73 results about "Plant management" patented technology

The invention discloses a protection setting calculation and optimization method based on an intelligent power plant management platform, and relates to the technical field of power system protection. By constructing a platform and equipment communication link, integrally collecting and preprocessing internal and external parameters, and dynamically updating topology and model parameters in combination with a switch state, the problem that a traditional static model is difficult to adapt to operation changes is solved, the timeliness and accuracy of the model are improved, a multi-scene database is constructed in a classified mode, labels are labeled, and the efficiency is improved. Fault current distribution is accurately calculated, constant value adaptability verification is carried out, constant value optimization is carried out in combination with a multi-objective optimization algorithm and sensitivity analysis, limitation of single-scene constant values is avoided, constant value conflicts under different scenes are reduced, reliability of initial constant values is guaranteed, sensitivity analysis is superposed, it is ensured that the constant values are still reliable during parameter fluctuation, and the reliability of the constant values is improved. The risk of protection operation refusal and misoperation is reduced, a constant value is issued, the device state is monitored in real time, evaluation indexes are calculated, the constant value is controllable in the whole process, and stable operation of the device is guaranteed.

The invention discloses a global-oriented multi-language multi-time-zone power station management method and system, and relates to the technical field of electric power digital management, and the method comprises the steps: collecting power station data, carrying out the standardized identification, and constructing a power station multistage coding identification main key; a multi-language mapping system is constructed by taking a multi-level coding identification main key of a power station as a drive, and time zone offset logic is constructed by using standardized identification; and performing hierarchical authority control based on the power station multi-level coding identification primary key prefix and generating a localized alarm prompt. According to the method, standardized identification of power station data is realized by constructing a unified power station multi-level coding main key; based on primary key driving multi-language mapping and time zone offset processing, accurate presentation and time consistency of data in a multi-language environment are guaranteed; authority control is achieved through primary key prefix division, multi-language alarm prompts are triggered in combination with signal coding, and a power station event management mechanism which is clear in alarm semantics, timely in response and controllable in boundary is achieved.

The invention relates to the technical field of intelligent agriculture, and discloses a traditional Chinese medicine organic planting management method based on big data analysis. A space coordinate system is established in a planting area, regular partitioning is carried out, a unique land parcel identifier is formed, a soil humidity sensor is arranged in the center of each land parcel, meteorological observation points are matched to continuously collect air temperature, wind speed and illumination, and an evapotranspiration potential index is generated through sliding window statistics and normalization. The system performs dynamic balance prediction and state correction according to the soil moisture content, the irrigation volume and the evapotranspiration loss, extracts a reference evapotranspiration rate by using an unirrigated sample, determines a threshold interval and a real-time deviation in combination with a historical quantile, and smoothly improves the stability through a sliding window. The threshold value is updated hour by hour within a feasible range, irrigation is triggered when the water content is lower than the threshold value, and the irrigation amount is limited within a reasonable range through depth and flow conversion. Structured logs are generated in the whole process, and irrigation whole-process trace reserving is achieved.

The invention relates to the technical field of disconnecting link maintenance and transformation, in particular to an isolation disconnecting link updating and transformation method. The method comprises the following steps: S1, performing inspection and quantitative analysis, performing equipment evaluation and problem diagnosis, and identifying operation problems and function deficiency; s2, performing model selection and material optimization, and setting a standard isolation switch and an electric operation assembly; s3, defining a transformation range, dividing transformation units based on the main wiring diagram, and covering the isolation disconnecting link and the grounding disconnecting link; s4, implementing a safety locking structure, adding an electrified locking and five-prevention system, and accessing whole plant management to prevent misoperation; and S5, accessing a monitoring system, and connecting an LCU screen and a computer monitoring system to realize remote centralized control. The contact resistance and temperature rise of the contact reach the standard, ablation is completely eradicated, the operating force and time are greatly reduced, jamming is returned to zero, the five-prevention locking success rate is extremely high, the misoperation drill can reach the zero penetration level, and the on-site actual effects of mechanical zero jamming, five-prevention zero penetration, remote control zero delay and old and waste utilization are achieved.

