1029 results about "Device parameters" patented technology

The invention provides a traffic comprehensive query optimization method and system applied to intelligent human-computer interaction, and the method comprises the steps: obtaining a traffic query request input by a target user in an interaction scene, carrying out the multi-modal feature analysis of the traffic query request, generating a semantic understanding feature and a scene correlation feature, and obtaining a semantic understanding feature and a scene correlation feature based on a preset language large model; performing dynamic interaction feature fusion processing on the semantic understanding feature and the scene association feature to generate a multi-dimensional intention feature, and performing service path optimization on the traffic query request of the target user according to the multi-dimensional intention feature to generate a comprehensive response strategy; and converting the comprehensive response strategy into interactive feedback information, dynamically adapting to equipment parameters in the real-time environment association information, and executing feedback operation to complete optimized response of the traffic query request. According to the invention, the response real-time performance, decision reasonability and execution reliability of the intelligent traffic query service can be improved.

The invention relates to the technical field of data analysis, in particular to a new energy power generation equipment health management platform based on a large model, which comprises a data acquisition and perception layer, an edge computing layer, a cloud processing layer and an application service layer, compared with the prior art that static historical data or single equipment parameters are adopted as a health detection reference, and the influence of environment dynamic change and equipment aging cannot be reflected, the scheme adopts a multi-dimensional simulation modeling technology, equipment parameters, weather parameters and other real-time working condition data are integrated to construct a digital twinborn model, and the digital twinborn model can be used for real-time health detection. Dynamic health reference values including generating capacity, instantaneous current / voltage, equipment temperature and the like are generated by simulating equipment operation states (such as photovoltaic efficiency attenuation at an extreme temperature and aerodynamic load of a fan in a salt mist environment) in different scenes. The method can accurately capture the interaction effect of the environment and the equipment, enables the health detection threshold to be dynamically adjusted along with the working condition, and improves the anomaly recognition accuracy by more than 35% compared with a traditional method.

The invention discloses a laboratory automatic process management and multi-source data fusion system based on the Internet of Things, and belongs to the technical field of laboratory management. The system comprises a sensing layer, an analysis layer, an intelligent management layer and an application layer. The sensing layer carries out global data acquisition and real-time analysis; the analysis layer integrates data, mines an association relationship and optimizes task matching; the intelligent management layer performs full-process automatic management and control on a laboratory, and comprises an equipment cooperation module, a dynamic detection module, a dynamic configuration module, a real-time simulation module, a preset trigger module and an intelligent auditing module; and the application layer performs multi-terminal collaborative interaction and intelligent assistant assistance processing. According to the system, intelligent collaboration of laboratory equipment, automatic detection of samples, dynamic optimization of equipment parameters and automatic early warning and processing of abnormal conditions are realized through technical means such as knowledge graph construction, a digital twinborn technology and multi-mode biological recognition, and the laboratory management efficiency and the data processing accuracy are effectively improved.

The invention relates to the technical field of equipment monitoring, in particular to a method and a system for collecting and monitoring production data of automotive trim injection molding equipment. Comprising the following steps: firstly, acquiring real-time operation state data from injection molding equipment through a communication node, generating an equipment state data set, and constructing a parameter coupling model through a time sequence analysis method; when the temperature parameter fluctuation exceeds a preset threshold value, fusing the pressure data to generate optimal configuration, transmitting the optimal configuration to related equipment through an interactive network, and determining a linkage parameter set; thirdly, the linkage parameter set is processed through a classification method, a deviation index is obtained, a synchronization instruction is generated according to deviation, it is ensured that beats between the devices are consistent, and a unified production cycle is formed; and finally, through the prediction model, extracting production cycle data, predicting parameter fluctuation, adjusting equipment parameters, and determining final process configuration. The problems of beat deviation and parameter fluctuation between injection molding equipment in production are solved, the production process is optimized, and the production efficiency and the product quality are improved.

The invention discloses a photovoltaic power station operation data monitoring method and system, and relates to the technical field of photovoltaic power station equipment monitoring, and the method comprises the steps: collecting equipment operation parameters to form an original data set; constructing an equipment association network by taking the equipment as a node; based on the data set and the association network, time sequence collaborative change and correlation analysis are carried out on the equipment parameters, and a collaborative change model is established; judging that equipment is abnormal through a model, and calling same-type parameters of directly connected nodes to form a to-be-verified set; in combination with the network connection edge weight, calculating the influence degree of the associated parameters on the abnormal equipment by applying a dynamic weight evaluation algorithm, and generating a priority list; and performing iterative optimization on the collaborative change model according to the priority list, and outputting a monitoring report. The system comprises a data acquisition module, a network construction module, an analysis modeling module, an anomaly judgment module, a weight evaluation module, a model optimization module, a data storage module and the like to realize abnormal associated factor identification and monitoring logic optimization.

