety and economic benefits is achieved.

The invention discloses a load balancing control method based on data traffic, and relates to the technical field of data transmission. Comprising the following steps: constructing a flow monitoring module based on data flow, constructing a self-adaptive flow classification model, dynamically allocating weights to load nodes with balanced loads, dynamically adjusting a transmission path of the flow, carrying out real-time load balancing scheduling, and processing abnormal flow. According to the load balancing control method based on the data traffic, the self-adaptive traffic classification model and the multi-path traffic reconstruction algorithm are introduced, so that the defects in the prior art are fundamentally overcome. The real-time performance, the stability and the resource utilization rate of the system are remarkably improved through deep classification of the traffic characteristics and a multi-path dynamic segmentation strategy. According to the method, resource allocation can be optimized in a complex traffic scene, the load balancing efficiency is improved, meanwhile, the service quality of delay-sensitive and bandwidth-intensive services is guaranteed, and the availability and reliability of the whole system are enhanced.

The invention discloses a power marketing knowledge graph construction method, system and device and a storage medium, and relates to the technical field of power system data management. At present, integration and application of multi-source heterogeneous data are difficult, data anomaly detection and treatment efficiency is low, and data circulation is unstable. The method comprises the following steps: collecting data of a plurality of sources, and carrying out data cleaning and standardization processing; classifying the data according to structuralization, semi-structuralization and non-structuralization, and forming feature vectors through a feature extraction method; extracting knowledge units from the feature vectors by using natural language processing and machine learning technologies, fusing knowledge from different sources through rules or models, and constructing a unified knowledge base; generating a knowledge graph in a graph database by using a knowledge base, and constructing a graph structure of nodes and edges; and carrying out real-time monitoring on data flow in the knowledge graph, and analyzing path efficiency. According to the technical scheme, the data management efficiency is improved, data circulation is monitored in real time, and the business process is optimized.

The invention relates to the technical field of medical information, in particular to a hospital pharmacy dispensing information intelligent processing method and system, and the method comprises the following steps: S1, obtaining the basic data of a medicine in real time; s2, acquiring medical record information, current medication conditions and physiological parameters of the patient from a hospital information system; s3, analyzing an existing drug combination, and detecting potential drug incompatibility; s4, a medication scheme of the patient is automatically generated; s5, pushing the medication scheme and the taboo information generated in the step S4 to work terminals of doctors and pharmacists, and providing medication suggestions at the same time; and S6, continuously monitoring the reaction of the patient, and when abnormity is monitored, performing drug adjustment through an adjustment algorithm. According to the system and the method, the medicine data is updated in real time, the patient information is integrated, and incompatibility identification and medication adjustment are carried out by utilizing an intelligent algorithm, so that the safety and the efficiency of hospital pharmaceutical dispensing are remarkably improved, and meanwhile, the individuation and the scientificity of a treatment scheme are optimized.

The invention provides an operating room high-value consumable management system based on artificial intelligence, and relates to the technical field of artificial intelligence, an intelligent sensing module collects position information, state information and use conditions of high-value consumables in real time through RFID tags and sensors, and identification and tracking of the consumables are achieved. And the artificial intelligence analysis module analyzes the use frequency of the consumables and the relevance between the operation type and the demand based on a deep learning algorithm so as to predict the future consumable demand. And the inventory management module dynamically adjusts the inventory according to the actual use condition and the predicted demand. The operation auxiliary module automatically generates a consumable list according to the operation type and the patient condition, and medical staff is reminded to prepare. And the compliance supervision module records full life cycle data of the consumables, provides a traceability report and ensures compliance. Through the technical means, the operation cost of a hospital is remarkably reduced, the operation safety and the satisfaction degree of a patient are improved, and wide application prospects and important social value are achieved.

