97 results about "Intelligent maintenance system" patented technology

The invention discloses an intelligent maintenance system and method for an electric actuating mechanism based on fault prediction, and particularly relates to the technical field of equipment maintenance. The method comprises the following steps: acquiring historical operation data in a preset operation period, generating a historical health data set, extracting characteristic parameters to obtain a historical characteristic parameter set, and constructing a trend change characteristic matrix according to the change trend of the historical characteristic parameters; constructing a multi-dimensional state space by using the trend change feature matrix, calculating the state aggregation degree of the fault event in the space, and determining an early warning index set; meanwhile, collecting real-time operation data to extract real-time feature parameters, forming real-time feature state vectors, and performing correlation calculation on the real-time feature state vectors and early warning indexes in a space to obtain real-time risk correlation factors; and finally, generating a risk probability prediction model according to the real-time risk association factor, the historical characteristic parameter set and the trend change characteristic matrix, predicting a fault probability and outputting a maintenance scheduling suggestion, thereby realizing intelligent maintenance of the electric actuating mechanism.

The invention provides a rotating machine fault diagnosis method and system based on an improved deep adversarial migration network. The method and system are suitable for cross-working-condition intelligent diagnosis of rotating machines such as motors, fans and bearings. According to the method, based on frequency domain feature extraction of vibration signals, high-robustness image input is generated through GAF conversion and AUGMIX enhancement; constructing a feature extractor fusing multi-scale convolution and an attention mechanism, and realizing feature migration between a source domain and a target domain in combination with an improved domain adversarial neural network (DANN); and an entropy minimization classifier is introduced to improve the classification confidence of the target domain. And feature extraction and classification performance synchronous optimization are realized through end-to-end joint training, and the classification accuracy and generalization ability are significantly improved under the conditions of sample imbalance and no label target domain. Experimental results show that the method still keeps high diagnosis precision under the conditions of unbalanced samples and cross working conditions, and is suitable for an intelligent maintenance system in an industrial scene.

The invention discloses an intelligent maintenance system and method based on image analysis, and the system comprises a data collection module, a data fusion module, an intelligent analysis module, and a decision alarm module, the data collection module is used for collecting multi-dimensional image data and equipment data of a target power transformation main device in a target power system, obtaining first multi-dimensional image data and target equipment data; the data fusion module is used for fusing images in the first multi-dimensional image data to obtain a target fusion image; the intelligent analysis module is used for performing fault identification on the target power transformation main equipment according to the target fusion image and the target equipment data to obtain a target fault identification result; and the decision alarm module is used for determining a target maintenance plan and a target alarm operation corresponding to the target fault recognition result. According to the invention, the fault diagnosis accuracy of the power transformation main equipment can be improved.

The invention relates to the technical field of home appliance maintenance, in particular to a cross-brand home appliance maintenance knowledge sharing system and method based on federated learning, a multi-mode fault data collection and intelligent maintenance system integrates image, text, fault phenomena and multi-mode instruction data collection, and after pre-processing, the multi-mode fault data collection and intelligent maintenance system is established. A feature decoupling model is established through a maintenance knowledge analysis module, a cross-brand maintenance knowledge base stores general and specific fault information and assists knowledge migration, a federal learning training module adopts differential privacy homomorphic encryption, data privacy is protected, gradient aggregation cooperative training is achieved, a reality maintenance guidance terminal is augmented to visually guide maintenance, the efficiency is improved, and the maintenance efficiency is improved. The preventive maintenance module calculates the equipment maintenance cycle, generates a prevention scheme, and reduces the failure rate and cost. The system significantly improves the maintenance accuracy, rapidly fuses into a new brand, realizes knowledge sharing and utilization, and brings revolutionary revolutions to the household appliance maintenance industry.

The invention relates to the field of intelligent transportation systems, discloses an intelligent highway maintenance system and method, and aims to solve the fundamental defects that in the prior art, the data acquisition dimension is single, the maintenance decision depends on artificial experience, and prospective prediction is lacked. The method comprises the following steps: acquiring multi-modal dynamic sensing data covering a whole road domain; constructing and updating a four-dimensional space-time digital twinborn model in real time; driving the causal graph neural network model to perform structure health state prediction and diagnosis; generating an active maintenance instruction through a multi-objective optimization engine; and the autonomous maintenance execution unit is dispatched to complete unmanned closed-loop operation. According to the method and the system, fundamental transformation of road maintenance from passive response to active prevention, from artificial experience to intelligent decision making and from discrete operation to closed-loop automation is realized, and the maintenance efficiency, the safety and the health level of the whole life cycle of the road are remarkably improved.

