1102results about How to "Improve computing efficiency" patented technology

The invention provides a method for quickly establishing a three-dimensional near-seafloor speed model in a shallow sea area; the method comprises the following steps of extracting initial informationand extracting a common-center-point gather; calculating the slope of the initial travel time in the common-center-point gather; judging whether a shot point is located in the seawater layer or not,and if yes, correcting the shot point to the seafloor; calculating the speed and the depth of inflection points corresponding to respective offset distances; performing linear interpolation on the speed at each depth to obtain a one-dimensional speed model of the common center point; after all the common-center point domain gathers are processed, adopting the linear interpolation method, calculating the speed values of the rest grid points by utilizing the one-dimensional speed model of all common center points, and a speed model is output. By adoption of the method for quickly establishing the three-dimensional near-seafloor speed model in the shallow sea area, the influence of the seawater layer on ray propagation is overcome, and the influence of multiple excitation receiving modes on an inversion result is eliminated, and the problem that the conventional method needs to carry out multi-ray tracing forward modeling is avoided.

The invention provides an offshore wind plant current collection network topology optimization method based on an improved grey wolf optimization algorithm, and the method comprises the steps: S1, obtaining basic data of an offshore wind plant, initializing the grey wolf algorithm, and generating an initial solution set; s2, adopting polar coordinates and a minimum spanning tree to optimize and expand the initial solution set; s3, optimizing a connection mode in each group; s4, executing the optimal connection of each group of fan combination; s5, performing optimal solution set exploration on the grey wolf algorithm by adopting an operator; s6, repeatedly executing until a preset maximum number of iterations is reached, and generating a topology model of the current collection system of the offshore wind plant; the method can improve the initial solution quality and the convergence efficiency, gives consideration to the global search capability and the local optimization precision, and achieves the quick and effective processing of geometric constraints.

The invention provides a lightweight large model operation method based on end-side deployment. The lightweight large model operation method comprises the following steps: acquiring equipment operation data and storing a model file in a mixed precision format according to quantization precision supported by terminal equipment; determining a mixing precision quantification strategy and carrying out strategy analysis on the equipment operation data; a Key-Value cache file obtained through model reasoning in the historical dialogue is reserved; calculating a semantic embedding vector of the second user request, calculating a cosine similarity between the semantic embedding vector and the first V vector, and judging whether the second user request has a similar intention or not according to a calculation result; and if yes, performing incremental reasoning on the second user request by multiplexing the Key-Value cache file. By monitoring the state of the terminal equipment in real time, dynamically adjusting the quantization level, multiplexing the cache data of the semantic related requests and merging batch request reasoning, the effects of balancing the energy consumption and performance of the terminal equipment, reducing repeated calculation and improving the service time of the equipment and the user experience are achieved.

The invention provides a hydrological flow long sequence prediction method and system of an improved state space model. The method comprises the steps of collecting multi-source data of a drainage basin to be predicted; constructing a time sequence sample pair by the processed multi-source data through a sliding window method, wherein the time sequence sample pair comprises an input sequence and a target sequence; a HydroMama model is constructed according to the time sequence sample pair, a HydroMama prediction model is trained, and an optimal hyper-parameter combination is searched for; based on the trained HydroMama model, traffic prediction and result restoration are realized by adopting an autoregression mechanism; the calculation efficiency is high, and the continuous state equation discretization of the state space model is utilized, so that the long-distance meteorological-hydrological hysteresis effect which is difficult to capture by a traditional cycle model can be captured; according to the method, skewed distribution of hydrological data is processed through logarithmic transformation, and an anti-overfitting objective function is combined, so that the prediction performance of the model on unseen data is remarkably improved, and the practical application value is high.

The embodiment of the invention discloses a harmonic compensation method for an angle error of a magnetic encoder, and the method comprises the steps: enabling the magnetic encoder to rotate at a constant speed in a setting mode, collecting sine and cosine signals outputted by the magnetic encoder, and carrying out the calculation through CORDIC, thereby obtaining an initial angle sequence containing a fundamental wave error and a harmonic wave error; extracting each harmonic component through an adaptive wave trap, constructing a harmonic compensation polynomial, and storing each coefficient; in an application mode, a magnetic encoder signal is collected in real time, a current initial angle value is solved, a harmonic compensation amount is calculated according to a current fundamental wave angular frequency and a stored coefficient, and an accurate angle value is obtained by subtracting the compensation amount from the initial angle value. According to the method, external reference signals and a large amount of storage space are not needed, each harmonic error is directly eliminated through a harmonic compensation polynomial, and approximate errors caused by traditional linear interpolation are avoided; and the compensation precision can be improved only by increasing polynomial terms, and the storage requirement is not obviously increased.

