476results about How to "Avoid efficiency" patented technology

The invention relates to the technical field of drying process control, in particular to a medicinal material drying process control method and system for traditional Chinese medicinal material processing, and the method comprises the following steps: calling a temperature set value, a humidity discharge rate and heating power based on the operation condition of a drying chamber, and analyzing the difference value between the medicinal material moisture content and the target moisture content; combining the medicinal material density with the water content change rate, matching the coupling relationship between the humidity discharge rate and the heating power, and establishing an initial drying process control value; according to the method, through collaborative analysis of the temperature set value, the humidity discharge rate and the heating power, accurate matching of initial drying conditions and material characteristics is achieved, excessive drying is avoided, linkage of the air speed and the humidity discharge rate is optimized, humidity distribution uniformity and environmental stability are improved, the evaporation rate is dynamically matched, and the drying efficiency is improved. Drying uniformity and quality stability are improved, termination control parameters are optimized in real time, drying time is shortened, energy consumption is reduced, and active ingredients and physical characteristics of medicinal materials are reserved to the maximum extent.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm and a control method thereof. The method comprises the following steps that real-time load information of all joints of the mechanical arm in the operation process is obtained, the acceleration and rotational inertia of the mechanical arm are monitored in real time, and real-time inertia force data are generated; the torque compensation amount needed by the mechanical arm is calculated according to the real-time inertia force data, and torque compensation control data are generated; performing dynamic analysis on the torque compensation control data, and performing real-time adjustment on the output torque of the servo motor based on the current load and inertia change condition to generate inertia compensation data; and the motion path and the motion speed of the mechanical arm are dynamically optimized according to the inertia compensation data, and path optimization data are obtained. Through real-time monitoring, dynamic adjustment and intelligent optimization, the operation precision, the movement efficiency, the stability and the self-adaptive capacity of the mechanical arm in the complex working environment are remarkably improved.

The invention provides a mold injection control method and device for electric toothbrush plastic molding, and relates to the technical field of plastic injection molding.The method comprises the steps that geometric data of an electric toothbrush and physical performance data of a plastic part are obtained, and an electric toothbrush mold model is initialized; initializing particle swarm parameters, performing material flow path optimization, and performing iteration to obtain an optimized mold model; injection molding simulation is conducted, and mold injection molding performance influence parameters are obtained; constructing an injection molding control strategy library, and performing matching retrieval to obtain a matched injection molding control strategy; selecting interval division is conducted, and a mold injection molding control strategy parameter interval is obtained; constructing a fitness function; global optimization is carried out, target mold injection control strategy parameters are determined, and mold injection control over electric toothbrush plastic forming is carried out. The technical problems that in the prior art, injection molding parameter optimization is always started after mold manufacturing is completed, and control parameters are adjusted through trial and error, so that cost is high, and efficiency is low are solved.

The invention discloses an intelligent dynamic optimization method and system for the battery temperature of a liquid cooling energy storage cabinet, and particularly relates to the technical field of dynamic optimization of the battery temperature, and the method comprises the steps: obtaining a temperature data set in a battery monitoring region in real time; obtaining a battery operation evaluation coefficient in real time, analyzing the battery operation evaluation coefficient, and determining the operation state of the battery; the operation state of the battery comprises a discharging state and a charging state, and a discharging normal instruction or a charging normal instruction is generated respectively; according to the method, the temperature data set and the battery operation evaluation coefficient are input into the pre-constructed neural network model, the future temperature change can be predicted, the temperature change in the discharging and charging processes is analyzed according to the generated discharging or charging optimization instruction, and the targeted temperature optimization strategy is obtained, so that the battery can be effectively optimized when the battery is discharged or charged. A cooling or current control strategy can be dynamically adjusted according to specific conditions, and the problem of battery overheating or low efficiency is prevented.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

The invention discloses a waste gas treatment device for adhesive tape production, and particularly relates to the technical field of adhesive tape production waste gas treatment.The waste gas treatment device comprises a main body, a rotating assembly is arranged in the main body, a filtering assembly is arranged on the outer surface of the rotating assembly, and a spraying assembly is arranged on the upper side of the filtering assembly; and three biological purification assemblies are arranged on the upper side of the spraying assembly. According to the waste gas treatment device for adhesive tape production, in the waste gas treatment process of adhesive tape production, the rotating assembly penetrates through the filtering assembly, the spraying assembly, the biological purification assembly, the scraping assembly, the oxygen injection assembly and other structures, and automatic dust removal through the filtering assembly in the particulate matter filtering step is achieved; the adhesive tape production waste gas treatment device ensures full contact of adhesive tape production waste gas and spraying liquid, achieves cleaning and oxygen supply of the biological purification assembly by utilizing the scraping assembly and the oxygen injection assembly, forms a complete adhesive tape production waste gas treatment chain, and realizes efficient treatment of waste gas.

