206results about How to "Avoid hysteresis" patented technology

The invention provides a range hood control method and system based on the Internet of Things, and relates to the technical field of artificial intelligence and the Internet of Things, and the method comprises the steps: obtaining multi-source layered environment characteristic data of heating power, oil smoke and airflow through constructing a layered data collection network, and carrying out the processing of edge calculation and a cloud server, drawing force required by each area is calculated, a self-adaptive fan control curve is generated, and then an initial control parameter set containing a main fan rotating speed value, an auxiliary fan rotating speed value and an air deflector angle value is generated; the method comprises the following steps: constructing a vortex flow prediction model based on a vortex tensor and a velocity divergence by using a cloud edge collaborative architecture, predicting an airflow movement track, calculating a real-time airflow deviation degree, dynamically adjusting an initial control parameter group to generate a target control parameter group, and finally continuously optimizing the prediction model through a deep reinforcement learning network.

The invention discloses an Internet of Things card anomaly detection method and system based on an improved K-means algorithm. The method comprises the following steps: S1, collecting and preprocessing feature data of an Internet of Things card; s2, performing clustering analysis on the preprocessed feature data by using an improved K-means algorithm; s3, calculating a distance threshold value of abnormal judgment according to a clustering result; and S4, judging whether the to-be-detected Internet of Things card is abnormal or not according to the threshold value. The system comprises a data preprocessing module, a clustering analysis module and an anomaly detection module. The invention aims to provide an efficient and intelligent anomaly detection method for the use behavior of the Internet of Things card through an improved K-means algorithm, and by automatically identifying the abnormal use behavior of the Internet of Things card in different application scenes, the accuracy of management and safety monitoring is improved, and malicious abuse and illegal behaviors are reduced.

The invention provides a multimedia content AI detection method and device, a storage medium and electronic equipment. The method comprises the steps of performing preprocessing and feature extraction on input multi-modal data; extracting emotion and behavior risk features related to hidden illegal behaviors from the multi-modal feature representation; generating a novel violation template through zero sample learning, and matching the novel violation template with newly input multi-modal features to obtain a novel violation feature template and a matching score; performing semantic alignment on the novel violation feature template and the multi-modal features, and generating a preliminary violation risk score according to an alignment result and a matching score; and generating a structured report by integrating the grading result, the high-risk fragment index and the feature contribution degree. According to the innovative method and system, efficient detection and accurate recognition of the multi-modal violation content are achieved by combining emotion decoding, user behavior analysis and zero sample learning technologies, and therefore the defects in the prior art are overcome.

The invention discloses a manufacturing method of a two-dimensional semiconductor field effect transistor based on a high-k gate dielectric integration technology capable of eliminating a buffer layer. The manufacturing method comprises the following steps: depositing a back gate electrode and a back gate dielectric on a substrate in sequence; transferring the channel layer to the back gate medium; sequentially forming a buffer layer, a source electrode, a drain electrode, a top gate dielectric layer formed by a high-k dielectric material and a top gate electrode on the channel layer; and annealing treatment is carried out in a vacuum environment, and the buffer layer sublimates, so that Van der Waals interfaces are formed between the channel layer and the top gate dielectric layer and between the channel layer and the source electrode as well as between the channel layer and the drain electrode. According to the high-k gate dielectric integration technology based on the erasable buffer layer, the erasable buffer layer after vacuum annealing is utilized, damage to a two-dimensional material is avoided, a high-quality high-k gate dielectric is deposited, interface regulation and control between a channel layer and a top gate dielectric are achieved, and the equivalent oxide thickness is reduced; and good contact between the channel layer and source and drain metal is realized, the contact resistance of the transistor is reduced, and the electrical performance of the transistor is improved.

