304results about How to "Easy to plan" patented technology

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention relates to the technical field of photovoltaic power generation power prediction, in particular to a photovoltaic power generation power high-precision prediction method and system. According to the technical scheme, the method comprises the following steps: data acquisition and preprocessing: historical photovoltaic power generation power data are widely collected, the time span covers at least one year in the past so as to obtain power generation power change information under different seasons, weather conditions and time periods, and meanwhile, meteorological data corresponding to the power generation power change information are collected; and cleaning the collected data, and removing missing values through data integrity check. The power generation efficiency and economy are improved, the operation and maintenance plan can be optimized, and the energy storage utilization efficiency is improved; the power grid stability and reliability are enhanced, scheduling is assisted, resource configuration is optimized, power market operation is greatly improved, support is provided for transaction decision making, renewable energy consumption is promoted, the system adaptability and the intelligent level are improved, real-time tracking and updating can be achieved, and intelligent management of a power station is assisted.

The invention discloses an intelligent parking space state recognition system and method based on video detection, and belongs to the technical field of parking space state recognition, and the method specifically comprises the steps: collecting the video data of a parking area, carrying out the preprocessing of the collected video data of the parking area, carrying out the modeling of a parking lot background based on a time sequence, and carrying out the real-time updating, based on a background modeling result, a foreground is detected, a foreground area is extracted, contour analysis is carried out on the detected foreground area, the occupation state of each parking space is judged, a real-time parking space distribution diagram of the parking lot is generated according to the occupation states of all the parking spaces, and the occupation or idle state of each parking space is displayed. The intelligent parking space state recognition system based on video detection can keep high accuracy in actual scenes such as complex illumination conditions, complex environments, different vehicle types and angles and the like, helps a driver to quickly find an idle parking space, and reduces traffic jam caused by parking space finding.

The invention discloses a laser cutting parameter adjusting system based on machine learning, and relates to the technical field of laser cutting. According to the method, multiple laser cutting parameters in the laser cutting process are collected, a short-term state index and a long-term state index are calculated based on the cutting parameters, and a laser cutting instant index and a laser cutting long-term index are generated respectively; based on the laser cutting instant index and the laser cutting long-term index, optimized laser cutting parameters are generated, and working parameters of the laser cutting machine are adjusted in real time; the adjustment result and the related data are sent back to the data storage module for updating the historical database; a real-time distribution curve is generated and compared with a target distribution curve, deviation is calculated, and a parameter optimization target curve is generated; and according to the optimized sub-targets, laser cutting parameters are adjusted in real time, and the stability and high efficiency of the laser cutting process are ensured. According to the system, accurate control over the laser cutting path is achieved through multi-layer and non-uniform control unit division and time-space correlation model construction.

The invention relates to the technical field of assembly line scheduling, in particular to an intelligent scheduling method and system for an automatic assembly line of a charging pile. The method comprises the following steps: acquiring a charging pile assembly production log; performing sliding window segmentation processing on the charging pile assembly production log, and performing staged assembly demand feature analysis to obtain assembly demand feature data of different stages; the real-time production progress of each assembly line is extracted based on the charging pile assembly production log; demand supply deviation prediction is carried out on the real-time production progress of each assembly line according to the assembly demand characteristic data of different stages, and assembly lines lack of productivity are marked; an all-dimensional real-time monitoring image of the assembly line is collected; and carrying out dynamic behavior analysis on the omnibearing real-time monitoring image of the assembly line, carrying out process operation logic analysis, and generating multi-stage process operation logic. According to the invention, high-efficiency and self-adaptive intelligent scheduling of the assembly line is realized.

The invention discloses a water plant ultra-short-term water supply prediction method and system based on time multi-scale characteristics, and relates to the technical field of water resource management and prediction. The method aims at solving the problems that historical water supply data is not fully researched and multiple scales of time are not considered in existing water supply prediction. The method comprises the steps of firstly collecting historical water supply data and related environmental factors of a water plant, preprocessing the data and dividing the data into a training set, a verification set and a test set; selecting historical water supply characteristic factors and related environment characteristic factors from a time multi-scale angle to form a characteristic set; verifying the correlation and potential relationship between the feature set and the water supply amount through correlation analysis, mutual information analysis and a feature histogram method; and then seven deep learning models are constructed based on a mean square error loss function to carry out water supply prediction research, and a Bayesian optimization technology is used to carry out hyper-parameter tuning.