The invention relates to the technical field of power-assisted robots, in particular to a self-adaptive control lower limb hydraulic power-assisted exoskeleton robot system which comprises a waist supporting device, a leg supporting device, a foot supporting device, a hydraulic power-assisted assembly, an attitude sensor, a lower limb force sensor, a wearable electromyogram sensor, a step obtaining module and a data analysis module. The system can intelligently adjust the output power of the hydraulic power assisting assembly according to the motion posture of the exoskeleton, the pressure data of the foot supporting device and the wearing target muscle state so as to provide proper walking assisting power and bearing support, and the system can further conduct secondary adjustment and fatigue alarm by monitoring the pace frequency and the muscle fatigue degree in real time. The exoskeleton assisting power output power in the carrying process is gradually adjusted according to the comparison result between the weight of the wearing target and the weight of the to-be-moved object, so that assisting power provided by the exoskeleton robot is effectively matched with the weight of the to-be-moved object when the wearing target carries the object.

The invention discloses a foldable composite configuration vertical take-off and landing unmanned aerial vehicle which comprises a fuselage, a front wing, a rear wing, a control plane, an execution mechanism, a rotor wing motor and a propeller, the front wing and the rear wing are connected with the fuselage through a front folding mechanism and a rear folding mechanism respectively, and the front wing and the rear wing are in an X-shaped layout after being unfolded. Rotor motors and propellers are installed on the front wings, control surfaces and execution mechanisms are installed on the rear wings, and the front wings and the rear wings can be folded to be attached to the fuselage. According to the unmanned aerial vehicle, only one power system is used, the unmanned aerial vehicle has the vertical take-off and landing capacity and high flexibility of a multi-rotor unmanned aerial vehicle and the low-energy-consumption cruising and high-speed flying capacity of a fixed-wing aircraft at the same time, the wings can be folded to be attached to the fuselage, the outer contour is cylindrical after folding, and the unmanned aerial vehicle is compact in structure and more convenient to store, transport and carry; in addition, the device can support the modes of barrel type launching, ground takeoff, handheld takeoff, high-altitude spreading and the like for deployment.

The invention discloses a multi-station automatic grinding system. The multi-station automatic grinding system comprises a workbench, a conveying belt set, automatic grinding stations and a plurality of carrying discs. The automatic grinding station comprises a conveying and distributing device, a rough grinding device, a multi-station grinding device and a carrying device; the multi-station grinding device comprises four grinding devices; each grinding device is provided with a cleaning mechanism; the carrying device comprises three carrying mechanisms. Each carrying mechanism is provided with two pushing mechanisms arranged in an array mode and two displacement mechanisms. The carrying disc is provided with an automatic adjusting structure. The invention further discloses a grinding machining method. Full-automatic processing of carrying, distributing, cleaning, grinding and discharging of the carrying disc of the optical fiber head material and recycling of the product and the carrying disc is achieved; the multi-station grinding device composed of a plurality of grinding devices is used for carrying out multiple times of fine grinding processing and standby processing on the optical fiber head, so that the processing efficiency is ensured.

The invention discloses an energy storage converter control method, which relates to the technical field of energy storage converters and is a method for monitoring the running state of an energy storage converter in real time and executing fault protection and / or state machine switching. The energy storage converter fault protection method comprises the following steps: reading a GPIO state or collecting a measurement value of each protection point, and comparing the measurement value with a fault threshold parameter to determine whether a fault occurs; when a fault occurs, a fault bit corresponding to the fault code register is activated, and the state machine is switched to a fault state and executes a fault protection program; the energy storage converter state machine switching method comprises the following steps: detecting whether a master control word register jumps or not compared with a previous period and executing corresponding operation; and executing corresponding control logic according to the current state of the state machine, wherein the states comprise a shutdown state, an alternating current pre-charging starting state, a standby state, an operation state, a fault state and a synchronous phase pulse test state. According to the method, the fault can be quickly responded, and the operation stability and safety of the PCS are ensured.