The invention discloses an intelligent interaction management method and system for a coal-fired thermal power plant, and relates to the technical field of coal-fired thermal power plant management, and the method comprises the steps: collecting multi-source heterogeneous data of the coal-fired thermal power plant; generating a structured data asset library; building an intelligent interaction core module; a layered intelligent control system is constructed, a production intelligent control layer performs parameter prediction and closed-loop regulation and control on production core equipment based on production related data in a structured data asset library, and an environmental protection intelligent control layer performs parameter optimization and linkage regulation and control on environmental protection processing equipment through a mechanism-data hybrid drive model based on production data and environmental protection monitoring data. The technical problems that in the prior art, a coal-fired thermal power plant lacks a collaborative management linkage mechanism, so that the operation efficiency is low, the environmental protection standard reaching stability is poor, and the management intelligence level is insufficient are solved, and accurate collaborative management and control of the coal-fired thermal power plant are achieved. And the operation efficiency, the environmental protection standard reaching stability and the management intelligence level of the factory area are improved.

The present application relates to the technical field of power plant management, and discloses a kind of intelligent power plant power factor intelligent regulation and control method and system, collect power plant equipment energy consumption data, environmental parameter, load demand data, equipment operating state data and historical power factor data, align multi-source data in combination with DTW algorithm, obtain time series data;GNN network is used to topologically model power plant equipment;Time series data is input into Transform model, and the result output by GNN network is combined to carry out feature extraction, and obtain future 24 hours load prediction result;Based on load prediction result, through the joint application of particle swarm optimization algorithm and DQN network, regulation and control scheme is generated;Regulation and control scheme is converted into regulation and control instruction, regulation and control instruction is issued, and regulation and control data is collected to carry out real-time feedback;The present application realizes the real-time prediction of power factor, and the response time of regulation and control is shortened compared with traditional manual regulation and control, reduces plant power factor, and improves response speed.

The invention discloses a steel plant management statistics intelligent data processing system and method. Comprising a converter original report module, a refining furnace original report module, a refining furnace production report module, a steelmaking production report module and a steelmaking three-shift report module. And each module is respectively provided with functions of data display, query, export and report updating. According to the invention, by integrating the related data in the production process of the steel plant and intensively displaying various types of data in different pages, unified management of the data is realized. Meanwhile, convenient data entry, modification and deletion operation and efficient query and export functions are achieved, the time and labor cost of data management are greatly saved, and the data management efficiency is improved.

The application relates to the technical field of power plant management, and discloses a virtual power plant management method based on multi-source data fusion, which is used for solving the problem that response delay occurs between devices when virtual power plant management is performed, and comprises the following steps: obtaining device response behavior characteristic parameters of each distributed energy device according to an initial device operation sequence library; obtaining a response lag index according to the device response behavior characteristic parameters; determining whether a non-synchronous response lag effect occurs for each distributed energy device according to the response lag index; screening all response lag devices; performing delay compensation on all response lag devices; obtaining an actual device operation sequence library; obtaining a fusion input matrix according to the actual device operation sequence library; extracting main characteristic dimensions based on the fusion input matrix; constructing a state characteristic vector sequence; and training a time sequence prediction model, so that the accuracy of virtual power plant management is effectively improved.

The invention relates to the technical field of seedling culture management, and discloses a crop planting management system based on three-dimensional digital twinning, and the system comprises a data collection module which obtains a three-dimensional time sequence of growth of each hole of a crop, environment light quality and root zone temperature data, and calculates growth state parameters to generate a threshold time field; the phase ridge determination module establishes a coordinate system according to the contour of the cultivation table top, calculates the derivative and curvature of a threshold time field, and determines a phase ridge curve and a normal derivative; the inversion module solves key sensitivity parameters and thermal boundary layer parameters based on ambient light quality and root zone temperature data; the first management module maps the normal guide number into a regulation and control execution parameter in combination with the parameters, and multi-hole growth progress synchronization is achieved; and the second management module calculates the distance from each hole to the phase ridge curve and the normal derivative, generates a transplanting moment and forms a continuous transplanting schedule, thereby realizing high-precision and full-process seedling digital management.

The application relates to the technical field of booster station plant management, and discloses a booster station plant data acquisition self-control energy-saving management system. Through standardized collection and deep feature extraction of full-factor operation data of the booster station plant, an innovative combination of physical information neural networks and a Transformer architecture is adopted to establish a precise prediction system covering load changes, equipment efficiency attenuation and environmental coupling influence. Meanwhile, through data envelopment analysis and life cycle assessment methods, a dynamic evaluation mechanism in the double dimensions of energy efficiency and carbon emission is constructed. Based on the fusion of multi-objective optimization algorithms and model predictive control, a real-time control strategy considering economic operation and low-carbon constraints is formed, so that the dual goals of improving the overall energy efficiency level and the management accuracy of carbon emission of the system are achieved.