The invention relates to a portrait marking method and device based on flow data, and computer equipment. The method comprises the following steps: performing multi-level time sequence analysis on historical active flow data, historical interactive flow data and historical passive flow data of a target object to generate an initial marking portrait; according to the current active flow data, the current interactive flow data and the current passive flow data of the target object, updating the features and the label of the initial marking portrait to obtain a current marking portrait; the classified scene flow data, the classified field flow data and the current marking portrait of the target object are mapped into the same embedding space, and a fused marking portrait is obtained; and according to the current active flow data, the current interactive flow data and the current passive flow data, adjusting a portrait output style of the fused marking portrait to obtain a target marking portrait. According to the method, the accuracy of the image in the marking process can be improved, and potential new rules in data can be captured in time.

The invention discloses a bioreactor process optimization system and method, and relates to the technical field of bioreactors. Comprising the following steps: collecting historical reaction process data of a bioreactor, and obtaining real-time reaction process data of the bioreactor; analyzing the historical reaction process data to obtain different influence results of different adjustment modes on the biological reaction process; establishing a cell culture model according to a corresponding relation obtained by different influence results of different adjustment modes on the biological reaction process; the real-time reaction process data is obtained, the biological culture process is influenced by adjusting the bioreactor, the specific adjustment mode is obtained through the real-time reaction process data and the required adjustment result, the required adjustment result can be obtained after the reaction in the adjustment mode obtained through backstepping, hysteresis is avoided, materials are saved, and the efficiency is improved. And the problem of poor subsequent biological reaction caused by excessive material addition, excessive design parameter adjustment and poor subsequent biological reaction due to hysteresis is also avoided.

The invention provides an intelligent power grid safety early warning system and a monitoring method in the technical field of intelligent power grids, which are used for solving the problem of safety early warning and monitoring function defects of the existing imperfect intelligent power grids, and comprise an information acquisition unit, a data management unit, an abnormity analysis unit and a safety early warning unit, the method comprises the steps of collecting power grid real-time data and line equipment data; constructing an adjacent matrix and a target matrix, calculating and analyzing to obtain a node weight and a target correlation degree, and analyzing by using a discrete model to obtain a dispersion index; a data evaluation value is obtained, and a data abnormal value is obtained through analysis; and generating intelligent power grid safety early warning information and a disaster recovery strategy according to the data abnormal value and the line equipment data. By constructing an intelligent power grid monitoring and early warning system, power grid equipment with potential safety problems is predicted in real time, the operation and maintenance efficiency is improved, the method can be suitable for power grid systems of different scales, the influence of faults on power grid operation is reduced, and large-area power failure events are effectively prevented.

The invention discloses a power grid construction subpackage management and control method based on big data, and the method comprises the following steps: collecting the data of a power grid construction site in real time, carrying out the preprocessing, and carrying out the storage through a distributed storage system; generating a comprehensive evaluation report by using the residual neural network and the graph convolutional network; adopting a particle swarm optimization algorithm to carry out subpackage provider selection and task allocation; predicting the construction progress through a long short-term memory network; performing image analysis on the video stream of the construction site by using a residual neural network and a single-time multi-frame detection algorithm; a Bayesian network and a multi-layer perceptron are used to predict construction risks, an isolated forest algorithm is used to carry out abnormal value detection on data, and a risk early warning report is generated and notified to related personnel. According to the invention, big data, deep learning and reinforcement learning technologies are fully combined, the power grid construction subpackage management and control method based on big data is described in detail, and the method has the advantages of high construction efficiency, accurate risk prediction and resource optimization.

The invention aims to provide an efficient memory allocation and recovery method for an Android device, and relates to the technical field of memory processing, the method comprises the following steps: step 1: obtaining running data of the Android device, and calculating a memory state entropy based on the running data; calculating a memory fragment risk degree in combination with the priority matrix and the operation data; 2, calculating a memory allocation dynamic threshold according to the memory fragmentation risk degree; calculating a self-adaptive memory compression rate; step 3, using a self-adaptive memory compression rate to compress memory data which is not used by the Android device at present; the page number of the activity process is lower than the set minimum page threshold value; 4, if the memory fragmentation risk degree exceeds a set fragmentation threshold value, the Android device executes memory fragmentation; and averagely distributing the residual memory capacity to all the active processes. According to the invention, accurate allocation and efficient recovery of memory resources are realized.