The utility model discloses a high-speed supporting box packaging machine which is characterized in that a feeding conveying line is arranged on the upper surface of a rack, an unwinding assembly is arranged above the rack, a bag making device for making a film into a barrel bag is arranged at an unwinding outlet of the unwinding assembly, a heat-sealing film-drawing assembly is arranged at a conveying outlet of the feeding conveying line, and the bag making device is connected with the feeding conveying line. A first telescopic conveying line and a second telescopic conveying line are arranged in the conveying direction of the heat-sealing film-drawing assembly, and a heat-sealing cutting device is arranged between the first telescopic conveying line and the second telescopic conveying line. By means of the integrated design, a plurality of procedures such as film unwinding, bag making, heat sealing, cutting and conveying are integrated in the same equipment, operation continuity and automation degree are remarkably improved, and the problems that traditional manual or semi-automatic operation is low in efficiency and pollution is prone to being introduced are solved. And low efficiency and potential sanitation hazards caused by manual operation are avoided, the high-efficiency requirement of large-scale production is met, and the overall packaging efficiency is improved.

The invention, which relates to the technical field of photovoltaic panel cleaning detection, discloses a photovoltaic panel dust covering detection method and system based on an unmanned aerial vehicle inspection picture, and the method comprises the steps: obtaining an image of a to-be-detected region in an RGB format, and carrying out the target detection of a photovoltaic panel through an optic neural network model; segmenting a photovoltaic panel image according to a detection result, performing degradation processing to obtain a low-resolution photovoltaic panel image, inputting the low-resolution photovoltaic panel image into the super-resolution model, and outputting a high-resolution photovoltaic panel image; and constructing a dark channel model and an atmospheric scattering model, carrying out defogging operation on the high-resolution photovoltaic panel image to obtain a first photovoltaic panel image, and carrying out gray covering condition classification by constructing and training an L-CNN lightweight classification model. According to the invention, the image resolution is improved through the super-resolution model; haze noise in the image is effectively removed through the dark channel model and the atmospheric scattering model, and the image definition is improved; by means of a lightweight L-CNN classification model, the dust covering condition of the photovoltaic panel is identified and classified, and personal errors are reduced.

The invention relates to the technical field of clustering analysis, and particularly discloses a fruit growth monitoring system, which comprises an acquisition module for acquiring a target value at a time point, acquiring target data based on the target value, and generating a target point; the clustering analysis module is used for setting a clustering radius and clustering the target points according to the density of the target points in the clustering radius; the clustering optimization module is used for adjusting a clustering cluster according to the theoretical gravity center and the maximum distance when the density of the target points in the overlapping region meets the condition; and the result module obtains the standard points and judges the growth state of the fruits based on the standard points. According to the invention, the efficiency of monitoring the growth state of the fruits can be improved.

The invention relates to the technical field of drilling waste treatment, in particular to a drilling slag slurry multi-stage circulation treatment system and method.The drilling slag slurry multi-stage circulation treatment system comprises an automatic grading module, a dynamic monitoring module, a slurry purification module, a separation optimization module, a waste slag classification recycling module and an intelligent control module; wherein the automatic grading module comprises a particle identification unit and a variable frequency grading unit; the particle identification unit is used for carrying out real-time detection on the size, the form and the density of particles in the drilling slag and the slurry through a multispectral sensor; the variable-frequency grading unit is used for adjusting the grading speed according to the detection data provided by the particle identification unit and carrying out dynamic grading on the particles; the dynamic monitoring module comprises an online sensor and a data analysis unit; according to the invention, through intelligent monitoring and automatic adjustment technologies, efficient grading, filtering and separation of drilling slag slurry and intelligent classification of waste slag are realized, and efficient operation of the system, improvement of the resource recovery rate and reduction of environmental pollution are ensured.

The invention relates to the technical field of data processing, and discloses a BIM model storage system and method based on lightweight data, and the system comprises a tree segment management module which is used for constructing a tree segment structure, and taking each segment as a storage unit; the segment multiplexing creating module is used for creating multiplexing segments in the tree-shaped segment structure, realizing data multiplexing in a reference mode, and creating style segments for centralized management of drawing attribute information; the parallel rendering module is used for rendering the model primitives by adopting a parallel computing method based on the geometric information and the attribute information in the multiplexing segments; the lightweight data generation module is used for organizing the BIM model data according to a tree-shaped segmentation structure and generating a binary data file; and the data compression module is used for compressing the binary data file to generate a compressed format storage file. The technical problem that efficient storage, transmission and application requirements of the BIM model are difficult to meet in the prior art is solved.