A tree and shrub identification tag device is provided. The device is comprised of a tag designed for efficient and adaptable plant identification while promoting environmental sustainability. The device comprises eco-friendly materials, including biodegradable and recyclable plastics, to ensure durability and weather resistance through UV protection additives and waterproof coatings. The tag body is available in various sizes and features a rubberized texture for enhanced grip. A notched slot at one end allows for secure attachment and easy removal, while perforations facilitate damage-free detachment from around a plant. The device may also be comprised of a color-coding system for categorizing plant types and includes a customizable surface for detailed labeling. Additionally, the tag may incorporate QR codes for accessing digital resources such as care guides and supplier information, enhancing overall plant management and user experience.

This application proposes a quality management method and system for new energy power plants, relating to the field of power plant management. The method includes: receiving a list of component models and quantities for target energy storage units in a target new energy power plant; statistically analyzing the time-series matrix of energy storage impact attributes, energy storage unit samples, and workload time-series information; performing a redundancy lifetime assessment on the target energy storage units based on the workload time-series information to obtain a predicted redundancy lifetime value; and assigning temporary or default periodic quality inspection labels to the target energy storage units. This application solves the technical problem in existing technologies where quality inspection of new energy power plant construction relies solely on periodic monitoring, potentially leading to the omission of critical risk moments and the existence of operational hazards. It achieves the technical effect of conducting redundancy lifetime assessments based on the service data and workload time-series information of energy storage units, enabling targeted quality inspection labels, and effectively preventing operational risks.

According to the factory management system data access method and system based on double-entrance hybrid deployment provided by the invention, through a double-entrance hybrid deployment architecture and a dynamic perception control mechanism, deep decoupling of client-side independent deployment sense and actual cloud storage is effectively realized; users of small and medium-sized enterprises can perceive a traditional local deployment system in a use process, so that psychological obstacles on data cloud are eliminated; meanwhile, all core production data are centrally stored in the cloud, the professional safety protection capability and the low-cost storage advantage of a cloud service provider are fully utilized, a company headquarters can directly access production operation data of customer factories in real time through an independent public network entrance, and efficient remote management and control and unification and coordination of multiple factories are achieved. According to the scheme, on the premise that local hardware burdens of clients are not increased and data consistency is not sacrificed, contradictions in three aspects of safety, cost and management efficiency are balanced, and the digital transformation requirements of vast small and medium-sized manufacturing enterprises in China at present are particularly met.

The invention relates to the technical field of planting management, and discloses an intelligent grass raising control system. According to the system, planting data of all seedling raising trays, chemical component data of grass seedlings in all seedling raising trays and sensing data of environments where the seedling raising trays are located are acquired through a multi-dimensional acquisition module and are classified to form a data set, and an intelligent evaluation module analyzes the water content of culture media in each seedling raising tray according to different types of culture media; a corresponding water retention coefficient is generated, the balance relation between the planting density and water management is accurately evaluated, the growth state of grass seedlings in each seedling raising tray is analyzed, a corresponding health coefficient is generated, insufficient nutrition or abnormal growth is recognized in time, the comprehensive evaluation precision is high, and the intelligent evaluation module evaluates whether the environment where the seedling raising trays are located meets the cultivation standard or not in real time; whether the structure of the culture medium of the seedling raising plate is abnormal, whether the water shortage risk exists in the cultivation mode and the health state of grass seedlings are judged, corresponding early warning signals are generated, and the dynamic management cultivation effect is good.

This invention provides a manufacturing factory management system based on a digital twin platform, belonging to the field of factory management technology. It includes: a physical factory layer for real-time collection of equipment operation, process parameters, material consumption, and employee work reports; a digital twin platform layer deployed on a cloud or local server, including a data acquisition and integration module, a model building and management module, and a simulation and prediction module, constructing a high-fidelity, real-time synchronized digital twin and identifying dynamic bottlenecks based on constraint theory; and an application service layer including sub-modules such as intelligent scheduling and dynamic dispatching, human-machine collaborative work assignment, precise quality control, predictive equipment maintenance, and intelligent logistics and warehouse scheduling. This invention achieves real-time mapping and interaction between the physical factory and virtual space through the digital twin, solving problems such as data silos, delayed response, and disconnect between planning and execution in existing systems, thus realizing adaptive optimization and precise control of the production process.