The invention relates to the technical field of data visualization, in particular to a digital twinning-oriented workshop data visualization system, which comprises a heterogeneous data space-time stamp reconstruction module for acquiring a sensor data stream, an MES service data packet and an environment monitoring data set and generating a space-time aligned fusion data frame; the dynamic topology compensation modeling module is used for constructing a digital twin body with a time sequence attribute and outputting a dynamic topology relation matrix comprising a geometric deviation compensation value; the event-driven incremental rendering engine module is used for generating a visual model only updating a change part by adopting a difference region recognition technology, and outputting a three-dimensional visual interface with an abnormal coordinate mark; and the bidirectional feedback control channel module is used for analyzing the abnormal coordinate mark, generating an equipment parameter adjustment instruction set through a reverse path tracking algorithm, and transmitting the equipment parameter adjustment instruction set back to the physical equipment controller. According to the invention, a bidirectional feedback closed loop with a physical equipment controller is realized, and dynamic adjustment and adaptive control are supported.

The invention belongs to the technical field of automatic control, and discloses an automatic equipment control method and system based on multi-source data. Comprising the steps of collecting multi-source data of an industrial equipment group in a mining area, integrating the multi-source data into multi-equipment operation data, and preprocessing the multi-equipment operation data to obtain basic equipment operation data; constructing a multi-modal hybrid model based on the basic equipment operation data, updating the multi-modal hybrid model, and performing equipment health state evaluation on each industrial equipment by using the updated multi-modal hybrid model to obtain comprehensive risk data; constructing a deep learning framework, generating an equipment control strategy priority based on the comprehensive risk data and the deep learning framework, and generating an instruction sequence based on the equipment control strategy priority; performing dynamic parameter adjustment on each industrial device in the industrial device group based on the instruction sequence to obtain an optimal operation parameter combination; sending the optimal operation parameter combination to a preset mining area equipment parameter database; and automatic control of each device is realized.

The invention relates to a full-life-cycle digital passport management system and method for electromechanical equipment, which take dynamic data in an operation and maintenance stage as a core and depend on linkage of an intelligent algorithm and full-cycle data. The problems of equipment parameter deviation, performance degradation and the like can be accurately identified based on five-dimensional full-period data integrated by a data passport construction module; and a fault influence range and associated equipment are positioned according to physical, logic and power dependency relationships presented by the equipment dependency graph construction module, so that a basis is provided for decision correction. Besides, through unique identification of a gene coding module, historical data and responsibility subjects of equipment can be traced, BIM model dynamic updating, feedback design and construction stage data verification optimization are driven, and a closed loop of operation and maintenance feedback-model correction-full-cycle data iteration is formed. The module solves the problems of traditional operation and maintenance data isolation and model static state, enhances the timeliness and accuracy of block chain evidence storage data, and supports applications such as intelligent service layer predictive maintenance and fault self-repairing.

The invention discloses a thermal power generating unit operation state detection method and system, and relates to the technical field of operation state detection, and the method comprises the steps: S100, building a working condition variable label; s200, carrying out statistics on correlation coefficients of sensors and faults under various working conditions in historical operation data of the thermal power generating unit, dynamically updating weights, and storing the weights in a working condition database; s300, based on the energy transfer chain of the thermal power generating unit, establishing a parameter incidence matrix between devices; according to the method, the data weights of different sensors are adaptively adjusted according to the real-time operation state of the equipment, the defect that the traditional multi-sensor fusion adopts fixed weight or simple weighted average and is difficult to adapt to the working condition change is overcome, a physical relationship network between the equipment is constructed, key associated parameters are identified in an energy transfer form, and the fault identification accuracy is improved. The robustness of anomaly monitoring under complex working conditions is improved, and the false alarm rate is reduced.