The invention discloses a case anomaly detection method and system based on deep learning, and relates to the technical field of medical images. Comprising a data collection and preprocessing module, a multi-modal feature extraction module, a feature fusion module, a context information integration module, an anomaly detection model module, a model training and optimization module, a model iteration training and verification module and a real-time monitoring and feedback module. The method further comprises the following steps: step 1, collecting medical image data from different modalities, and carrying out denoising and standardization processing on the medical image data; 2, extracting features of different modal images by using a deep convolutional neural network, and fusing the features to form a comprehensive feature vector; 3, combining the clinical information of the patient with the fusion features to generate a comprehensive feature vector; 4, analyzing the fused feature vector through the trained deep learning model, and outputting an anomaly detection result; 5, the system monitors a new case in real time, automatically prompts abnormity and continuously updates the model according to feedback of a doctor.

The invention provides a method for regulating and controlling technical parameters in a continuous preparation process of a filling material, and relates to the technical field of mine filling automatic control. According to the method, aiming at the condition that the material level of the stirrer is in a safety interval, pumping pressure-stirrer material level double-rate interval control is adopted, that is, the material level of the stirrer is maintained in a safety range required by a process, and meanwhile, the pumping pressure for conveying a filling material required by underground filling is ensured; after the pumping pressure and the material level of the stirrer are both stable, aiming at the condition that the concentration of the slurry pump is not in a set concentration range, adopting concentration fuzzy PID control, and adjusting the concentration of the slurry pump until the concentration is stable in the set range; aiming at the conditions that the concentration of a slurry pump is in a set concentration range and the frequency of a stirrer is in a normal operation range, torque fuzzy PID control is adopted, the frequency of the stirrer is adjusted, and energy conservation and emission reduction are achieved on the premise that the shear stress of a filling material is stabilized.

The invention provides a photovoltaic array installation error detection and automatic correction system, and relates to the field of photovoltaic technology. The photovoltaic array installation error detection and automatic correction system comprises a data acquisition layer, the data acquisition layer is connected with an edge processing layer, the edge processing layer is connected with a central processing layer, the central processing layer is connected with an execution layer, the execution layer is connected with a user interaction layer, and the user interaction layer is connected with a management and maintenance layer. Through dynamic reference generation and self-learning optimization, an optimal angle reference can be intelligently generated according to real-time environment change, an error detection and correction algorithm can be continuously optimized, prediction and advanced adjustment functions are combined, calculation is carried out in advance, a correction instruction is issued, it is ensured that the photovoltaic panel is always located at the optimal power generation position, and the power generation efficiency is improved. And meanwhile, an unattended automation technology is utilized, so that the maintenance cost and the manpower demand of the system are reduced, and the overall operation efficiency and reliability are improved.

The invention relates to the technical field of artificial intelligence, and discloses a natural resource ecological protection and restoration engineering monitoring method, which comprises the following steps: acquiring image data of an ecological region, and generating an environment change index of the ecological region according to the image data and a preset environment change index generation algorithm; generating an environment change type of the ecological region according to the environment change index; generating a species community and an ecological niche of the ecological region according to pre-acquired species data; generating a biological change index of the ecological region according to the species community and the ecological niche; and generating a monitoring report of the ecological region according to the environmental change type and the biological change index, and performing ecological protection and ecological restoration on the ecological region according to the monitoring report. According to the invention, the efficiency of natural resource ecological protection and restoration engineering monitoring can be improved.