The invention discloses a load self-adaption device based on online system identification. The device comprises a track-Jacobian matrix offline generation module used for obtaining a pseudo-inverse matrix of a track-Jacobian matrix offline; the online excitation and measurement module is used for online controlling the robot to execute excitation motion and measuring to obtain an actual joint response error vector; and the rapid parameter identification module is used for combining the actual joint response error vector with the pseudo-inverse matrix to obtain an inertial parameter of a target load. The track-Jacobian matrix represents a linear sensitivity relation between inertia parameter disturbance of a load at the tail end of the robot and a joint response error. According to the method, the dynamic solving process with high calculation complexity is transferred to the offline stage to be completed, and the deterministic matrix-vector operation only needs to be executed once in the online stage, so that the calculation delay of online identification is greatly reduced on the premise of ensuring the identification precision.

The invention discloses a thermodynamic system analysis method and system based on a physical information neural network. The method comprises the following steps: constructing a thermodynamic system mathematical model; converting a solving problem of the mathematical model of the thermodynamic system into an optimization problem taking a minimum control equation set residual error as a target function; constructing a physical information neural network; constructing a composite loss function based on the control equation set residual sum of squares, and performing end-to-end training on the physical information neural network to obtain a trained physical information neural network; taking the trained physical information neural network as an approximate solver, and for any given thermodynamic system input working condition, predicting a group of approximate solutions as initial predicted values through one-time forward propagation; and inputting the initial predicted value into a traditional numerical solver for accurate solving to obtain a to-be-solved state variable. According to the method, a feasible technical path is provided for applications with strict requirements on the analysis and calculation speed of the thermodynamic system, such as real-time simulation, online optimization and digital twinning.

The invention provides a resource scheduling method, electronic equipment, a storage medium and a computer program product. The method comprises: obtaining global information of a target computing power network, the target computing power network comprising a plurality of nodes, the plurality of nodes comprising a plurality of user nodes, a plurality of network nodes and a plurality of computing nodes, the global information at least comprising state information of the plurality of nodes and state information of a plurality of transmission paths, each transmission path comprises a user node and a computing node; constructing a resource scheduling model of the target computing power network based on the global information of the target computing power network; converting the resource scheduling model into a quantum computing model; constructing a quantum circuit based on the quantum calculation model; and determining a resource scheduling result of the target computing power network according to the measurement result of the quantum circuit. Through the scheme provided by the invention, the global resource scheduling strategy can be quickly obtained.

The invention discloses a large model reasoning hardware accelerator based on data flow execution, and belongs to the technical field of large model reasoning hardware acceleration. Each node comprises a linear calculation kernel used for sequentially completing query / key / value projection and attention output projection in multi-head attention calculation, dimension raising projection and dimension reduction projection calculation in a feedforward neural network layer, and inverse quantization operation after each projection is output; the multi-head attention calculation kernel is used for completing loading and quantization of query / key / value vectors, multi-head division and cache management, attention score calculation, Softmax normalization, context vector generation and KV cache updating; the RLA kernel is used for completing residual addition, layer normalization, quantization / inverse quantization and nonlinear activation calculation of each layer; the three cores in the same node are located in the same SLR area, and data transmission is carried out through a hardware queue and an HLS blocking mechanism. According to the invention, high hardware utilization rate and low-delay reasoning can be realized.

The invention discloses an image fusion method based on a double-branch feature decoupling auto-encoder, and belongs to the field of computer vision and image processing. According to the method, for the problems of modal pollution and structural distortion in infrared and visible light image fusion, structural semantic information and high-frequency texture details of a source image are extracted respectively by constructing a content feature coding module and a detail feature coding module, and feature decoupling is achieved. And performing deep fusion on the decoupled features by using an adaptive feature weighting mechanism, and reconstructing a fused image with infrared target saliency and visible light detail definition through a shared decoder. According to the method, an end-to-end two-stage training strategy is adopted for optimization, complex prior or post-processing is not needed, the effects of improving the fused image contrast, edge preservation and target recognition performance are remarkable, and the method is suitable for the fields of weak light monitoring, intelligent perception, unmanned system navigation and the like.