The invention discloses an automatic auditing business process control method based on rule optimization. The method comprises the following steps: S1, generating a unified auditing data pool; s2, constructing a multi-layer rule engine, and generating an initial rule set; s3, initializing an improved bee colony optimization algorithm, and dynamically defining a search space of a population; s4, dynamically optimizing the initial rule set by using an improved bee colony optimization algorithm, adjusting a single rule parameter in a search space in a worker bee stage, selecting an optimal rule direction in an observation bee stage, and introducing collaborative bees to optimize conflicts among multiple rules, and re-initializing a low-diversity population in a reconnaissance bee stage, and generating an optimized rule set; s5, generating a rule execution plan, adjusting the node priority of the auditing process and optimizing a task path; and S6, realizing continuous optimization iteration of the rule set and the audit process path based on implementation feedback of the rule execution plan. According to the invention, the multi-layer rule engine and the improved bee colony optimization algorithm realize automatic auditing business process control.

The invention relates to the technical field of underwater dredging construction. The underwater dredging construction method comprises the steps that environment information of a to-be-constructed area is obtained, the environment information is input into a sludge physical property model, sludge physical property information is obtained, the sludge physical property information is compared with a preset sludge standard value, a comparison result is obtained, and the sludge type is determined based on the comparison result. And selecting a sludge construction mode corresponding to the sludge category based on the sludge category, adjusting working parameters of the construction equipment according to the sludge construction mode, and after the working parameters are adjusted, executing dredging construction until the dredging task of the to-be-constructed area is completed. The method has the effects of improving the construction efficiency and reducing equipment damage.

The embodiment of the invention discloses an ear canal CT image segmentation method based on improved nn-UNet, and the method comprises the steps: collecting and marking the ear canal CT data of a plurality of patients, and making a data set; preprocessing the data; training on a training set by using a model improved based on nn-UNet, dividing a data set for multiple times by using a five-fold cross validation method, then training and verifying each fold, and storing a training model after training is completed; reasoning and testing the trained model on the test set data which are not seen; post-processing is carried out, and an output result of the model is optimized; and formulating an evaluation scheme to evaluate the prediction result. According to the method, the segmentation efficiency and accuracy can be greatly improved, and subjective deviation and human resource waste possibly generated in the manual segmentation process can be effectively avoided.

The invention provides a circuit board test method and device based on a flying probe tester. According to the method, a test point set is determined by obtaining a design file of a circuit board, establishing an electrical connection model and identifying components and pins on the circuit board. And generating test combination set data by analyzing the conduction relationship between the test points, and further generating test scheme data. And based on the test scheme data, a test path is generated by using a path optimization algorithm, finally, the flying probe tester is used for testing, and a test report is generated according to a test result. The flying probe tester adopts the combination of an annular probe and a sharp needle probe, the annular probe is used for a pin connector to ensure accurate contact, and the sharp needle probe is used for other types of components to improve the test precision and adaptability. Compared with a traditional test method, the test scheme can be generated more efficiently, the test path can be optimized more efficiently, the test time is remarkably shortened, and the reliability and accuracy of circuit board testing are improved.

The invention relates to the technical field of geotechnical engineering, and discloses a wall-attaching type microwave rock crushing method and device suitable for shield tunneling machine loading, and the method comprises the steps: obtaining an image of a surrounding rock tunnel face in real time through image collection equipment, and carrying out the preprocessing; analyzing the preprocessed image of the tunnel face of the surrounding rock, and analyzing the surface state of the surrounding rock by using a computer vision algorithm to obtain the current hardness of the surrounding rock; judging whether a microwave system needs to be started according to the current hardness of the surrounding rock; if the current hardness of the surrounding rock is smaller than the preset threshold value, the pushing system and the microwave system are not started; if the current hardness of the surrounding rock is larger than or equal to the preset threshold value, the control module starts the pushing system to push the microwave system to make contact with the surrounding rock tunnel face; meanwhile, a microwave system is started to heat the surrounding rock; in the working process of the microwave system, the images of the tunnel face of the surrounding rock are continuously obtained and analyzed, the hardness of the surrounding rock is judged according to the image analysis result, and the precision and adaptability of the microwave rock crushing technology can be improved.