The invention relates to a balanced color perception enhancement method for rail transit target detection, and belongs to the technical field of rail transit and computer vision. According to the method, brightness channel adaptive histogram equalization enhancement CLAHE-LC, a multi-segment tone channel mask mechanism MSHCM-M and a three-stage hybrid mechanism non-maximum suppression TSLSH-NMS technology are combined, so that the problems of complex illumination, multi-target shielding, color interference and the like in a rail transit scene are solved, and the accuracy and recall rate of target detection are improved. The method can be seamlessly integrated into an existing deep learning target detection framework, is compatible with multi-label, multi-category and multi-scale features, and remarkably improves the precision and recall rate of target detection in a rail transit scene. Experimental verification shows that the real-time performance is guaranteed, meanwhile, the detection performance is obviously improved compared with a traditional scheme, and the method is suitable for being applied to an actual rail transit safety monitoring and intelligent maintenance system.

The invention relates to the field of grid beam detection, and discloses a grid beam coverage area anomaly detection method, which comprises the steps of S1, acquiring an image sequence or a video frame comprising a to-be-detected grid beam coverage area; s2, performing the following processing on each frame of image in the image sequence; s3, based on time sequence consistency analysis between adjacent frames, judging the structure or texture change condition of the grid beam candidate region; s4, performing similarity calculation on the candidate region and a preset abnormal image sample to generate an abnormal score of each image block; s5, if the abnormal score exceeds a preset threshold value, judging that the area is an abnormal grid beam; according to the method, multiple key technologies such as multi-scale feature extraction, an attention mechanism, time sequence consistency analysis and depth anomaly recognition are combined, so that the abnormal region of the grid beam structure can be accurately recognized under a complex background, and the detection precision, the real-time performance and the automation degree are remarkably improved; the method is especially suitable for large-area inspection and intelligent maintenance system integration.

The invention discloses an intelligent maintenance system and method for a cement stabilized macadam base. The maintenance system comprises a monitoring module, an intelligent control module, maintenance equipment and a covering material, the monitoring module comprises a temperature and humidity sensor, a moisture evaporation sensor and an environment monitor; the intelligent control module comprises a data collector, a data processing unit and a controller; the maintenance equipment comprises a water tank, a water pump, pipelines and nozzles. The maintenance method adopts the maintenance system and further comprises the maintenance steps of data acquisition, data analysis and processing, maintenance decision and control and real-time monitoring and adjustment. The system has the beneficial effects that by monitoring the temperature, humidity, moisture evaporation rate and environmental parameters of the cement stabilized macadam base in real time and utilizing the data processing and control module to accurately calculate maintenance parameters, accurate maintenance of the base is achieved, uniform development of the strength of the base is ensured, generation of diseases such as cracks is effectively reduced, and the service life of a road is prolonged.

The invention discloses a mine road intelligent maintenance system for multi-source sensing of a mining dump truck. The mine road intelligent maintenance system comprises a mine car sensor array, an edge computing unit and an equipment collaboration network, the mine car sensor array, the edge computing unit and the equipment collaborative network are in signal connection in sequence; the mine car sensor array is used for collecting whole car and road data and comprises a load sensor, a six-axis IMU, a laser radar, a binocular vision sensor and a vibration sensor. According to the invention, real-time monitoring and dynamic maintenance of the mine road state are realized, and a manual inspection blind area is effectively eliminated. The accuracy of road degradation identification is improved through multi-dimensional data fusion, and resource waste caused by misjudgment is avoided. And an equipment cooperation mechanism solves the conflict problem of maintenance operation and transportation tasks, and the road use efficiency is remarkably improved. The edge computing architecture reduces the data transmission load, and ensures the stable operation of the system under a complex working condition.

The invention belongs to the technical field of wind power operation and maintenance, and discloses a wind turbine generator intelligent maintenance system combined with component residual life evaluation, which comprises a sensing edge layer, a station cooperation layer, a cloud intelligent center, an execution and safety interlocking layer and a credible evidence storage layer. The system collects unit operation and environment data through the edge layer, gathers the data through the station cooperation layer, inputs the data into the cloud intelligent center for multi-domain degradation modeling and life prediction, and determines a maintenance window and a resource scheduling scheme based on a comprehensive cost optimization model. The cloud center realizes cross-station model sharing and self-learning through a federated learning mechanism, the execution layer completes maintenance tasks and safety locking control, and the trusted evidence storage layer performs block chain abstract evidence storage on key data. According to the system, health state evaluation, intelligent maintenance decision making and whole-process credible tracing of the wind turbine generator are realized, and the operation and maintenance safety and economy are improved.