The invention provides a dam deformation prediction method and system. The method belongs to the field of water conservancy dam deformation monitoring, and comprises the following steps: carrying out clustering analysis on a plurality of measuring points of a dam by adopting a K-means algorithm, and merging the measuring points with similar characteristics into the same group to obtain a plurality of measuring point groups; wherein in the clustering analysis process, a correlation change method is selected to determine the optimal cluster number; inputting the clustered measuring point groups into a pre-established HST-M model for spatial feature fusion to obtain a high-dimensional feature matrix; carrying out feature dimension reduction on the high-dimensional feature matrix by adopting an auto-encoder; and inputting the dimensionality-reduced low-dimensional features into a ridge regression model for prediction so as to obtain a deformation prediction result of the dam measurement point. The system comprises a clustering module, a fusion module, a dimension reduction module and a prediction module. The method and the system can improve the prediction stability of dam deformation.

The invention relates to the technical field of low-altitude aircraft operation safety, in particular to a low-altitude aircraft-oriented conflict-free flight path planning method before takeoff, which comprises the following steps of: calculating a basic protection interval and an aerodynamic disturbance range of a low-altitude aircraft and determining a three-dimensional dynamic safety boundary; discretizing the airspace into a regular three-dimensional grid array; constructing preset space-time paths before takeoff of the low-altitude aircrafts, mapping the preset space-time paths to the three-dimensional grid array, and identifying all the preset space-time paths before takeoff with space-time overlapping by analyzing the expected space-time occupation section; constructing a space-time path optimization algorithm, and setting priority decision rules of various types of low-altitude aircrafts; generating a space-time path adjustment instruction with the minimum comprehensive adjustment cost; and integrating all space-time path adjustment instructions to generate a conflict-free track before takeoff. According to the method, the conflict in the preset flight path before the low-altitude aircraft takes off is detected and released, and the method can be used for formulating the conflict-free preset flight path and flight path of the low-altitude aircraft before the low-altitude aircraft takes off.

The invention discloses an unmanned aerial vehicle patrol and security monitoring linkage management method. According to the system, urban geographic information data and key monitoring area coordinates are acquired, hierarchical grid division is realized by using a quadtree spatial index algorithm, and a patrol task distribution basic data structure is established; solving an approximate optimal solution of the patrol path of the unmanned aerial vehicle by adopting an improved greedy strategy, and performing optimization adjustment according to technical parameters and weather conditions of the unmanned aerial vehicle; the unmanned aerial vehicle collects video data in real time through high-definition camera equipment in the patrol process, target detection is conducted through a deep learning model, and potential safety hazards such as personnel gathering and vehicle abnormity are recognized; detecting violent conflicts and illegal intrusion behaviors in combination with a time sequence analysis algorithm, and generating an abnormal event report; cooperative work of the unmanned aerial vehicle, the ground robot and the command center is realized; and a patrol scheme is dynamically adjusted according to meteorological conditions, and robot scheduling is optimized through energy consumption management.

The application relates to a heterogeneous feature clustering method based on a privacy image, characterized in that a diffusion generation process is converted from a pixel domain to a feature domain through a featureized noise and matrix matching technology, a to-be-trained diffusion generator model with a conditional denoising UNet architecture as a core is constructed, and training is conducted in combination with a sample-level consistency constraint and a distribution-level alignment constraint, then a feature pair with mutually aligned semantics is constructed by using a pseudo image generated by the trained diffusion generator model; subsequently, a variational autoencoder model is trained, cross-domain feature mapping is realized through a double-layer alignment loss, and finally a unified domain feature dataset is formed and clustering is conducted; the method has the advantages that cross-domain feature unification is completed under the premise that no source domain data and no paired samples are available, original image leakage is effectively avoided, a high-quality unified feature set can be directly output, and a safe and efficient solution is provided for cross-domain visual analysis under a privacy limited scene.