A continuous induction quenching device for two ends of an automobile push rod is used for manufacturing the automobile push rod. Two conveying lead screws are installed in the middle of the quenching machine tool, the head ends of the conveying lead screws are in butt joint with mistake-proof feeding devices above the conveying lead screws, inductors are arranged on the two sides of the middle sections of the conveying lead screws and connected with variable vibration devices and heating power sources, blanking grooves are formed in the tail ends of the conveying lead screws, and quenching liquid grooves are installed below the conveying lead screws. A spiral groove of the conveying lead screw is matched with the diameter of the rod portion of the push rod, and the ball head end and the ball socket end of a discharging port of the mistake-proof feeding device are matched with a ball head and a ball socket at the two ends of the push rod respectively. According to the invention, simultaneous quenching of the ball head and the ball socket at the two ends of the push rod can be realized, the problems of low efficiency and overlarge cost input caused by separate quenching of multiple devices in the prior art are solved, wrong quenching, re-quenching and missing quenching in the machining process are avoided, and high dependence on personnel skills and operation attention is avoided; quenching efficiency of the ball head and the ball socket at two ends of the push rod can be improved, product quality can be guaranteed, and manufacturing cost can be reduced.

The invention discloses a bridge support displacement online probability prediction method, which comprises the following steps: acquiring historical monitoring data of main beam temperature and support displacement, and introducing a group of false points to construct a probability prediction model based on a flow sparse Gaussian process, according to the approximate posterior distribution and the real posterior distribution, constructing a variational free energy boundary as a lower bound of a logarithmic marginal likelihood function, deriving an optimal variational free energy boundary and optimal approximate posterior distribution, and obtaining a specific expression of a probability prediction model; when new monitoring data is obtained, new monitoring data and old historical monitoring data are summarized in combination with new false points and old posterior distribution, new approximate posterior distribution is derived, new real posterior distribution is derived by using the Bayesian theorem, then model parameters and posterior distribution are updated online, and a new specific expression of the probability prediction model is obtained. According to the method, massive health monitoring flow data of the bridge is processed based on the flow sparse Gaussian process model, online probability prediction of bridge support displacement is achieved, and the method has high engineering application value.

The invention particularly relates to a solar charging type dock and an unmanned ship, and relates to the technical field of unmanned ships, the solar charging type dock comprises an unmanned ship body and an upper bin body arranged on the upper side of the unmanned ship body in a communicating mode, a charging socket is installed on the rear side of the unmanned ship body, and inner slopes are obliquely arranged on the left side and the right side of the bottom of the unmanned ship body correspondingly; a plurality of clamping grooves are formed in the middle of the inner inclined face, the lifting movable plate can be arranged in the unmanned ship body in a suspended mode according to the magnetic principle that under the condition that a lower electromagnet in the unmanned ship body is powered on, the same poles of the lower electromagnet and an upper floating magnet repel each other, and a space frame on the lifting movable plate is used for installing electric appliance accessories of the unmanned ship. The rubber floating air bag is fixedly arranged on the periphery of the lifting movable plate, the buoyancy of the lifting movable plate can be improved, when the water level of water entering the unmanned ship body is high, the rubber floating air bag enables the lifting movable plate to float, and the water level of the water entering the unmanned ship body is high. Therefore, the use effect is improved.

The invention discloses an overhead power transmission line foreign matter cleaning device, and belongs to the technical field of power transmission lines, the overhead power transmission line foreign matter cleaning device comprises a first mounting rack and a second mounting rack, one side of the second mounting rack is fixedly connected with a fixed rotating shaft, and a fixed base is provided with a fixed cylinder cleaning assembly. And a cleaning assembly is installed in the fixed cylinder cleaning assembly, a limiting rotating mechanism is installed on one side of the fixed cylinder cleaning assembly, device driving mechanisms are installed on one side of the first mounting frame and one side of the second mounting frame correspondingly, and fixing assemblies are installed on the corresponding sides of the first mounting frame and the second mounting frame correspondingly. By arranging the fixed cylinder cleaning assembly and the limiting rotating mechanism, the fixed cylinder cleaning assembly can achieve rotating cleaning, and meanwhile, when a bristle plate and a wet sponge plate are abraded or damaged, the bristle plate and the wet sponge plate can be rapidly taken down from the first mounting cylinder and the second mounting cylinder and replaced.