The invention is suitable for the technical field of smart beam fields, and provides a design optimization method and system for a highway smart beam field based on big data, and the method comprises the steps: collecting the related data of transportation equipment through an Internet of Things and a real-time bidirectional data interaction channel, and constructing and dynamically updating a site three-dimensional digital twin model; fusing the model and adjacent node broadcast information, and outputting an optimal real-time driving path by using an improved pheromone diffusion algorithm; setting a hierarchical processing protocol for tasks with different priorities, and recalculating an optimal path sequence and adjusting task allocation when resource conflicts or path deadlocks are detected; identifying potential risks in combination with frequency domain analysis, and dynamically adjusting paths and equipment parameters; the algorithm is continuously optimized through offline training and online learning of a historical database. The system comprises a model building and updating module, a path selection module, a conflict detection module, a path adjustment module and an operation optimization module, efficient scheduling, risk early warning and continuous optimization of the intelligent beam field are achieved, and the operation efficiency and safety are improved.

The invention relates to the technical field of data processing, in particular to an operation and maintenance object information query and work order automatic generation system based on NFC (Near Field Communication) equipment, which comprises an NFC identity recognition module, an equipment parameter summarization module, a work order information verification module, a work order structure generation module, a work order push confirmation module and a work order submission terminal based on NFC, and reading the NDEF data of the NFC tag of the equipment through the NFC reader-writer. According to the invention, automatic acquisition of equipment identity is realized through near field communication, multi-dimensional data consistency verification is carried out in combination with a chip sequence, all equipment information is automatically linked with a background data set, multiple parameters acquired on site are subjected to system logic screening, and work order key information is automatically distributed to a standard structure field. A data packaging link is controlled by a unified standard template, multiple safety verification and full-process state monitoring are executed in a submission stage, error risks are reduced in each link by means of systematic processing, a data tracing link is continuously improved, and field response and overall circulation efficiency are remarkably improved.

The invention provides a plane layout optimization method and device based on CAD drawing automatic identification, relates to the field of plane layout optimization, and solves the technical problems that only non-overlapping constraints among equipment are considered, actual space obstacles cannot be automatically avoided, and a generated layout scheme cannot be implemented. The method comprises the following steps: extracting barrier data in a layer; and through a parameter configuration mechanism, generating an equipment parameter information structural body containing the mapping relationship between the equipment and the process. And establishing a continuous optimization model containing an IPOPT solver, and obtaining an initial layout solution containing the relative orientation of each device. And based on the relative orientation of each device in the initial layout solution, introducing an obstacle rejection constraint and a device non-overlapping constraint to construct a discrete optimization model, obtaining the center position coordinates of each device through a Gurobi solver, and integrating a dynamic adjustment mechanism and an interlayer relationship optimization mechanism. And generating a new layout map for visual output. The method and the device are used in a plane layout optimization process.

The invention relates to the technical field of relay protection, in particular to an automatic checking method and system for a relay protection setting value of a power plant, and the method comprises the steps: obtaining relay protection data, including obtaining electrical parameters, equipment parameters and power topological structure information; constructing a topological graph model of the power network by using the power topological structure information, constructing an electrical model based on elements according to the electrical parameters and the equipment parameters, and combining the topological graph model with the electrical model by using node parameter association to obtain a power system integrated model; and performing constant value check calculation based on the power system integrated model, and generating a report according to a constant value check calculation result. Hidden layers are set for different fault types by adopting the multi-branch structure network, so that the neural network can perform specialized learning for different types of fault features, and the adaptability of the system to different fault scenes is enhanced.

The invention discloses a coating production line control method, device and equipment, a storage medium and a program product, and relates to the field of production scheduling, and the coating production line control method comprises the steps: monitoring production environment parameters and equipment operation parameters of coating production line equipment; parameter adjustment is carried out according to the production environment parameters and the equipment operation parameters through a pre-trained deep reinforcement learning model, the equipment parameter adjustment amount is obtained, and the pre-trained deep reinforcement learning model is obtained by carrying out model training on a preset initial deep reinforcement learning model based on historical coating production data obtained in advance; and adjusting the equipment operation parameters of the coating production line equipment according to the equipment parameter adjustment amount, and controlling the coating production line equipment subjected to parameter adjustment to perform coating production. The production line equipment parameters are dynamically adjusted through the deep reinforcement learning model, the problem that the film coating production quality is affected by film coating production line equipment parameter immobilization in the prior art is solved, and the film coating production quality is guaranteed.