The invention discloses an instrument remote intelligent maintenance system based on real-time data, which belongs to the technical field of instrument intelligent maintenance and is composed of an instrument, a two-way communication layer, an application server and a user terminal. The instrument acquires operation and health data through the data acquisition module, and the operation and health data are preprocessed by the edge calculation module and then uploaded to the cloud. The bidirectional communication layer adopts various communication technologies to guarantee real-time and stable transmission of data. And the application server constructs a predictive maintenance model by using big data analysis and a machine learning algorithm, and realizes anomaly detection, fault prediction and intelligent maintenance decision in combination with database data. The user terminal provides a visual interface, and supports remote operation and maintenance strategy customization. The system solves the problems of insufficient real-time performance, remote limitation and low intelligent degree of traditional instrument maintenance, realizes closed-loop management of real-time abnormal detection, fault prediction, remote calibration and intelligent maintenance decision of the instrument, and improves the operation efficiency and data reliability of the instrument.

The invention discloses a laboratory equipment fault diagnosis and maintenance decision generation system based on a large language model, and relates to the technical field of fault diagnosis. According to the system, a cross-device and multi-data type intelligent maintenance system in an AI For Science black light laboratory scene is constructed, standardized processing and intelligent fusion of multi-modal data are realized, and fault diagnosis, root cause tracing and executable maintenance decision generation are enabled through association of a large language model and knowledge, so that the maintenance efficiency is improved. A self-learning optimization closed loop is formed based on decision feedback and a knowledge vector library, the maintenance operation convenience is improved in combination with man-machine collaborative interaction, manual misjudgment and downtime are reduced, intelligent, standardized and traceable support is provided for full-life-cycle operation and maintenance of black light laboratory equipment, and the maintenance efficiency is improved. The problems that traditional maintenance is slow in response and low in accuracy, fault diagnosis lacks semantic understanding and causal reasoning ability, operation and maintenance knowledge is difficult to accumulate and inherit, and a full-life-cycle maintenance closed loop is lacked are solved.

The invention belongs to the technical field of vehicle maintenance, and particularly relates to a vehicle intelligent maintenance system and a vehicle, the vehicle intelligent maintenance system comprises a multi-source data acquisition module, a fault reasoning module, a maintenance knowledge graph engine module, a central control screen augmented reality maintenance guidance module and a maintenance process information interaction and support module; the multi-source data acquisition module is used for acquiring operation data of the vehicle in a preset time period before and after a fault; the fault reasoning module is used for analyzing the fault data, calculating the part failure probability and generating a fault influence degree thermodynamic diagram; the maintenance knowledge graph engine module is used for storing and managing a maintenance knowledge graph and supporting natural language interactive query; the central control screen augmented reality maintenance guidance module is used for providing maintenance guidance in a three-dimensional animation and augmented reality form; and the maintenance process information interaction and support module is used for realizing information interaction between maintenance personnel and the system and providing operation guidance for the maintenance personnel. According to the invention, functions of vehicle intelligent maintenance and fault prediction can be realized.

The invention relates to the technical field of green isolation belt maintenance, in particular to a vehicle-mounted intelligent maintenance system and method for a green isolation belt, and the system comprises an environment monitoring module which is used for monitoring the environment parameters of a green belt in real time; the control center is used for processing data and outputting a control instruction; the intelligent maintenance vehicle automatically adjusts irrigation, fertilization and pesticide spraying according to the instruction; the executing mechanism is used for precisely executing the maintenance task; and the remote monitoring management system realizes remote monitoring and management. Through the vehicle-mounted intelligent equipment, automatic irrigation and fertilization are realized, and the labor intensity and the time cost are reduced. And the GPS and GIS technologies are utilized, so that accurate space positioning of the maintenance area is ensured. The intelligent control system optimizes resource utilization, avoids waste and reduces cost. And system standardized operation ensures consistent maintenance quality. Plant growth conditions and environmental parameters are monitored in real time, and problems are solved in time. The system collects maintenance data, and provides a basis for strategy formulation through analysis.