The invention discloses an energy-saving and carbon-reducing control method based on AI intelligent assistance and a comprehensive management platform, and the method comprises the steps: obtaining the energy-saving data of each piece of equipment in real time, and calculating the carbon emission of each piece of equipment according to each piece of energy-saving data; a carbon emission plan and a characteristic curve of each device are obtained, and the optimal carbon emission of the target factory is determined; determining a target electric energy distribution plan of each device of the target factory in a future preset time period according to the optimal carbon emission; receiving planned execution data of each device, and calculating the actual carbon emission and the actual carbon saving amount of each device according to the planned execution data and the carbon emission of each device; and according to each equipment ID and the corresponding actual carbon emission and actual carbon saving amount, newly adding the attribute of the data object of each equipment in the data twinborn model of the target factory, so as to display the actual carbon saving amount and actual carbon emission amount of each equipment of the target factory in real time through the data object in the data twinborn model in the data twinborn module.

The embodiment of the invention relates to a method and system for controlling the rotating speed of a cooling fan of a loader and a medium, and belongs to the technical field of cooling fans of loaders, the control method comprises the steps that the double-closed-loop control process of the rotating speed of the fan is executed, and the rotating speed of the fan is calculated through a PD calculation method; based on the rotating speed target value of the cooling fan, the actual hydraulic pressure value at the current moment and the actual hydraulic pressure values at the first two moments, an expected output current value at the current moment is obtained; calculating the expected voltage value of the electric control valve core at the current moment based on the expected output current value at the current moment, the actual output current value at the current moment, the actual output current value at the last moment and the target rotating speed value of the cooling fan by utilizing a PI calculation method; accurate control of the rotating speed of the fan is achieved, the hysteresis problem of a control valve element is avoided, and the rotating speed of the cooling fan is closer to the ideal target rotating speed.

The invention relates to the technical field of intelligent monitoring, regulation and control of welding process parameters, and particularly discloses an intelligent regulation and control method and system for resistance welding process parameters of a junction box. Abnormal pressure applying links in the welding process are recognized, and the low-risk pressure applying process is extracted for welding; after welding is completed, the strength and size indexes of welding spots are evaluated through welding spot quality detection, the welding spot quality deviation coefficient is calculated, and whether the welding quality reaches the standard or not is judged; and for the welding spots with unqualified welding quality, the system calculates the adjustment direction and range of the pressure parameters through an optimization algorithm, test welding is conducted according to the optimized parameters, and the improvement effect of the pressure applying process is verified in real time till the welding spot quality meets the preset standard.

The invention provides a light curtain fault detection method and system, and relates to the technical field of light curtain fault detection, and the method comprises the steps: detecting the first signal intensity of a light beam emitted by an emitter end of a light curtain in real time, and the second signal intensity of the light beam received by a receiver end; if it is detected that the second signal intensity is smaller than or equal to the first signal intensity, and the difference between the second signal intensity and the first signal intensity exceeds a first preset threshold value, the calibration tool is controlled to conduct offset detection on the light curtain, and offset is obtained; if it is detected that the offset exceeds a second preset threshold value, light curtain fault early warning is carried out; and if it is detected that the second signal intensity is greater than the first signal intensity and the difference between the two is greater than or equal to the first signal intensity, performing light curtain fault early warning. By implementing the method, fault detection can be carried out in real time in the working process of the light curtain, the fault detection efficiency is improved, and potential safety hazards are reduced.

The invention relates to the technical field of wastewater treatment, and provides a reverse osmosis (RO) concentrated water treatment method which comprises the following steps: step 1, treating sewage discharged by an RO concentrated water tank through a first catalytic ozonation module, a biological aerated filter module, a second catalytic ozonation module and a drainage reflux module in sequence; 2, online water quality detectors arranged at the water inlet ends of the first catalytic ozonation module, the biological aerated filter module, the second catalytic ozonation module and the drainage backflow module are used for monitoring water quality indexes in real time, and detection data are transmitted to an automatic control system; on-line water quality index detection is adopted, hysteresis of off-line detection is avoided, unattended operation, self-control operation and high process operation flexibility can be achieved, the method is suitable for application scenes with huge incoming water quality fluctuation, and parameters of the selected process module are easy to adjust and switch.