The invention relates to a medical image file processing method and device and electronic equipment, and the method comprises the steps: obtaining a series of DICOM files; according to the metadata of each DICOM file, shared data and non-shared data of each DICOM file are determined, and the metadata of each DICOM file comprises patient information, equipment parameter information and spatial position information corresponding to the corresponding slice image; combining the non-shared data of each DICOM file to obtain combined non-shared data, and storing the shared data and the combined non-shared data in a first metadata field; pixel metadata corresponding to each slice image is stored in a second metadata field, and the pixel metadata comprises a gray slope, an intercept, a window width and a window level; merging the pixel data corresponding to each slice image, and storing the merged image data in an image data field; and combining the first metadata field, the second metadata field and the image data field to generate a medical image file.

The invention discloses an intelligent recommendation method and system for a corrugated carton packaging production process. The method comprises the steps that a user inputs the type, corrugation type parameters, the number of layers, the size, the weight, production process requirements and quality control standards of corrugated boards through a terminal; the system is based on a cloud computing platform, combines a mechanical property model and a manufacturing process database of a corrugated board, calculates and analyzes input data, and recommends a production process parameter combination; the system collects data in the production process in real time through a sensor, compares the data with recommended parameters, and automatically adjusts parameters of production equipment or gives an alarm. And the system continuously collects production data and user feedback, and updates the recommendation model by using a machine learning algorithm. According to the method and the system, the defective rate is effectively reduced, the production efficiency is improved, the recommendation result is accurate, the management is efficient, and the requirements of corrugated paper field production can be met.

The invention provides a layout generation method, a constraint file editor, a layout generator and a terminal.The layout generation method comprises the steps that firstly, device parameters of a target fixed circuit structure are converted into device parameters of a corresponding layout structure based on a configuration file; and determining the arrangement type, array number, spacing and other structural layouts of the device according to the layout rule. And generating a constraint file of bottom design through preset process parameters, and finally generating a layout result of the target fixed circuit structure based on the structural layout of the device and the constraint file. According to the scheme, full-automatic layout and wiring of the analog circuit layout can be realized, the design efficiency is remarkably improved, and the dependence on a specific process and a professional threshold of a layout engineer is reduced. Meanwhile, through parameterization constraint and symmetrical layout control on a fixed circuit structure, high symmetry of the layout is ensured, and the stability and reliability of circuit performance are effectively improved.

The invention provides a global adaptive power supply adjustment method and system under the condition of local load increase, and relates to the technical field of power system dispatching. The method comprises the following steps: acquiring regional power grid topology, equipment parameters and real-time operation data; historical power consumption data of classified users in recent three years are collected and preprocessed; generating a reference load curve based on real-time data and a historical rule, calculating a deviation ratio, predicting a load in future 2 hours, and outputting a risk level; the regional energy storage equipment is called to dynamically adjust the discharge power when the risk is medium or low, and the adjacent regional resources are evaluated and cross-regional line switching and power supply support are executed when the risk is high or the energy storage is insufficient. Through layered adjustment and a dynamic response mechanism, rapid identification and accurate control of sudden load increase are realized, power supply stability and resource utilization efficiency are improved, and the method is suitable for local load fluctuation response of scenes such as residential areas and industrial parks.

Method and servers for determining optimized device parameters of an electronic circuit. The method includes accessing design information of the electronic circuit, determining a set of device parameters of the electronic circuit based on the design information, receiving information indicative of set of performances-of-interest to be optimized, and a corresponding set of target values, each target value being associated with a corresponding performance-of- interest, defining a multi-objective reward function based on the set of performances-of-interest to be optimized and outputting, using a pre-built Machine Learning (ML) algorithm interacting with an electronic design automation (EDA) environment, an optimized device parameter value for each of the device parameters based on the multi-objective reward function.

The invention belongs to the technical field of data processing, and discloses a deep learning optimization system and method for intelligent equipment. Comprising the steps of collecting data and performing data cleaning to obtain accurate equipment parameters; identifying a target intelligent device resource level based on the accurate device parameters; performing energy consumption scheduling on the target intelligent device based on the resource level to obtain a scheduled intelligent device; performing disturbance simulation on the accurate equipment parameters, and outputting a disturbance stability index; appropriate parameter screening is carried out on the accurate equipment parameters based on the disturbance stability index, and stable equipment parameters are output; performing risk assessment on the stable equipment parameters to obtain parameter risk indexes; performing model matching on the scheduling intelligent equipment based on the parameter risk index to generate a model adjustment strategy; performing scene simulation based on the model adjustment strategy, performing loading optimization on the model adjustment strategy based on a simulation effect, and outputting an optimization model strategy; the efficient deep learning optimization method for the intelligent equipment is realized.