The invention relates to the field of carbon emission management, energy distribution and optimization regulation and control, in particular to an industrial enterprise carbon emission allocation method based on regional energy and carbon platform supervision data. The method comprises the following steps: step 1, data acquisition and preprocessing, including data acquisition, data preprocessing and data analysis; 2, initial carbon quota distribution, wherein a multi-level decision-making model is established, and the initial carbon quota of an enterprise is calculated; 3, a dynamic adjustment mechanism is provided, wherein the dynamic adjustment mechanism comprises the steps of obtaining power data feedback, calculating a quota adjustment coefficient and dynamically adjusting a quota; step 4, accumulation of double-control index data, including accumulation of the double-control index data and accumulation of carbon emission intensity data; and 5, continuously optimizing the model. According to the method, the flexibility of carbon quota distribution is improved, normal production of enterprises is ensured, meanwhile, the total carbon emission control target is not exceeded, and the overall efficiency of carbon emission management is improved by introducing a multi-level decision model and a feedback mechanism.

The invention discloses a risk prediction and adaptive budget allocation system and method based on construction settlement, and the method comprises the following steps: S1, collecting historical settlement and initial budget data of a construction project, carrying out the multi-dimensional marking, and generating a structured data set; s2, processing the data by applying multi-level time sequence analysis, and identifying key levels; s3, generating an optimal quantity-price coupling relationship based on a multi-party game model, and performing preliminary tuning; s4, performing hyper-parameter optimization through a dual-domain heterogeneous population tuning mechanism; s5, analyzing the influence of external factors on the cost by using a structural transition model, and carrying out deviation analysis; s6, risk scenes under different construction situations are generated through risk simulation, and risk factors are identified and adjusted; s7, adjusting budget allocation in real time based on an adaptive complex network mechanism; and S8, a review report is generated, and multi-party collaborative review is supported. According to the method, the dynamic adjustment capability of budget allocation is improved through various optimization mechanisms, and audit transparency is improved.

The invention relates to the technical field of network advertisements, in particular to an advertisement data management system based on market trend analysis, which comprises a data acquisition module, an analysis module, a prediction module, a conversion rate calculation module and an adjustment module. The advertisement effect is reflected by acquiring click and purchase data after advertisement putting, the change trend of user behaviors is accurately recognized through the analysis module, and the user change rate is calculated, so that data support is provided for an advertisement putting strategy, and the advertisement putting strategy is more accurate by analyzing historical data, predicting the future purchase trend and comparing with predicted data in real time. The conversion rate change of the advertisement is accurately evaluated, a basis is provided for optimizing the advertisement effect, whether the advertisement putting duration needs to be adjusted or not is judged according to the comprehensive analysis of the user change rate and the conversion rate change rate, efficient utilization of advertisement resources is ensured, the accuracy and flexibility of advertisement putting are remarkably improved, the conversion rate and the return on investment of the advertisement are facilitated, and the user experience is improved. And continuous optimization and improvement of the advertisement effect are realized.

The invention discloses a distributed energy dynamic load adjusting method and system based on a virtual power plant, and relates to the technical field, and the method comprises the following steps: obtaining first data of the virtual power plant, and building a topological structure of the virtual power plant; constructing a distributed energy output prediction model based on an LSTM deep learning algorithm; combining the output of the distributed energy output prediction model and the topological structure to generate an energy scheduling scheme in real time; monitoring real-time data of the distributed energy, comparing the real-time data with the energy scheduling scheme, and performing feedback updating; according to the invention, a virtual power plant topological structure is constructed, a prediction model is constructed based on an LSTM algorithm, an energy scheduling scheme is generated in combination with model output and the topological structure, and a feedback updating technology is carried out. The problems that existing regulation and control based on a static model cannot adapt to dynamic changes of energy production and load requirements in real time, and due to the fact that an effective cooperation mechanism is lacked between distributed energy sources, energy waste is caused, and the regulation efficiency is low are solved.

The invention provides a bridge state assessment method and system based on multi-source sensing data, and relates to the technical field of bridge state assessment, and the method comprises the steps: collecting historical monitoring data at a starting point, a center and an end point of a bridge, carrying out the preprocessing of the data, and taking a mean value for subsequent assessment; and updating the state value of the bridge part through Kalman filtering to obtain acceleration and displacement data after state updating. And then, an expert scoring method is adopted to carry out state evaluation on the data, and a random forest model is constructed to carry out state prediction. The current monitoring data is evaluated through the model, if the current moment state predicted by the model is lower than the state threshold value, the abnormal index of the bridge is calculated and then compared with the preset abnormal threshold value, and if the abnormal index exceeds the standard, early warning is given out. According to the method, multi-source data are effectively integrated, the accuracy and real-time performance of bridge monitoring are improved, and a scientific basis is provided for safe operation of a bridge.