The utility model discloses a hoisting type conveying belt automatic returning device which comprises a fixed machine frame, a rotary machine frame, a carrier roller mechanism and an on-site machine frame, the fixed machine frame comprises a cross beam frame and two supporting frames symmetrically installed at the two ends of the cross beam frame, and the bottoms of the supporting frames are fixedly connected with the on-site machine frame; the rotary rack comprises an adjusting beam frame and two lifting frames symmetrically installed at the two ends of the adjusting beam frame. The middle of the adjusting beam frame is rotationally connected with the middle of the beam frame through a rotating assembly. The carrier roller mechanism comprises a straight roller assembly and two conical roller assemblies installed on the two sides of the straight roller assembly, and the outer side ends of the two conical roller assemblies are fixedly connected with the two sides of the rotary rack. The utility model provides a mechanical automatic return device applied to a hoisting type conveying belt, which can realize automatic adjustment and real-time correction after the belt deviates. The daily workload of maintenance personnel is greatly reduced; and the height, the length and the interval adjustment of the groove angle of the carrier roller of the automatic return device of the conveying belt can be realized, and various field application requirements can be met.

The utility model belongs to the technical field of sintering machine air bellow gas temperature measurement, and particularly relates to a novel sintering machine air bellow gas temperature measurement device. Which comprises a sintering machine air bellow, and an air bellow retaining wall, an air bellow supporting beam and a track supporting beam are arranged at the bottom of the sintering machine air bellow, and is characterized in that a primary protection pipe is welded to the air bellow retaining wall, the other end of the primary protection pipe is welded to the air bellow supporting beam, and a protection cover bottom plate is welded to the upper portion of the primary protection pipe through T-shaped steel; the protective cover bottom plate is coated with a protective cover coating, and a plurality of secondary protective pipes with one ends penetrating through the lower side of the primary protective pipe are connected into the primary protective pipe in a sleeved mode. According to the air bellow gas measuring device, multi-layer protection can be carried out on the independent thermocouples, washing of sintered ore is avoided, the measuring precision is guaranteed, the independent thermocouples can be rapidly replaced, the service life of the air bellow gas measuring device is prolonged, operation is reliable, and the manufacturing cost is low.

The invention relates to a coal slime flotation froth three-dimensional model classification method based on a Pointnet network, and aims to solve the technical problem that the current two-dimensional froth image cannot process the states of three-dimensional froth space fluctuation and the like and the internal relationship of the space, and the technical scheme is as follows: expanding a data set; designing a foam characteristic network architecture; extracting a high-dimensional feature lambda 1 by using a point cloud feature extractor f theta; projecting the high-dimensional feature lambda 1 into a low-dimensional space by using a feature projection head # imgabs0 # to obtain a low-dimensional feature lambda 2 of the single-groove foam point cloud; splicing and fusing the single-groove foam point cloud features by using the feature fusion function yf to obtain an overall feature gamma; classifying the overall feature gamma through a multi-layer perceptron (MLP) and a Softmax function, and outputting a classification result; evaluating and analyzing. According to the method, the foam three-dimensional point cloud models of a plurality of flotation cells are used as input, the limitation of a single-cell data source is made up, the generalization ability and stability of the model are improved by using the flotation machine overall feature training network, and the performance of the model is optimized.

The invention discloses a vehicle multi-domain fusion control method, and relates to the technical field of vehicle multi-domain fusion control, and the method comprises the steps: collecting and analyzing the data of each domain in real time through a central calculation unit CCP, rapidly judging whether the configuration parameters need to be updated or not when the working conditions change, and rapidly responding to various complex working conditions. In addition, an A2L file is automatically called through a CCP to be compared with a newest project configuration file, parameter items needing to be updated are automatically recognized, redundant data are eliminated, and then synchronous updating of configuration parameters of all control domains is guaranteed through centralized distribution of configuration updating files and a consistency scoring mechanism, so that the control effect and safety under automatic driving are improved. According to the method, the control deviation caused by desynchrony under the traditional architecture is avoided, the configuration requirements of each domain are automatically detected based on the CCP, parameter comparison and redundancy check are carried out through an automatic A2L file matching mechanism, the configuration file is quickly identified and updated, and the calculation burden of the system is reduced through redundancy elimination.