The invention relates to the technical field of wastewater treatment, and discloses an aluminum oxidation wastewater treatment system and method, and the system comprises a tank body, the top end of which is connected with a top cover through a bolt; the crushing feeding assembly is arranged on the top cover and is used for crushing large-particle impurities in the sewage; the stirring assembly is arranged in the tank body and is used for stirring sewage in the tank body; the temperature control assembly is arranged on the outer wall of the tank body and is used for assisting in purifying the sewage; the filtering assembly is arranged at the bottom end of the tank body and is used for filtering the sewage subjected to pH regulation again. Through cooperation of internal structures of the crushing feeding assembly, large-particle impurities in sewage entering the hopper are crushed, meanwhile, the amount of sewage entering the tank body is controlled, and the problems that due to impurity blockage, system operation is unstable, and the sewage treatment efficiency is reduced are solved.

The invention provides a capacitance detection method, system and device and a storage medium, and relates to the technical field of capacitance detection. The method comprises the following steps: acquiring a capacitance type and a parameter range of a measured capacitor, and matching with a preset resonance library according to the capacitance type and the parameter range to obtain a target resonance inductance parameter; constructing a target LC resonance test circuit based on the target resonance inductance parameter, the capacitor type and the parameter range, and measuring the tested capacitor based on the target LC resonance test circuit to obtain resonance response data; and analyzing the resonance response data to obtain a capacitance performance index. Through the scheme, the capacitor testing efficiency is improved.

The invention relates to the technical field of artificial intelligence, and provides an occupied port detection method and device, equipment, a storage medium and a product. The method comprises the steps of obtaining a to-be-detected image; inputting the to-be-detected image into the target detection model to obtain a port position detection result; based on the port position detection result, determining a currently occupied port of the optical cable cross connecting cabinet to be subjected to quality inspection; the target detection model is obtained by pre-training an initial model constructed based on a YOLO-EF algorithm based on a preset loss function, a sample detection image and a sample port position detection result; the sample port position detection result is used for adjusting model parameters of the initial model, so that the preset loss function conforms to a preset threshold value. Through the above mode, a semantic segmentation model does not need to be adopted, the problems of low detection efficiency and low accuracy of the semantic segmentation model can be avoided, accurate detection of the ports occupied by the optical cable cross connecting cabinet is realized by using the deep learning model, and improvement of the quality inspection accuracy of the optical cable cross connecting cabinet is facilitated.

The invention discloses a method and device for predicting the sealing performance of an electrical penetration assembly and a storage medium, and the method comprises the steps: obtaining the structure attribute information of a target electrical penetration assembly in response to a prediction signal of the sealing performance of the target electrical penetration assembly, external environment attribute information of the target electrical penetration assembly at different moments and gas attribute information of gas in a barrel of the target electrical penetration assembly at different moments are collected in real time, and the gas attribute information comprises gas temperature and gas pressure of the gas in the barrel of the target electrical penetration assembly at different moments; based on the gas temperature, judging whether the target electrical penetration assembly meets a preset condition of sealing performance monitoring or not; and if the target electrical penetration assembly meets the preset condition, predicting the sealing performance of the target electrical penetration assembly based on the structure attribute information, the external environment attribute information and the gas attribute information.

The invention provides an index system construction method and system for electricity market operation, and the method comprises the steps: integrating data from different sources of an electricity market, and carrying out the preprocessing of the data; pre-processed data are obtained, and self-supervised data extraction is carried out on the data from the pre-processed data; constructing a power market multi-task meta-learning optimization model; a real-time operation index system of the electricity market is constructed and dynamically adjusted; a multi-target strategy optimization algorithm is constructed, and an optimal market strategy is designed according to the market indexes adjusted in real time; and carrying out system learning and system adaptive updating according to the optimal market strategy, and keeping the optimal performance. By combining meta-learning and self-supervised learning technologies, the system can quickly adapt to different market environments and scenes, automatically mine and generate key operation indexes from large-scale unmarked power market data, and realize real-time updating and optimization of the index system.

The invention relates to the technical field of ship task scheduling, and discloses a pure electric unmanned ship battery safety management control method, which comprises the following steps: step S101, collecting historical task data, constructing and training a random forest model, and obtaining required electric quantity of M tasks; s102, collecting second task feature data of the M tasks and ship feature data of the U ships; s103, constructing a fleet task atlas, constructing an adaptation degree prediction model according to the fleet task atlas, and outputting the adaptation degree between the ship and the task; and step S104, generating a distribution scheme according to the adaptation degree prediction model in combination with an improved geyser optimization algorithm, wherein the distribution scheme represents that the task is distributed to the ship. According to the method, the optimal allocation scheme is automatically generated by constructing the adaptation degree prediction model and combining the improved geyser optimization algorithm, maximum coverage and reasonable allocation of task execution are achieved, and the energy utilization efficiency is improved.