The invention discloses a rocket fault reconstruction method for ensuring the safe height of an orbit under random factor disturbance. The method comprises the following steps: S1, establishing a deterministic trajectory optimization model of a carrier rocket; s2, on the basis of the deterministic trajectory optimization model, introducing random disturbance representing initial state deviation and engine thrust deviation, and establishing a random trajectory optimization model of the carrier rocket; s3, performing convex processing on the random trajectory optimization model, and converting an original problem including a nonlinear term and a non-convex constraint into a convex optimization problem; and S4, solving the convex optimization problem after the convex processing by adopting a sequence convex algorithm, and obtaining a rocket fault reconstruction trajectory capable of ensuring the safe height of the orbit under the disturbance of random factors. According to the method, efficient online solving of a strong nonlinear non-convex random trajectory optimization problem can be realized, so that a fault reconstruction trajectory which can strictly guarantee the safety height of an injection orbit in the probabilistic sense is generated.

This invention discloses a system for identifying and simulating key nodes in traffic networks based on deep reinforcement learning. Addressing the high computational complexity of key node search and the lack of dynamic verification in traffic networks, this invention employs the following system: a road network topology mapping module parses GIS map data and removes redundant information to generate a weighted topology map; a deep reinforcement learning identification module, based on a deep Q-network, maps node features to graph embedding information through graph representation learning, with the optimization objective of minimizing cumulative normalized connectivity, outputting the optimal key node sequence; and a traffic effect simulation verification module uses a traffic simulation platform to establish a realistic road network model, quantitatively analyzing the impact of key node failures on average travel time and waiting time. This invention features low time complexity, the ability to generalize from small synthetic networks to extremely large-scale real networks, and simulations have verified its high application value in actual traffic management and disaster prevention.

This application discloses a method, apparatus, equipment, and storage medium for calculating the moisture content of electrical insulation paperboard, relating to the field of transformer technology. The method for calculating the moisture content of electrical insulation paperboard includes: collecting data on the exposure time and environment of the target transformer body; calculating the moisture absorption rate of the electrical insulation paperboard of the target transformer body based on the exposure time and environmental data combined with a moisture absorption rate calculation model; calculating the stage saturated moisture content of the electrical insulation paperboard of the target transformer body based on the exposure time and environmental data combined with a stage saturated moisture content calculation model; and calculating the moisture content of the electrical insulation paperboard based on the exposure time, moisture absorption rate, and stage saturated moisture content. This method achieves accurate calculation of the moisture content of electrical insulation paperboard based on environmental information and a constructed calculation model without damaging the paperboard, improving the efficiency of moisture content calculation while ensuring real-time performance.

The application discloses a kind of collaborative robot job parameter dynamic optimization method, comprising: real-time acquisition of the multimodal sensor data of collaborative robot in the process of job, multiple job parameters are analyzed based on job quality evaluation model to identify key parameter set from it;Multi-objective function is constructed with job time, end force as optimization target, and multi-objective function is converted into single-target optimization function by weighting sum method;Key parameter set is input to machine learning model, and single-target optimization function is used as optimization target to carry out iterative optimization, in each iteration, the standard deviation σ of the current learning result of machine learning model is obtained, according to the comparison result of standard deviation σ and preset critical value s, dynamically select optimization strategy, when σ≥s, new parameter set is generated using random exploration strategy, when σ<s, new parameter set is generated using guided optimization strategy of machine learning model, and the optimized parameter set is output.

The invention aims to provide a gas turbine blade unstable flow visualization testing method, and belongs to the technical field of gas turbine experimental testing. Comprising the following steps: inputting boundary conditions and geometric conditions of a calculation model; numerical simulation flow field data under the condition are obtained through CFD calculation; if yes, the next step is carried out, and if not, a higher-quality grid is generated and returned to the encrypted grid, and CFD numerical simulation calculation is carried out again; selecting a proper resolution ratio based on an actual demand; generating a data source quantity and obtaining CFD calculation data according to the data source quantity; and outputting the three-dimensional transient visual flow field at the moment obtained by the checked high-precision CFD numerical simulation. The actual working condition of the gas turbine blade can be simulated, the test error of an actual instrument under the severe working condition is avoided, and the test difficulty is reduced. The precision of CFD numerical simulation is optimized, and the error is reduced, so that the credibility of the three-dimensional visualization cloud picture is improved.