The invention discloses a method for complementing data in a cigarette manufacturing process based on a GAN (Germanic Analysis)-Transformer (Germanic Analysis-Transformer). The method comprises the following steps: acquiring multi-modal time sequence data in a cigarette manufacturing process, performing preliminary interpolation to obtain a multi-modal original tensor, and generating a missing mask at the same time; performing feature extraction on the multi-modal original tensor, and embedding process prior to obtain sparse perception unified representation; inputting the sparse perception unified representation and the missing mask into the generative adversarial network to obtain a preliminary completion sample; and inputting the preliminary completion sample and the multi-modal original tensor into a mixed Transform model in parallel for fine calibration, and obtaining a corrected completion sequence. Through cooperation of multi-modal deep fusion, physical knowledge embedding, uncertainty quantification and online reinforcement learning, the problem of data missing is solved, and data support is provided for a credible, reliable and evolvable cigarette manufacturing data intelligent maintenance system.

The application discloses a PC intelligent maintenance system, method and medium, which is applied to a PC device to be maintained, the PC device to be maintained is provided with hardware to be maintained, and the system comprises an information collection module, a maintenance item confirmation module, a scheme generation module, a maintenance execution module and a backup processing module; the information collection module is used for acquiring hardware information and hardware use duration of the hardware to be maintained, and the information collection module generates device information based on the hardware information and the hardware use duration; the maintenance item confirmation module is used for acquiring a first maintenance time of the hardware to be maintained, and calculating a health degree score based on the device information and the first maintenance time; the maintenance item confirmation module sets a maintenance item based on a benchmark score and the health degree score; the scheme generation module is used for acquiring user feedback information corresponding to the maintenance item, and generating a maintenance scheme based on the user feedback information, the maintenance item, a scheme template and a plurality of maintenance strategies; and the application can realize intelligent maintenance of the hardware, and prevent invalid maintenance and over-maintenance.

The invention relates to the technical field of intelligent agricultural equipment and nondestructive testing, and discloses a stadium lawn intelligent maintenance system and method based on AI vision, and the system comprises a mobile operation platform, a frequency conversion pneumatic excitation module, a bimodal vision collection module and an edge calculation processing module. The system applies controllable pulsating airflow to a lawn by using a variable-frequency pneumatic excitation module, induces blade resonance and destroys an air boundary layer; and after the airflow stops instantaneously, the bimodal visual acquisition module synchronously acquires free attenuation vibration and a thermal recovery image sequence of the blade. And the edge calculation processing module calculates an equivalent stiffness coefficient, a damping ratio and a thermal time constant of the blade through decoupling analysis, and evaluates the water deficit, disease or growth potential state of the lawn based on multi-dimensional parameter joint distribution. According to the method, the non-contact early warning and accurate identification of the microscopic physiological characteristics of the lawn are realized by actively constructing the mechanical thermal unbalanced state.

This invention provides a method and apparatus for controlling the entry and exit of specimens in an intelligent curing system, relating to the field of data processing technology. The method includes: acquiring entry information of specimens to be cured; verifying the specimens based on the entry information to obtain verified specimens; determining target curing environment parameters based on the verified specimens and preset curing requirements; controlling the curing chamber according to the target curing environment parameters to obtain cured specimens; generating quality inspection data related to the cured specimens when an exit instruction for the cured specimens is received; comparing the quality inspection data with preset exit conditions to determine whether exit is permitted, and controlling the exit operation when exit is permitted. This invention achieves intelligent control of the entire process of specimen entry and exit, while ensuring the accuracy and completeness of the verified specimen exit information.

The application relates to the technical field of maintenance operation management, and particularly discloses a power equipment remote intelligent maintenance system and method, which comprises the following steps: S1: obtaining the oil temperature of a transformer, screening out a first-level monitoring device and a second-level monitoring device, and pre-setting an initial detection time length and a detection node based on historical maintenance times; S2: obtaining equipment data between a current detection node and a previous detection node, calculating a fault value of a first-level equipment based on the equipment data, and screening out a pending equipment; S3: obtaining the resistivity of an insulation resistance in the pending equipment, calculating the conductivity and the electric field intensity, calculating the dielectric loss power of the insulation resistance in the pending equipment, and screening out a maintenance equipment. The application introduces a power equipment remote intelligent maintenance system and method to improve the traditional power equipment maintenance, so that the fault point can be quickly and accurately positioned, and the time and resource waste in the power equipment operation and maintenance process can be reduced.