The invention provides an RIES multi-objective optimization planning method considering the uncertainty of source load prediction, and the method comprises the steps: building an RIES basic structure, generating an uncertainty scene set of a source load prediction error through a Latin hypercube sampling method, carrying out the reduction of the uncertainty scene set through a clustering technology, and carrying out the reduction of the uncertainty scene set. According to the method, total cost, carbon dioxide emission and system reliability are taken as objective functions, an RIES multi-objective optimization planning model considering source load prediction uncertainty is constructed, a multi-objective optimization function is solved by adopting a non-dominated sorting genetic algorithm NSGA-III, a Pareto leading set is obtained, and through a strategy of combining a fuzzy entropy theory and TOPSIS, a Pareto leading set is obtained. And selecting the optimal solution of the Pareto leading edge set to obtain a multi-target optimization planning result of each typical scene. The method can comprehensively consider the influence of uncertain source load prediction on planning, improves the reliability of the system and the utilization rate of renewable energy sources, reduces the investment and operation and maintenance cost of the system, and reduces the emission of carbon dioxide at the same time.

The embodiment of the invention relates to a vehicle driving control method and device, equipment and a medium, and the method is applied to a vehicle, and comprises the steps: obtaining the perception data of a vehicle sensor, processing the perception data through a preset visual language model VLM based on a first prompt word of a thinking chain, obtaining an initial scene description text, obtaining the external data of a vehicle road cloud, and obtaining the external data of the vehicle road cloud; processing the external data through a VLM based on a second cue word of a thinking chain to obtain a candidate scene description text, and merging the initial scene description text and the candidate scene description text according to a preset text cue splicing format to obtain an enhanced scene description text, and analyzing the enhanced scene description text through a VLM based on a third prompt word of the thinking chain, obtaining an analysis result, and sending the analysis result to a vehicle controller, so that the vehicle controller performs vehicle driving control based on the analysis result. Therefore, more accurate scene understanding can be provided, driving planning and control are optimized, and safety and efficiency are improved.

The invention relates to a source network load storage collaborative planning method and system considering a multi-agent mixed game, and the method comprises the following steps: building a power distribution network collaborative planning model considering a source network load storage agent master-slave-cooperation mixed game, and enabling the power distribution network collaborative planning model to be a double-layer model, comprising an upper-layer master-slave game sub-model of a power distribution network operator and other subjects and a lower-layer cooperative game sub-model between a source load storage side subject; and solving the power distribution network collaborative planning model by adopting a solving strategy combining a target cascade analysis method and an adaptive coefficient alternating direction multiplier method to obtain an optimal planning scheme of a new line, a distributed power supply and an energy storage device. Compared with the prior art, the method has the advantages that efficient configuration and coordinated utilization of source network load storage resources can be realized, and the planning reliability is improved.

The invention relates to the technical field of computers, and provides a high-speed data transmission method for micro-computing equipment, which comprises the following steps of: acquiring to-be-transmitted data of the micro-computing equipment, identifying the characteristics of the to-be-transmitted data, analyzing a transmission mode, determining a data priority, analyzing hardware configuration information based on the data priority, and detecting the performance of a data transmission interface. The method comprises the following steps: calculating an interface transmission rate and evaluating transmission quality to formulate a data transmission strategy, partitioning data, analyzing a transmission sequence, constructing a transmission time window, monitoring a window state to construct a high-speed transmission path, identifying a transmission node, analyzing a transmission index of the transmission node, and finally generating a data transmission scheme based on the transmission index. According to the invention, the data transmission efficiency of the micro-computing device can be improved.

The invention belongs to the technical field of dynamic traffic, and particularly discloses a multi-source data fusion traffic dynamic induction method, which comprises the following steps: S1, traffic information is acquired from multi-source heterogeneous data, and the data comprises real-time traffic information, navigation map data, a road network topological structure and a public traffic timetable; s2, carrying out data fusion processing on the traffic information obtained in the step S1, and generating fused traffic state estimation; s3, on the basis of travel historical records and preferences of the user, constructing a personalized travel guide model, and providing an optimal travel suggestion; and S4, monitoring the traffic condition in real time, performing dynamic adjustment based on user feedback and emergencies, and optimizing the travel path. By integrating real-time information from various data sources, comprehensive perception of road traffic conditions is ensured, traffic changes in space and time can be processed at the same time, the weight is dynamically adjusted according to the reliability of the data sources, and the influence of data islands and information loss is reduced.

The invention discloses an intelligent automobile path tracking system for urgent danger avoidance, and particularly relates to the technical field of intelligent automobiles, the system comprises an environment sensing module, the output end of the environment sensing module is connected with a danger assessment module, and the output end of the danger assessment module is connected with a path planning module. The output end of the path planning module is connected with a decision planning module, the output end of the decision planning module is connected with a vehicle control module, and the output end of the vehicle control module is connected with a communication and interaction module. Through cooperative work of all the modules, perception of the surrounding environment of the vehicle, danger assessment, path planning, decision making, vehicle control and communication interaction with the outside are achieved, under the dangerous condition, the system can make a response rapidly, effective danger avoiding measures are taken, the driving safety of the vehicle and the accuracy of path tracking are ensured, and the driving safety of the vehicle is improved. And a comprehensive and reliable guarantee is provided for safe driving of the intelligent automobile.