The invention discloses a cigarette cut tobacco batch production circulation control system and method based on single-point condition judgment, and the system comprises a work order parameter verification module which is used for receiving a work order issued by a scheduling system, and carrying out the work order information integrity verification, process parameter compliance verification and equipment suitability verification; the regular expression configuration module is used for providing a visual interface and configuring a regular judgment rule set of equipment starting conditions, ending conditions and abnormal ending conditions; and the single-point judgment execution mechanism is embedded into the PLC control layer, acquires equipment parameters in real time, dynamically evaluates regular conditions based on a finite-state machine model, and triggers an equipment start-stop instruction. According to the system, the cigarette cut tobacco batch switching time is remarkably shortened, and the work order circulation efficiency is improved; the manual intervention frequency is further reduced, and the misoperation rate is further reduced; and the problem of production interruption caused by disconnection of the equipment state and the process parameters is solved.

The invention discloses a portable WiFi access device monitoring and early warning system based on big data analysis, and relates to the technical field of computers. Comprising an intelligent sensing and dynamic sampling module, a self-adaptive edge calculation module, a data preprocessing and correction module, a deep learning model construction module, an anomaly detection module, a multi-channel early warning module and an equipment parameter optimization module. According to the invention, the sampling frequency and the adaptive edge computing framework are dynamically adjusted through the intelligent sensing chip, so that the problem of energy consumption and performance balance is solved, and the data acquisition and processing efficiency is improved; user behavior modeling and anomaly detection are carried out by using a deep learning model and an isolated forest algorithm, so that the accuracy and the real-time performance of early warning are remarkably improved; meanwhile, an equipment parameter optimization knowledge base is established at the cloud end, equipment parameters are dynamically adjusted in combination with a remote control interface, the operation state of the equipment is optimized, and the intelligent management level and the operation efficiency of the equipment are improved.

The invention discloses an intelligent heating ventilation air conditioner optimization control system and method, and relates to the technical field of intelligent control, and the method comprises the following steps: first analysis obtains a first corresponding relation, sends a first regulation and control signal, completes the regulation and control of equipment parameters in different time durations, second analysis obtains an optimal regulation and control time duration, and sends a second regulation and control signal; and sending the second regulation and control signal, and performing first compensation. Through historical data analysis and dynamic regulation and control, excessive operation or low-efficiency operation of equipment is avoided, the equipment operation strategy can be adjusted according to the optimal regulation and control time duration, energy consumption is further reduced, PID control is adopted for temperature compensation, indoor temperature changes can be quickly responded, the equipment operation strategy is adjusted according to the optimal regulation and control time duration, and the energy consumption is further reduced. The service life of equipment is prolonged, different environments and operation conditions are adapted through historical data analysis and dynamic regulation and control, and energy consumption and operation cost of the system can be remarkably reduced by optimizing operation parameters and operation time of the equipment.

The invention discloses an equipment parameter cooperative control system and method, and belongs to the technical field of equipment parameter cooperative control, and the method comprises the steps: carrying out the multi-stage comparison and analysis of the deviation degree of a real-time production parameter value and an initial production parameter value, carrying out the sudden disturbance monitoring, and when the sudden disturbance of target equipment is detected, carrying out the control of the target equipment; performing reverse reasoning and positioning on abnormal equipment operation parameters of abnormal reasons causing sudden disturbance, generating regulation and control instructions of the abnormal equipment operation parameters, and performing dynamic updating on the regulation and control instructions of the abnormal equipment operation parameters based on the abnormal production parameter values; and after the production task is completed, carrying out quality detection on the product, and if the product quality detection is not qualified, carrying out product quality early warning and generating a product quality production report. The problems that target equipment depends on preset process parameters in production, the sudden disturbance in the production process lacks real-time dynamic compensation capacity, and the production quality is affected are solved.

The invention relates to the field of information technology and energy-saving simulation, in particular to a self-adaptive building energy-saving system based on equipment topology and digital twinning, which comprises the following steps: if specific equipment is added in the system, an edge side can automatically detect the increase of the equipment, and communicates with the equipment through an internet of things protocol to obtain equipment information; the corresponding platform acquires information on the edge side and then mounts the information into the corresponding asset group, a topological structure is established, and a control model updating process is triggered, specifically, the platform acquires equipment parameters, data validity is verified, the equipment parameters are classified and added into a parameter pool, and sensitivity analysis is performed on all the parameters; and performing model optimization based on a reinforcement learning algorithm according to the sensitivity weight to complete model updating. The system can meet the energy-saving requirement in a complex scene, has the self-adaptive capacity in coping with equipment adjustment and scene change, and can reduce the energy consumption and greenhouse gas emission of the system.