The invention provides an intelligent construction method and system for a substation hardware equipment platform, and the method achieves the unified management of data and models through the construction of a standardized hardware equipment basic platform. An intelligent operation and maintenance system is built, online infrared and manual dual modes are adopted for temperature measurement, and multi-stage temperature threshold values are set for real-time early warning; an intelligent maintenance system is constructed, a standard operation card is pushed according to a ten-step method, and digital operation is carried out in combination with intelligent wearing; building a hardware fitting digital twinborn model, fusing three-dimensional visualization and temperature simulation, and realizing precise positioning and temperature rise prediction of hardware fitting equipment; and performing'green / yellow / red 'three-color dynamic rating on the hardware fitting by applying an artificial intelligence technology, and intelligently recommending an optimal maintenance strategy. Compared with the prior art, a whole-process closed-loop management system of'monitoring-early warning-diagnosis-disposal 'is constructed, and the quality, efficiency and safety level of equipment management are improved.

The present application relates to the technical field of sponge city, and particularly relates to a sponge city ecological greening intelligent maintenance system, which comprises a base; the base is provided with a sewer device; the sewer device comprises a sewer body, a filter basket and a filter grid; the filter basket is provided with a cleaning mechanism; the cleaning mechanism comprises a cleaning shell; the cleaning shell is provided with a sliding block; the filter basket is provided with a guide seat; the guide seat is provided with a guide inclined groove; a lifting strip is movably arranged in the guide seat; the sewer body is provided with a rotating fan blade at the bottom of the filter basket; the top of the rotating fan blade is connected with a linkage rod; the linkage rod is movably arranged in the guide seat; the outer wall of the linkage rod is provided with a reciprocating spiral groove; and the guide seat is rotatably provided with a rotating pin. The potential energy of rainwater flow is converted into mechanical energy to drive the cleaning mechanism to automatically clean the blockage, so that the traditional manual cleaning and additional power demand are avoided; the design can remove the sundries in real time, prevent the sewer device from being blocked, improve the drainage efficiency and reduce the risk of waterlogging.

The application discloses an intelligent predictive maintenance system for an external pressure type ultrafiltration membrane based on a pollution state sensing, relates to the technical field of ultrafiltration membrane maintenance, and is characterized in that the system is a closed-loop intelligent maintenance system, which is constructed through a collaborative architecture of a sensing layer and an execution layer, data sharing and dynamic optimization decision-making, forms a closed-loop maintenance process of'sensing-analysis-early warning-decision execution', breaks the problem of fragmentation of each link of a traditional system, improves the intelligent level and operation efficiency of the maintenance system, promotes the transformation of ultrafiltration membrane maintenance from 'after-the-fact remediation' to 'prior prediction', and provides a core technical guarantee for the large-scale and efficient application of the ultrafiltration membrane technology.

The invention provides a concrete curing device and method for tunnel construction. The curing device comprises a base, a supporting table, a guide rail, a mounting plate and a spraying pipe, and a driving assembly used for driving the spraying pipe to slide along the guide rail is arranged at the top of the supporting table; the intelligent maintenance system further comprises an intelligent control system, and the maintenance method comprises the steps that the inner wall of the tunnel is scanned through the humidity sensor, humidity information of the concrete surface is obtained, and corresponding coordinate position information is obtained through the positioning module; comparing the humidity information with a preset standard humidity value, and determining a spraying grade according to a humidity difference value; according to the spraying grade, obtaining a corresponding spraying control parameter from a pre-stored spraying strategy table; and the conveying assembly is started, accurate spraying is conducted on the target coordinate position according to the spraying control parameters, and the problem that in the prior art, existing concrete spraying and curing equipment cannot conduct accurate spraying and curing according to the humidity of the inner wall of the tunnel is solved.

The invention relates to the technical field of concrete curing, in particular to a mass concrete temperature control anti-cracking intelligent curing system and method.The mass concrete temperature control anti-cracking intelligent curing system comprises a multi-parameter sensing module used for collecting temperature, humidity and environment temperature and humidity and wind speed parameters; the enclosure maintenance wallboard is arranged on the outer side of the mass concrete and forms an enclosure space, and the enclosure maintenance wallboard at least comprises an adjustable thermal resistance unit, a controlled convection unit, a surface electric heating unit and a wet maintenance water return unit; the edge control module is in communication connection with the multi-parameter sensing module and the enclosure maintenance wallboard, comprises a local calculation unit and a local storage unit, and is used for performing rolling optimization solution under the condition that the maximum temperature, the temperature difference, the cooling rate and the enclosure space humidity constraint are met; and control instructions for the adjustable thermal resistance unit, the controlled convection unit, the surface electric heating unit and the wet maintenance water return unit are output so as to realize cooperative regulation and control. According to the invention, the temperature control efficiency and the quality controllability can be improved.