The present application relates to the technical field of short-circuit current calculation, and particularly relates to a flexible DC system short-circuit current calculation method based on a physical information neural network, comprising: setting a model input feature vector, the model input feature vector being used to represent a system operating state before a fault and fault information, and performing data preprocessing on the model input feature vector; constructing a hybrid driving calculation model, the hybrid driving calculation model comprising a physical calculation module and a neural network module, and being coupled based on a preset fusion architecture; performing end-to-end training and optimization on the hybrid driving calculation model by using a preset composite loss function; and performing flexible DC system short-circuit current calculation based on the optimized hybrid driving calculation model, so that the problems of poor precision, speed and convergence, poor interpretability and weak generalization ability in the prior art are solved.

This invention belongs to the field of metal processing, specifically a numerical simulation method for laser shock strengthening of complex structural parts with adaptive positioning, including the following steps: (1) establishing a structural model of the part; (2) meshing the structural model of the part; (3) customizing the parameters of the VDLOAD subroutine to generate an array of laser loading positions and impact pressures; (4) establishing dynamic explicit analysis steps and boundary conditions, and using the VDLOAD subroutine to calculate the residual compressive stress field formed on the surface of the part after laser shock. This method is based on the vertical mapping between the real-time normal vector of the free surface and the projection of the transient pressure in the VDLOAD subroutine to construct a surface adaptive judgment algorithm, ensuring that the transient pressure is always accurately aligned with the normal of the laser-shocked surface, while using a fully parameterized spatiotemporal array to generate laser loading positions and impact pressures in batches, realizing the simulation of the continuous laser shock strengthening process on the surface of any complex structural part.

A kind of detection method and device of fake APP, terminal, the method includes: determining training data;Keyword extraction is carried out to the brief introduction of each pair of target APP and the brief introduction of suspected APP, and the data vector of each keyword is determined;Determine the weight value of each keyword, and the data vector of each keyword in the target APP is weighted based on the weight value Operation is carried out to obtain target APP data vector, and obtain suspected APP data vector;The vector comparison result of target APP data vector and suspected APP data vector is calculated, the absolute value of the vector comparison result is determined as first data vector, and the square of the vector comparison result is determined as second data vector;Obtain the training text vector of the pair of target APP and suspected APP;Training fake detection model.The present application can enhance the association between keywords in model training phase, reduce the misjudgment rate.

The application provides a Gaussian ball clustering method for three-dimensional reconstruction of a highway traffic accident site, comprising the following steps: S1, collecting multi-view images of the accident site, solving camera parameters and initializing a three-dimensional Gaussian ball scene; S2, constructing a rendering training loop based on the initialized three-dimensional Gaussian ball scene, and adaptively adjusting the Gaussian ball density through cloning or splitting operations; S3, extracting the spatial and color features of the three-dimensional Gaussian ball, and constructing a multi-modal physical feature set; S4, calculating an adaptive discrete step length based on the multi-modal physical feature set, and mapping the continuous features into discrete coordinates; S5, generating a multi-modal hash index based on the discrete coordinates, and realizing continuous rearrangement of the same type of Gaussian ball in the display memory through GPU parallel sorting; S6, performing physical conservation formula parameter aggregation on the same type of Gaussian ball continuously stored in the display memory, releasing redundant display memory and simplifying the three-dimensional Gaussian ball set; and S7, completing all training iterations, and exporting the final simplified three-dimensional Gaussian ball set into a general PLY format file.

The application provides a kind of to identify batch electronic product average life evaluation method, it is related to the technical field of reliability engineering and life evaluation, solve the technical problem that average life evaluation method in prior art is difficult to accurately evaluate the average life of the batch electronic product.The method comprises establishing a segmented life distribution model, the segmented life distribution model includes a first stage life distribution model and a second stage life distribution model separated by a segmentation point time T, and the segmented life distribution model satisfies the parameter constraint relationship at the segmentation point time T;Wherein, the first stage life distribution model adopts an exponential distribution model with failure rate λ;The second stage life distribution model adopts a Weibull distribution model;Based on the segmented life distribution model, the average life of the batch electronic product is calculated and obtained by applying the average life analytical expression.The application can improve the calculation efficiency and calculation accuracy.