The invention provides a second-hand vehicle residual value dynamic evaluation method and system and electronic equipment, and relates to the technical field of vehicle evaluation and analysis. Comprising the following steps: respectively inputting features corresponding to a vehicle individual data chain, features corresponding to external environment data and adjusted weights into a random forest model, a gradient lifting decision model and a seasonal autoregression comprehensive moving average exogenous regression model in time sequence analysis; obtaining a first second-hand vehicle residual value evaluation result, a second second-hand vehicle residual value evaluation result and a third second-hand vehicle residual value evaluation result; performing weighted fusion on the first second-hand vehicle residual value evaluation result, the second second-hand vehicle residual value evaluation result and the third second-hand vehicle residual value evaluation result to obtain a second-hand vehicle comprehensive residual value dynamic evaluation result; and outputting a second-hand vehicle residual value dynamic evaluation report based on the second-hand vehicle comprehensive residual value dynamic evaluation result, and pushing the second-hand vehicle residual value dynamic evaluation report to the user side. According to the method, factors such as external environment fluctuation are updated in real time, so that the hysteresis of a traditional valuation system is avoided, and the timeliness of valuation is ensured.

The invention relates to the technical field of building geological data management and decision making, and discloses a virtual reality-based building geological data management and decision making system, which comprises the following steps of: establishing a two-dimensional coordinate system on the ground by taking the position of a tower body of a tower crane on the ground as an original point; executing a dangerous area dynamic calculation strategy, and calculating the range of the dangerous area in real time; executing a constructor position prediction strategy, and predicting the position of each constructor at a future moment; according to the predicted position of each constructor at the future moment, a constructor danger judgment strategy is executed, if it is predicted that the constructor enters the dangerous area at the moment, a personnel moving direction planning strategy is executed, a new moving direction is planned for the constructor entering the dangerous area, and a cargo moving path planning strategy is executed; and the position of the goods at the moment is planned, and the decision process of tower crane goods transportation is optimized, so that the construction site safety and the working efficiency are improved.

The invention relates to the technical field of organic fertilizer equipment, in particular to a raw material mixing device for organic fertilizer fermentation, which comprises a fixed shell and a stirring assembly, the stirring assembly comprises two groups of stirring pieces, each group of stirring pieces at least comprises a rotating arm and two parallel stirring rods, the stirring rods are in running fit with the rotating arm through connecting pieces, and the rotating arm is fixedly connected with the fixed shell. The rotating arm is in running fit with the fixed shell through a rotating shaft; the rotating shaft of at least one group of stirring pieces is connected with a driving device, a cleaning piece is arranged between the stirring rods of the two groups of stirring pieces in a sliding fit manner, and driving energy transmitted by the driving device through the rotating shaft is partially transmitted to the cleaning piece through the stirring rods, so that the cleaning piece can automatically reciprocate on the stirring rods during mixing and stirring; accumulated objects on the stirring rod are removed, automatic cleaning is achieved, the requirement for manual intervention is reduced, hysteresis of a traditional cleaning mode is avoided, and long-term stability of equipment and production is guaranteed.

The invention provides a deformation prediction method and device for a supporting structure of a foundation pit around a dense building, and can be applied to the technical field of engineering construction and artificial intelligence. The prediction method comprises the steps that excavation information of a foundation pit and soil body information, supporting structure information and hydrological information corresponding to the foundation pit are obtained; the excavation information, the soil body information, the vertical supporting information, the horizontal supporting information, the inertia information and the hydrological information are input into a deformation prediction model, an initial prediction result is output, the initial prediction result comprises multiple pieces of horizontal deformation information, a deformation peak value and a deformation type, and the horizontal deformation information represents horizontal deformation results of multiple point positions in the supporting structure; and determining a target prediction result corresponding to the foundation pit based on the multiple pieces of horizontal deformation information, the deformation peak value and the deformation type. By means of the method, the deformation condition of the supporting structure under the complex load condition can be accurately predicted, the precision of the target prediction result is higher, and the safety, stability and economical efficiency of foundation pit engineering are improved.