The invention relates to the technical field of power systems, and discloses an automatic checking and correcting system and method for relay protection setting values of a booster station, and the system comprises a configuration analysis module which is used for analyzing an SCD configuration file of a transformer substation, extracting parameters of a protection device, and importing a setting value list to establish a mapping relation table; the data acquisition module is used for acquiring a constant value, a soft pressing plate state and a communication message of the IED equipment in real time through an MMS protocol; and the intelligent verification module is used for executing constant value comparison, pressing plate state verification and communication abnormity responsibility judgment. The invention develops a handheld intelligent inspection system, and solves the three major problems of low efficiency, high error rate and prominent safety risk in the checking operation of the relay protection constant value of the traditional transformer substation. According to the scheme, full-process automation of configuration analysis, data acquisition, intelligent verification, safety correction and report generation is realized, so that the routing inspection of the secondary equipment is upgraded from an extensive mode to a precise operation and maintenance normal form, and a guarantee is provided for safe and stable operation of a power grid.

According to the job task allocation method and device, the electronic equipment and the storage medium provided by the embodiment of the invention, the total task amount of job tasks to be allocated and the equipment parameters of all image forming equipment in an image forming equipment cluster are acquired, and the equipment parameters are used for indicating the health degrees of the image forming equipment; determining a preset task amount corresponding to each image forming device based on the total task amount and the device parameters of each image forming device; and allocating the job task based on the preset task load, so that the actual task load allocated to each image forming device does not exceed the corresponding preset task load. The health degree of the image forming equipment is considered when the job tasks are allocated, the image forming equipment with the low health degree allocates the printed manuscripts with fewer pages, the situation that the image forming equipment with the low health degree is damaged due to the fact that too much tasks are allocated is avoided, and the overall job task allocation strategy is optimized.

An electric hair cutting device including a housing having a first end and a second end opposite the first end, a bladeset mounted to the housing at the first end, the bladeset having a stationary blade and a reciprocating blade, an electric motor electrically connected to a power source within the housing, a drive shaft operatively connecting the electric motor to the reciprocating blade, a control module within the housing which controls a rotational speed of the electric motor; and a trigger which sends an actuation signal to the control module based on a partial actuation of the trigger between a minimum actuation and a maximum actuation, the control module adjusting a rotational speed of the electric motor based on the actuation signal. A related charging stand is provided that is compatible with the hair cutting device and features a digital display of device parameters.

According to the new energy Internet of Things multi-level analysis sharing platform based on digital twinning, the intelligent perception and edge access module collects data through a multi-mode sensor, and reliable data is provided for subsequent analysis through edge calculation preprocessing and hierarchical transmission. And the digital twinning construction and dynamic updating module constructs equipment-level, station-level and regional-level digital twinning models based on a Bayesian optimization algorithm to realize dynamic calibration of the models. And the multi-level analysis decision module realizes equipment fault diagnosis, generation power prediction, energy storage scheduling and power grid optimization through a four-level architecture. The digital twin mirror image regulation and control module establishes a bidirectional control channel and supports remote configuration of equipment parameters and automatic triggering of fault plans. The cross-domain data security sharing module adopts a three-layer security protection system and a federal learning mechanism to guarantee data security and sharing. According to the platform, full-process intelligent management of the new energy system is realized, and the operation efficiency and safety of the system are improved.

The invention discloses an intelligent operation and maintenance method and system for medical equipment, electronic equipment and a storage medium, and relates to the technical field of medical equipment operation and maintenance. The method comprises the following steps: acquiring equipment operation parameters and historical use data of each medical equipment in a hospital, and constructing an equipment parameter diagram of the hospital based on each equipment operation parameter and historical use data; determining abnormal medical equipment in the equipment data graph in combination with the historical fault equipment graph of the hospital; determining an anomaly type and an anomaly level based on the anomaly parameters of the abnormal medical equipment; and determining an operation and maintenance strategy corresponding to the abnormal medical equipment in combination with the diagnosis time window of the abnormal medical equipment, the medical importance level, the abnormal type and the abnormal level. By implementing the technical scheme provided by the invention, the operation and maintenance efficiency of the medical equipment can be improved.