The application discloses an energy-saving and emission-reducing anti-crack intelligent maintenance system for mass concrete and a mounting method, the maintenance system comprises a heat preservation layer located on the outer side of a mass concrete structure, geotextile is arranged between the mass concrete structure and the heat preservation layer, and a maintenance water pipe is arranged between the geotextile and the heat preservation layer, temperature and humidity adjusting and controlling assemblies are arranged in communication with both ends of the maintenance water pipe, a plurality of temperature and humidity sensors are arranged on the geotextile, and the temperature and humidity sensors are in communication connection with corresponding temperature and humidity adjusting and controlling assemblies; a cooling water pipe is further arranged in the mass concrete structure, and both ends of the cooling water pipe are in communication with the maintenance water pipe. The intelligent maintenance system can adjust and control according to the temperature and humidity of the mass concrete structure, so that the generation of concrete temperature shrinkage cracks and dry shrinkage cracks is effectively reduced.

The invention provides a trimming disc loading and unloading device and an intelligent maintenance system and method.The trimming disc is used for trimming a polishing pad in chemical mechanical polishing equipment, the unloading device comprises a base, a stripping assembly and a grabbing assembly, and lossless and automatic separation of the trimming disc and the trimming disc base is achieved.

The invention discloses a power station intelligent installation and intelligent maintenance system based on a wireless ad hoc network, and relates to the field of power station operation and maintenance technology and industrial internet technology, and the system comprises a plurality of node devices which are used for data collection, edge calculation, wireless networking and data multi-hop transmission and forwarding; a plurality of node devices form a system through wireless networking, and the system has a distributed multi-copy data storage function and is applied to intelligent installation and intelligent maintenance of a power station. The networking mode of a plurality of node devices in the system adopts a decentralized distributed network, a dynamic source routing protocol and an active bridging protocol are used among the node devices, and the protocols are automatically switched according to the number of the node devices in the network to form a Mesh network. According to the invention, a base station, a wireless router or a switch and other network devices are not needed, and the network can be automatically organized to transmit and share data in the network by taking the network as a collection device and a network communication node device.

The invention discloses a thermal power plant boiler equipment intelligent maintenance system and method based on dynamic monitoring, and relates to the technical field of boiler intelligent maintenance. Boiler operation training data, maintenance time length label data and maintenance system label data are collected in advance, and a maintenance time prediction model and a system fault probability prediction model are trained; for a boiler to be monitored, historical overhaul data are collected in advance, actual boiler operation data and actual system efficiency data are collected in real time when the boiler to be monitored operates, and whether overhaul monitoring needs to be carried out or not is judged. If yes, obtaining predicted detection time and the maintenance probability of each operation system, and generating a maintenance sequence of each operation system based on the predicted detection time, the maintenance probability of each operation system and historical maintenance data; the maintenance accuracy of each operation system is quantified for maintenance personnel, and the fault risk of the boiler is reduced.

The invention discloses a side slope ecological restoration intelligent maintenance system and method, and relates to the technical field of ecological restoration, and the system comprises a data collection module which obtains real-time patrol data and environment monitoring signals from a sensor array deployed in a road network, and the environment monitoring signals comprise side slope surface displacement, soil humidity, vegetation coverage and rainfall intensity; the event judging module is used for obtaining the initial position and severity description of the emergency through fusing the multi-source input signals, and judging whether the event exceeds the processing capacity of a single area or not by adopting a preset threshold value according to the obtained initial position and severity description of the emergency; if so, extracting state records and position coordinates of the ecological restoration equipment in the adjacent areas from the central database; the intelligent maintenance system and method for ecological restoration of the side slope have the beneficial effects of intelligent monitoring, rapid response and accurate restoration.

The present invention discloses an intelligent maintenance system and method for the main tower climbing formwork construction. The method includes S1: Before the formwork of the main tower climbing formwork is removed, the temperature and humidity information is collected through the temperature and humidity monitor; S2: Based on the deep learning algorithm, perform a modeling operation on the information to obtain a state classification result; S3: Perform a maintenance operation on the main tower according to the classification result; S4: Train the model in S2 based on transfer learning; S5: After the formwork of the main tower climbing formwork is removed, perform a secondary maintenance operation on the main tower; The system includes a hydraulic climbing formwork frame, a water supply pipeline, a spray head, a temperature and humidity monitor, a control module, and a water supply module. The present application performs intelligent maintenance on the main tower climbing formwork by constructing a transfer learning model based on the deep learning algorithm.