The invention relates to an intelligent explosion-proof junction box for an offshore oil platform, which comprises a shell, an environment monitoring module, a central processing unit, an intelligent fault diagnosis module, a remote monitoring module and a management module, and is characterized in that the shell is made of a multi-layer composite explosion-proof material and has excellent corrosion resistance, heat insulation and explosion-proof performance; the environment monitoring module is used for monitoring temperature, humidity, gas concentration and vibration in real time by integrating a plurality of sensors, data are transmitted to the central processing unit for analysis, and the intelligent fault diagnosis module is used for performing real-time fault analysis and prediction on the data based on a logical algorithm, generating a fault report and performing early warning. The remote monitoring module realizes real-time data transmission and remote control through the Internet of Things, the management module is combined with the block chain technology to ensure the safety of a remote command, the junction box realizes real-time monitoring, fault diagnosis and remote control of equipment through an intelligent system, and the safety and management efficiency of the equipment on an offshore oil platform are improved.

The invention relates to the technical field of steel cofferdam settlement monitoring, and discloses a steel cofferdam settlement monitoring system and method.The method comprises the steps that after foundation soil body characteristics and soil layer distribution of a steel cofferdam burying area are obtained, the foundation soil body characteristics and the soil layer distribution are substituted into a pre-established foundation model to obtain preset bearing capacity; and then, the bearing capacity of the foundation is monitored in real time, and whether settlement monitoring is conducted or not is determined according to the relation between the real-time bearing capacity and the preset bearing capacity. And when the real-time bearing capacity is larger than or equal to the preset bearing capacity, settlement monitoring is started. In the monitoring process, the real-time settling velocity, the soil water content, the pore water pressure and the environment temperature are obtained, and the settlement score of the steel cofferdam is comprehensively evaluated. And determining an early warning grade according to the settlement score, and setting the sending frequency of the alarm information according to the early warning grade. According to the method, the settlement state of the steel cofferdam and the influence factors of the settlement state are monitored and evaluated in real time, and timely early warning of potential risks is achieved, so that construction safety and structural stability are effectively guaranteed.

The invention relates to the technical field of vehicles, in particular to a vehicle-mounted music playing control method and device, a vehicle, a medium and a program, and the method comprises the steps: monitoring a Bluetooth playing state of a client; if the Bluetooth playing state is a playing state, reading a Bluetooth setting mode configuration item; and controlling the vehicle machine system to execute the target operation according to the Bluetooth setting mode configuration item. Therefore, the problems that the Bluetooth music playing mode is single, audio file analysis is unreliable, hysteresis exists, and the experience feeling is poor due to the fact that autonomous control cannot be conducted according to user requirements in the prior art are solved.

The invention provides a real-time disaster situation multi-dimensional verification alarm method and system based on a large model, and relates to the technical field of disaster situation response, and the method comprises the steps: carrying out the backtracking and calling of multi-source disaster situation associated data after a disaster situation early warning signal is received, and obtaining a heterogeneous disaster situation data set; starting a dynamic task chain scheduling mechanism, executing multi-priority thread trend analysis, and outputting a multi-dimensional index analysis result; executing multi-dimensional index cross verification, and outputting a disaster verification conclusion; disaster decision making is carried out, and a disaster decision instruction is generated; carrying out township-level disaster situation grid positioning, and outputting a disaster boundary coordinate set; after the thermal rescue target spot is framed, geological disaster avoidance analysis is carried out, and a safety buffer area is positioned; and executing an alarm verification response. The technical problems that in the prior art, a disaster monitoring and early warning system mainly depends on a step-by-step reporting mechanism, so that disaster information transmission is lagged, accuracy is lost, and timeliness and accuracy of post-disaster rescue work are affected are solved.