The invention relates to the technical field of optical disk storage, in particular to an optical disk box storage management system based on hierarchical management, which comprises a data analysis module, a weight calculation module, a strategy making module, a medium selection module, a dynamic adjustment module and a performance evaluation module. According to the method, the optical disk data is deeply analyzed, and the access frequency, the file number and the modification frequency of the data are recorded, so that the storage requirement and the change of a data access mode can be more effectively identified, the accurate recording and weight distribution of data attributes and the sensitivity analysis of optical disk files are realized, and a more detailed storage management strategy is constructed; storage positions and priorities can be adjusted according to actual use conditions of data, real-time data requirements are responded, a mechanism is adjusted in real time to improve response speed and operation efficiency of a system, continuous adaptability and efficiency of a storage strategy are ensured through regular performance evaluation, and reliability and safety of long-term data storage are guaranteed.

The invention relates to a construction method of a customer complaint processing system for coping with supervision complaints, which belongs to the field of customer service and supervision compliance management, and comprises the following steps: (1) establishing diversified complaint channels, and integrating the complaint channels to a unified platform; (2) complaint information is comprehensively collected and intelligently analyzed; (3) establishing a quick response and processing mechanism based on an analysis result of the step (2); (4) performing closed-loop control and continuous improvement on complaints; (5) technical support and informatization construction; and (6) personnel training and ability improvement. The method has the advantages that the processing efficiency is improved, manual intervention is reduced through the automatic processing process, and the complaint response time is shortened; (2) the processing quality is enhanced: the complaint is ensured to be professionally and timely processed by a standardized processing flow and an intelligent distribution mechanism; and (3) resource configuration optimization: tasks are intelligently allocated according to the processing capability, and waste of human resources is avoided. (4) customer satisfaction is improved, complaints are quickly responded and effectively solved, and customer credibility and loyalty are enhanced.

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

The invention provides a multi-data source plug-in computing resource scheduling method and system supporting algorithm iteration, relates to the technical field of data sources, and comprises the steps of extracting multi-dimensional features by constructing a data source feature analysis engine, generating a feature fingerprint identifier, designing an intelligent matching mechanism based on the feature fingerprint identifier, and optimizing algorithm plug-in selection. The method has the beneficial effects that the utilization efficiency of computing resources is improved, resource allocation is adjusted in real time, stable system performance is ensured, and closed-loop optimization is formed.

The invention provides an intelligent drinking water distribution path optimization method based on a mobile terminal, and the method comprises the steps: calculating an order regularity coefficient and a recovery rate, classifying users into four types, and determining the order basic priority according to the four types; and calculating a time urgency degree weight in combination with the expected order delivery time so as to determine a comprehensive priority value of the order. According to the method, real-time road conditions and deliveryman position information of a delivery area are collected, an order density thermodynamic diagram is generated, distribution area grids are adaptively divided based on the thermodynamic diagram, and a high-density area is covered with a smaller grid size. And a multi-objective optimization model is established by combining the vehicle-mounted capacity state of the deliveryman, and optimization objectives comprise weighted delivery time minimization, vehicle-mounted capacity utilization rate maximization, container recovery efficiency maximization and workload balancing. And solving the optimization model by using an ant colony algorithm, adjusting a distribution path in real time, and dynamically updating path planning according to vehicle-mounted capacity change data so as to realize efficient and flexible intelligent distribution management.