The invention discloses a chip workshop industrial energy-saving management system based on big data analysis and an optimization method, and relates to the technical field of industrial energy saving. The method comprises the following steps: S1, collecting multi-source heterogeneous data of a factory affair system, process equipment and a production system of a chip workshop, performing space-time alignment and standardization processing on the multi-source heterogeneous data, and constructing a unified data model; s2, on the basis of the unified data model, a system energy efficiency state set is constructed in a multi-stage processing mode, and the system energy efficiency state set comprises a process energy efficiency entropy, a thermodynamic collaboration degree and a dynamic energy efficiency load balancing index; s3, inputting the system energy efficiency state set into a decision model fusing multi-objective optimization and reinforcement learning to generate a collaborative optimization strategy; and S4, based on a collaborative optimization strategy, determining and executing a corresponding control mode through a hierarchical security control mechanism. Through multi-source data fusion and intelligent collaborative energy-saving optimization, the energy efficiency characterization precision and the self-adaptive energy-saving capability of the chip workshop are improved.

The invention discloses a plant management and control system based on artificial intelligence and big data, particularly relates to the field of plant management and control, and comprises a plant data acquisition module, a virtual plant construction module, a global coordination optimization module and a control instruction execution module. According to the plant management and control system based on artificial intelligence and big data, path conflicts and resource competition decision conflicts are recognized in advance through a virtual plant model of a virtual plant construction module, a predictive negotiation space is provided for management and control scheme optimization, and the risk of control instruction overlapping in the physical world is effectively avoided; global collaborative decision of multiple edge nodes is realized through a global collaborative optimization module, a control scheme is dynamically adjusted to eliminate conflicts, and instruction overlapping caused by independent operation is avoided; and a virtual model and agent strategy is continuously corrected through feedback data by the control instruction execution module, so that the stability of global coordination is further enhanced, conflict recurrence is avoided, and efficient operation of the system is guaranteed.

Provided is a data management device (10) that uses data collected from a plant (20) equipped with a module (244) for executing data processing and a facility hierarchy including a facility (21), equipment (22) belonging to the facility (21), and a device (23) belonging to the equipment (22) as a basis to visualize the dependency relationship of the facility hierarchy and the dependency relationship between the module (244) and signals input to and output from the module (244), said data management device (10) comprising: a dependency relationship analysis unit (11) that generates dependency relationship data (121) indicating the dependency relationship of the facility hierarchy and the dependency relationship between the module (244) and the signals on the basis of facility hierarchy data (31) and input / output signal data (33); and a screen display unit (13) that uses the dependency relationship data (121) as a basis to generate screen data for a plant management screen indicating the dependency relationship of the facility hierarchy and the dependency relationship between the module (244) and the signals.

The application discloses a fine drip irrigation control system, which comprises a water source system, a head system, an electrical control system, a programmable controller PLC and an HMI system, and each system cooperatively realizes fine irrigation; the programmable controller PLC is internally provided with an irrigation control program, and the irrigation control program comprises a timing irrigation subprogram, a sensor data acquisition subprogram, a precise fertilizer application control subprogram, a frequency converter communication control subprogram and a data alarm subprogram. The fine drip irrigation control system has the remarkable effects of water saving and fertilizer saving: through the drip irrigation mode and the precise fertilizer application control, the fertilizer utilization rate is increased by more than 50%, and the water consumption is reduced by more than 60%; the automation degree is high: the whole process automation of irrigation, fertilization, monitoring and alarm is realized, manual on-site operation is not needed, the labor intensity is greatly reduced, and the fine drip irrigation control system is suitable for large-scale planting bases; the monitoring is accurate and comprehensive: the key parameters such as soil humidity, nutrients and pH value are monitored in real time, the data acquisition precision is high, and a scientific basis is provided for planting management.

The invention relates to the technical field of positive sample preparation, in particular to a tube implanting module and a preparation device and method of a positive reference substance, the preparation device comprises a clamping assembly and a processing assembly, and the clamping assembly is used for clamping a target sample. The machining assembly comprises a plurality of machining modules, each machining module and the clamping assembly can move relatively, and when the machining modules correspond to the clamping assembly and are located at the machining stations of the target samples, the machining modules conduct machining treatment on the target samples. Wherein the plurality of processing modules comprise a punching module and a pipe planting module, and the punching module can perform punching treatment on the target sample so as to form a hole in the target sample. And the tube implanting module can perform tube implanting treatment on the target sample so as to implant a microtube into the hole. According to the preparation device, the target sample is subjected to punching treatment and tube implanting treatment through the punching module and the tube implanting module, part of manual operation is replaced, micro-tube implanting is completed, and the preparation efficiency of the positive reference substance is effectively improved.