The invention discloses a big data system based on building industry multi-data source project information unification, and relates to the technical field of big data systems. The system comprises a data acquisition layer which is directly connected with an external data source and is responsible for acquiring data and providing original materials for subsequent processing; the data preprocessing and cleaning layer is used for receiving data input of the data acquisition layer and carrying out primary processing on original data; the data storage layer is used for receiving the data of the data preprocessing and cleaning layer, storing the cleaned data, providing a basis for data analysis and supporting the storage requirement of large-scale data; the data processing layer is used for directly reading the data of the data storage layer and deeply analyzing the stored data; the big data system provided by the invention solves the problem of project information management in the building industry through improvements in the aspects of data unified management, data integrated analysis, decision-making efficiency improvement, data security guarantee and the like, and brings better effects and positive influences.

The invention discloses a regional power grid wind power generation power prediction optimization method which comprises the following specific steps: firstly, collecting data such as wind speed and wind direction through meteorological sensors arranged in a wind power plant and around the wind power plant, and carrying out cleaning and de-noising preprocessing; secondly, decomposing the meteorological data into intrinsic mode function components with different frequencies by adopting variational mode decomposition, constructing a convolutional neural network to perform feature extraction on each component, and adaptively adjusting the size of a convolution kernel according to the periodic characteristics of the data; then, a bidirectional long-short-term memory network based on an attention mechanism is used for fusing features, time sequence information is captured, power prediction is achieved through a full-connection neural network, and the weight of the FCNN is initialized through a random forest regression model to improve the initial performance of the model; according to the method, the prediction precision is improved through multi-level characteristic decomposition and a self-adaptive mechanism, the real-time response capability is good, and the requirements of the regional power grid for the precision and timeliness of wind power generation power prediction are met.

The invention provides a method and system for reconstructing a cartilage surface and electronic equipment, and the method comprises the steps: obtaining an outer contour model of a bone cortex and a mark point set of the cartilage surface, and converting the outer contour model of the bone cortex and the mark point set of the cartilage surface into a preset image coordinate system; obtaining a projection point of each mark point in the mark point set on the cartilage surface on the outer contour of the bone cortex, and determining a coverage area of the projection points; performing elastic stretching on the coverage area of the projection point, and further simulating the wear degree of the cartilage surface to obtain a reconstructed cartilage surface; the problem that muscle or ligament abrasion is caused due to the fact that the influence of cartilage is not brought into prosthesis planning in an existing joint replacement operation is solved, practical and effective guidance can be provided during prosthesis planning and adjusting, prosthesis planning and adjusting are better facilitated, and the prosthesis planning and adjusting efficiency is improved. The intelligent degree, usability and reliability of prosthesis planning in joint replacement are improved to a great extent.

The invention discloses a large model training and application method and system based on a city brain, and relates to the technical field of artificial intelligence and smart city management. Comprising the steps of 1, collecting and preprocessing data, 2, training a large-scale language model, 3, utilizing the trained large-scale language model to understand and predict city dynamics, and utilizing the large-scale language model to carry out semantic understanding and relation reasoning on city data, and 4, displaying a model analysis and prediction result by using a visual interface, and performing interactive question and answer by using the visual interface. And 5, continuously learning and updating the large-scale language model according to the feedback: continuously learning and improving the large-scale language model according to the newly acquired city data and the user feedback, regularly evaluating the performance of the model, and adjusting the structure and parameters of the model according to the evaluation result.

The invention discloses an underground water level prediction method based on a KAN network architecture. The underground water level prediction method comprises the following steps: collecting underground water level historical data and underground water level influence factor historical data as original data; using LASSO regression to screen out relatively independent variables having influences on the underground water level for predicting the underground water level; obtaining a training set and a verification set; building an underground water level prediction model based on a KAN network architecture, wherein the underground water level prediction model comprises an LSTM layer, a Dropout layer and an output layer of the KAN network; training and optimizing an underground water level prediction model based on a KAN network structure to obtain an optimal parameter combination; and taking the optimal parameter combination as a model parameter to obtain a current underground water level prediction model based on the KAN network structure, inputting the fitted verification set data into the current underground water level prediction model based on the KAN network structure, and using the verified current underground water level prediction model based on the KAN network structure for underground water level prediction. According to the method, the underground water level is predicted.