The invention provides a parameterization calculation method for a top cover modal and a pitch diameter of a water turbine, and belongs to the technical field of hydraulic mechanical structure dynamics and simulation automation. The method comprises the steps of parameter input, parametric modeling, automatic simulation control, pitch diameter identification and analysis and result output: after a parameter set is input, constructing a parametric modeling module based on Python and ANSYS Spaceclaim, and automatically generating a three-dimensional geometric model containing a basic panel, a rib plate and a connecting hole; an automatic simulation control module is constructed based on Python and ANSYS Workbench, and full-process automatic simulation is completed to obtain a modal analysis result; pitch diameter characteristic parameters are automatically recognized through a least square discrete Fourier transform algorithm of non-equiangular sampling and confidence degree judgment; and outputting results to form a standardized database. According to the method, automation and quantification of the whole process are achieved, modeling and simulation efficiency is improved, manual misjudgment is avoided, the method is suitable for different types of water turbine top covers, and reliable data support is provided for structural optimization and resonance risk assessment.

The invention discloses a vehicle time history response calculation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a mass matrix, a damping matrix and a stiffness matrix of a to-be-tested vehicle; inputting the mass matrix, the damping matrix and the stiffness matrix into a pre-constructed time history response calculation model; according to the mass matrix, the damping matrix and the stiffness matrix, the time history response of the to-be-tested vehicle is calculated through a time history response calculation model; wherein the time history response comprises a displacement vector, a speed vector and an acceleration vector. According to the embodiment of the invention, the calculation efficiency can be remarkably improved while the calculation precision is ensured, and the method is suitable for time-history response analysis of the key structure of the commercial vehicle under the complex dynamic load.

The invention discloses a cleaning method and device, a cleaning robot and a readable storage medium, and the method comprises the steps: obtaining a single-frame image, carrying out the recognition of the single-frame image based on a preset deep learning algorithm, and obtaining an initial behavior recognition result; based on a multi-frame image fusion judgment strategy, performing fusion judgment on the initial behavior recognition result corresponding to each single-frame image to obtain a first behavior recognition result; if the first behavior recognition result is abnormal recognition, accumulating each single-frame image, performing time sequence behavior recognition on multiple frames of images obtained by accumulation according to a time sequence behavior recognition algorithm, and determining a second behavior recognition result; determining a target behavior recognition result based on the first behavior recognition result and the second behavior recognition result; the target behavior recognition result is used for determining a target cleaning strategy so as to perform cleaning processing. By adopting the method, the cleaning efficiency is improved.

This invention discloses a method, apparatus, medium, and product for calculating bonding interactions in molecules. The method includes: allocating bonding tasks using a three-dimensional interconnected computing architecture, and constructing a bonding atom set and a bonding task index table matching the allocated bonding tasks within each computing module. This ensures that identical atoms do not need to be repeatedly stored when the computing module executes all bonding tasks, reducing the storage overhead of atomic information; dynamically acquiring and filling atomic information from the local machine or other computing modules into the corresponding storage locations of the bonding atom set, extracting atomic information according to the index table, calculating bonding components in parallel, and accumulating the bonding components of identical atoms to obtain the resultant bonding force; and updating the resultant bonding force and atomic information through inter-module interaction, and re-storing the atomic information according to the updated spatial arrangement. This reduces the amount of data transmission between modules while ensuring the accuracy of molecular bonding interaction simulation, thereby improving the stability and efficiency of the overall molecular dynamics simulation.

The application belongs to the technical field of simulation analysis, and particularly relates to a switch cabinet condensation margin evaluation method based on simulation analysis and order reduction reconstruction, steps of which comprise: establishing a simulation analysis model of the switch cabinet, adding a physical field and performing physical field coupling and mesh partitioning; solving the simulation analysis model to obtain the temperature and humidity distribution inside the switch cabinet; optimizing the simulation analysis model; replacing electromagnetic heat with loss data as a heat source input into a non-isothermal flow field; constructing a physical field snapshot library for order reduction model training; processing the physical field snapshot library based on a POD method to construct an order reduction model; training a Kriging model to reconstruct the temperature and humidity field of the switch cabinet; constructing a condensation margin evaluation cloud map; in the actual operation process of the switch cabinet, calculating an actual dynamic dew point temperature to generate an actual operation condensation margin cloud map, and completing online real-time evaluation. The application can realize efficient and accurate reconstruction of the temperature and humidity field inside the switch cabinet and efficient evaluation of the condensation margin of the switch cabinet.