The application provides a kind of intelligent power plant management system and portable UWB positioning device, belongs to power plant safety management technical field, including including positioning system and integrated management platform;Wherein, the positioning system includes UWB positioning subsystem, video monitoring subsystem, equipment monitoring subsystem;The UWB positioning subsystem is used to detect the position of target wearing portable UWB positioning device within the preset range;The video acquisition subsystem is used to obtain the image information in the area corresponding to each image acquisition unit;The equipment monitoring subsystem is used to obtain the operating parameters of each unit equipment;The positioning system uploads the position of the target, the image information and the operating parameters to the integrated management platform.The application enables managers to real-time grasp personnel information, construction site conditions and equipment operating status, which is conducive to improving the work efficiency of daily maintenance and repair of power plants, enables timely handling of emergency events and conditions, and truly achieves intelligent management.

The application belongs to the technical field of unmanned agriculture, and relates to a covering plant management device for an unmanned farm and an application method thereof. The C-shaped planting assembly comprises a plurality of C-shaped planting discs, the C-shaped planting disc is provided with a C-shaped seeding groove, and a cutter is arranged in the C-shaped seeding groove. The first pulling assembly is connected with the cutter and pulls the cutter to move in a first direction along the C-shaped seeding groove. The second pulling assembly is connected with the cutter and pulls the cutter to move in a second direction along the C-shaped seeding groove. The control module is electrically connected with the first pulling member and the second pulling member. The crop management node of the covering plant accesses the control module of the unmanned farm to control the first pulling assembly and the second pulling assembly, and unmanned cutting management of the covering plant is realized. The application provides a simple device for realizing unmanned management of covering crops and can access an existing unmanned farm to realize unmanned harvesting management of covering plants.

The invention discloses a self-adaptive energy management method for a virtual power plant, and relates to the technical field of power plant management, and the method comprises the following steps: configuring initial parameters, and setting initial values of a prediction model and an optimization target; periodically collecting real-time data; according to the real-time data, using an adaptive model fusing weather and tide factors to predict the return time of each fishing boat; based on the predicted return time, estimating a charging power demand of aggregation of all fishing boats in a period of time in the future; making an optimal plan of battery charging and discharging and power grid interaction; converting the optimization plan into a control instruction, issuing the control instruction to a battery system and a charging station, and monitoring an execution condition; and parameters of the prediction model are automatically updated regularly according to the actual return time and the prediction error. According to the method, a self-adaptive load prediction mechanism based on real-time environment data and a historical behavior mode is introduced, energy storage optimization scheduling is driven, and power fluctuation between renewable energy power generation and random loads can be stabilized.

The invention provides an agricultural digital management system and method, and the system comprises a field management module which is used for managing field archives, monitoring soil, and monitoring climate; the planting management module is used for executing plantology management, four-condition monitoring, yield prediction and agricultural machinery operation; the grain purchasing module is used for executing weighing management, purchasing standard management, purchasing application management, contract management, payment management and supplier management; the grain sales module is used for managing agricultural products, orders, after-sales, transportation, storage and customers; the digital management system covering the whole process of agricultural production is constructed through deep integration of the field management module, the planting management module, the grain purchasing module, the grain selling module and the data service module, and the system realizes refinement, intellectualization and transparency of agricultural production by using the Internet of Things, big data, artificial intelligence and remote sensing technologies. The agricultural production efficiency and the economic benefit are obviously improved.

The utility model belongs to the technical field of power generation control, and particularly discloses an energy storage frequency modulation system network architecture for a thermal power plant. Through a power plant management and control network architecture combining energy storage adjustment and flue gas monitoring, energy storage adjustment and flue gas monitoring technologies in a power plant are organically combined, and the overall operation efficiency and the environmental protection level of the power plant are improved. On one hand, the energy storage system utilizes power plant operation condition information reflected by flue gas monitoring to optimize a charge-discharge strategy and an operation mode of the energy storage system, and the comprehensive benefits of the energy storage system on power plant production are improved; and on the other hand, the flue gas monitoring data is not only used for meeting environmental protection compliance, but also can perform data interaction and collaborative decision with energy storage adjustment from the perspective of power plant energy comprehensive management and efficient utilization, so that dual optimization of the power plant on energy utilization and environmental protection is realized, and the method is suitable for being popularized and used in power output and environmental protection management and control projects of the thermal power plant.