The invention relates to the technical field of power generation prediction data processing, and discloses a photovoltaic power station power generation prediction method based on multi-model fusion, which combines a photovoltaic physical model, a power generation statistical model and a power generation prediction model, not only considers a physical rule, but also combines statistical learning and machine learning to improve the stability and accuracy of prediction, and improves the prediction efficiency. And predicting the generating capacity of the photovoltaic power station by using environmental meteorological data, electrical parameters of the photovoltaic power station and historical power generation data. Accurate photovoltaic power generation power prediction is beneficial to better arrangement of power generation plans and resource scheduling of the power grid. The prediction precision and stability are improved through multi-model fusion, so that power grid dispatching is more accurate and efficient, and power grid instability factors caused by photovoltaic power generation fluctuation are reduced. Therefore, photovoltaic power generation can be connected to a power grid more stably, the power demand is met, and wide application and development of clean energy are promoted.

The invention provides an intelligent government affair question-answering method and an intelligent government affair question-answering device for enhancing a large model by combining speech recognition with a database, and relates to the technical field of intelligent government affair question-answering, comprising the following steps: S1, a speech recognition system is responsible for accurately converting a user speech instruction into a written text as a primary link of information conversion; and S2, the user intention analysis is used for accurately converting a user voice instruction into a written text, and then the text input is input into the large language model. According to the scheme, the convenience of user interaction is finally improved through the voice recognition technology, the user is allowed to rapidly interact with the system through the natural language, manual input is not needed, the information acquisition speed is increased, services are easier to access, and secondly, the natural language processing capacity of a large model is improved, and the user experience is improved. Therefore, the system can understand complex query intentions and provide accurate and humanized answers, and the naturalness and accuracy of query are enhanced.

The invention relates to the technical field of operation and maintenance control, in particular to an operation and maintenance control method and system based on an automation strategy, and the method comprises the following steps: based on server state monitoring data obtained in real time, recording the current energy consumption and load data of a data center, recognizing the stable operation state and potential faults of a server, and determining the state of the server; and performing preventive maintenance on the equipment identified as the fault risk, and generating an equipment maintenance list. According to the invention, by automatically collecting the state data of the server and carrying out real-time performance evaluation and fault prediction on operation and maintenance management, early identification and preventive maintenance of fault risks and automatic generation and adjustment of a resource configuration table are realized, the data processing capability is directly influenced, continuous operation of key applications is ensured, energy consumption is optimized, and the service life of the server is prolonged. Automatic load balancing not only optimizes resource allocation, but also reduces errors and delay caused by manual intervention, and improves operation accuracy and response speed.

The invention discloses a wind power equipment fault diagnosis optimization algorithm, and belongs to the field of wind power equipment management, and the optimization algorithm comprises the following specific steps: I, collecting and preprocessing various operation data of wind power equipment; iI, constructing an equipment fault map and carrying out feature extraction on the preprocessed data; iII, performing fault diagnosis and classification on the real-time data based on the equipment fault atlas; iV, optimizing a fault processing scheme according to the diagnosed fault and the type thereof; v, evaluating the probability of fault occurrence and formulating a corresponding early warning mechanism; according to the method, local optimum can be avoided, the effectiveness of a fault processing scheme is improved, the flexibility and adaptability of the scheme are enhanced, a feasible solution can be quickly generated, the method is suitable for large-scale data processing, the accuracy of fault probability evaluation is improved, comprehensive fault occurrence probability analysis is provided, targeted maintenance strategies are helped to be formulated, and the method is suitable for large-scale data processing. Resource configuration can be optimized, potential faults can be responded in time, the downtime of equipment is shortened, and the operation cost is reduced.

The invention discloses an energy efficiency optimization method and system for an irrigation area, a storage medium and electronic equipment, and relates to the field of intelligent irrigation. The method comprises the steps that crop information and soil information of all sub-areas in an irrigation area are obtained, and the irrigation area is communicated with branch pipelines of all the sub-areas through a main pipeline; for any sub-region, according to the crop information and the soil information, determining the irrigation amount and the irrigation priority required by crops in the sub-region; according to the irrigation amount and the irrigation priority corresponding to each sub-region, a first irrigation plan of a connected region is generated, the connected region is composed of at least two sub-regions, and the first irrigation plan comprises an irrigation mode for irrigating crops in each sub-region in the connected region in different time periods. By implementing the technical scheme provided by the invention, the overall energy efficiency optimization of the irrigation system can be realized.