The invention discloses a Gobi facility greenhouse vegetable carbon footprint evaluation method and system based on image recognition, and relates to the technical field of agricultural environment, the type of a Gobi facility greenhouse is automatically discriminated through an image recognition technology, and the appearance characteristics of the greenhouse are analyzed in combination with a convolutional neural network algorithm. Building standards and planting management data in a preset database are called, the carbon emission accounting model is used for accurately calculating the complete-cycle carbon footprint of facility building and vegetable production, and an emission reduction strategy and a scene simulation result are generated. Image recognition is carried out through photographing, the greenhouse type can be rapidly determined, the carbon footprint accounting result is generated through one key, operation is easy and convenient, and efficiency is high. According to construction standards and management measure data of different types of greenhouses, carbon emission can be accurately checked, adjustment is performed in combination with actual conditions, the reliability of checking results is improved, and decision support is provided for green sustainable development of gobi facility greenhouse vegetable planting.

The invention discloses a smart factory management method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence. The method comprises the following steps: acquiring employee online information, equipment state information and skill points of each employee sent by each station control equipment; obtaining production information of a factory; obtaining a real-time objective skill rate based on the online information of each employee and the skill point number of the corresponding employee; when the employee online information is an employee abnormal state and / or the equipment state information is an equipment abnormal state, recording the station state information as an abnormal state, and generating corresponding real-time alarm information; based on the real-time alarm information, generating a corresponding current day alarm information distribution map; and displaying the real-time objective skill rate, the station state information, the real-time alarm information, the current day alarm information distribution diagram and the production information as display information, and generating factory management suggestions for instructing a factory administrator to perform factory management. By implementing the technical scheme provided by the invention, the difficulty of factory management can be reduced.

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent planting system for full-cycle biogas and fertilizer nutrition coupling in an under-forest ginseng field and an operation method.The system is an under-forest ginseng intelligent planting management platform, a central control system serves as a core, operation of all subsystems is coordinated, and a no-tillage miss-seeding intelligent compensation system is integrated; accurate seeding and miss-seeding automatic compensation are realized; the biogas manure in-situ soil improvement system is used for preparing slow-release biogas manure and improving soil; the unmanned aerial vehicle multispectral monitoring system collects growth data and evaluates conditions; the precise harvesting collaborative operation system is combined with positioning data to realize mechanical pre-digging and manual fine trimming; and the data analysis and management system processes information, generates an operation guidance scheme and issues the operation guidance scheme, so that whole-process intelligent management from sowing to harvesting is formed.

This application relates to a prefabricated smart plant cultivation box and its control method, which includes a planting pot, a water tank, a filter assembly, a soil sensor, an equipment room, and an intelligent control assembly. This application achieves automated monitoring and precise control of the entire plant growth cycle through deep collaboration of multi-sensor fusion and intelligent algorithms. The system can collect environmental parameters such as temperature, humidity, and light, as well as soil nutrients and plant morphological characteristics in real time. Through big data analysis technology, it quickly identifies growth anomalies, such as water shortage, nutrient deficiency, or pest and disease risks, and automatically generates targeted maintenance strategies. This integrated design eliminates the cumbersome process of manual observation and experience-based judgment in traditional plant cultivation, allowing even non-professional users to easily achieve scientific plant management, significantly lowering the maintenance threshold and improving plant survival efficiency.

The invention relates to the technical field of thermal power generation equipment, in particular to an intelligent use method for management operation and maintenance of thermal power generation equipment. According to the technical scheme, the system comprises a basic platform layer and a security operation and maintenance intelligent application layer, and the basic platform layer comprises an underlying data structure and a governance mechanism. The use method of the basic platform layer comprises the following steps: 1) a planning and temporary storage stage: generating a node to be imported and a relation draft according to expansion, transformation and maintenance plans, entering a temporary storage area, and executing field normalization and preliminary legality check on the draft; and 2) rule verification and conflict pre-detection: running a rule engine in a temporary storage district. The method has the advantages that a closed-loop intelligent isolation planning capability is formed, and the thermal power generation equipment is ensured to be more stable in overall running safety and more accurate in maintenance processing when being used, and is higher in safety and longer in service life in the running process.