The invention discloses an alpine grassland degradation monitoring and predicting method and system, and relates to the technical field of data processing. According to the alpine grassland degradation monitoring and predicting method and system, a grazing monitoring area and a mouse hole monitoring area of the alpine grassland are determined based on analysis of grazing monitoring data and mouse hole monitoring data of each monitoring area of the alpine grassland and historical area ground data of the alpine grassland; monitoring the grazing monitoring area and the mouse hole monitoring area of the alpine grassland to obtain ground data of the selected monitoring area; based on meteorological altitude factors of the alpine region, data acquisition conditions of the plurality of remote sensing devices are analyzed, first syn-position remote sensing representative data of the alpine grassland are selected, and grassland vegetation conditions of the monitoring region are evaluated in combination with ground data of the monitoring region, so that alpine grassland degradation prediction is realized. The grassland degradation condition is comprehensively analyzed and predicted, the accuracy of grassland degradation monitoring in the high-cold region is expected to be improved, and a scientific basis is provided for grassland resource management and protection.

The invention provides a remote sensing image photovoltaic identification method based on an SAM model, and relates to the technical field of computer vision, and the method comprises the steps: collecting and preprocessing a multi-resolution photovoltaic segmentation data set, and enabling the data set to be used for adjusting an image encoder; constructing an SAM segmentation network comprising the image encoder, the prompt encoder and the mask decoder, and inputting interactive click information and the data set into the SAM segmentation network; adding a segmentation output layer into the mask decoder to generate a preliminary segmentation result; and feeding the preliminary segmentation result back to an interaction module for interactive adjustment, and decoding again after adjustment to generate a final segmentation result.

The invention discloses a running vehicle number prediction method, system, device, medium and program based on an SARIMA-RF combined model, and belongs to the technical field of railway freight volume prediction. The method comprises the following steps: acquiring the monthly data of the running condition of a railway international combined transport train to form a train running condition monthly data set; utilizing an SARIMA model to fit a running vehicle number sequence in the monthly data set of the running condition of the class, and obtaining a predicted value sequence and a residual sequence; fitting the residual error sequence by using an RF model to obtain a prediction result; and combining the predicted value sequence of the SARIMA model with the prediction result of the RF model to obtain a final prediction result of the SARIMA-RF combined model. According to the method, the accuracy of the prediction result of the running number of the international railway intermodal trains is improved, and the running uncertainty of railway transportation and the running uncertainty of the international railway intermodal trains are reduced.

The invention belongs to the field of big data processing, and discloses a crowd travel mode identification method and system for multi-view subspace learning based on dynamic and static feature fusion, and the method comprises the steps: carrying out the preprocessing of original signaling data, and cleaning invalid data; extracting a travel track of each user, and processing possible abnormal conditions of ping-pong data and drift data; after track data of a single user in a sample time period is obtained, segmenting the track data, and extracting a single trip chain track; extracting static characteristics such as travel behaviors and travel traffic of a travel chain in combination with multi-source data such as mobile phone signaling data, an OSM road network and public traffic data; extracting time sequence features by using an LSTM model, and modeling dynamic features of a travel track; static and dynamic features are fused, and travel modes are captured from multiple views. According to the method, the complex travel mode can be more accurately captured, data redundancy and noise are reduced, and the accuracy of travel mode identification is remarkably improved.

The invention relates to the technical field of logistics, in particular to a digital twin network traffic prediction method, device and equipment and a storage medium, and the method comprises the steps: obtaining historical traffic data of a digital twin network of a logistics circulation center, and obtaining traffic training data from the historical traffic data; constructing an initialization model based on the TimeGPT time sequence model, and performing parameter configuration on the initialization model to obtain a basic prediction model; performing training processing on the basic prediction model based on the traffic training data to obtain a traffic prediction model; acquiring updated traffic data from the digital twin network of the logistics circulation center according to a preset period, and performing updating training on the traffic prediction model according to the updated traffic data to obtain an updated traffic prediction model; acquiring real-time traffic data of the digital twin network of the logistics circulation center, and performing traffic prediction based on the updated traffic prediction model and the real-time traffic data to obtain a predicted traffic result; according to the method, the problem of overfitting of the model can be effectively prevented.

The invention belongs to the technical field of planing machining equipment, particularly relates to a Roots blower impeller planing device and method, and aims to solve the problems that in the prior art, when a Roots blower impeller is machined, the impeller is prone to position deviation and cannot be automatically positioned, and in the process of planing the impeller, the machining efficiency is high. In order to overcome the defects that in the prior art, a planing device comprises a mounting frame, stand columns are fixedly mounted at the four corners of the bottom of the mounting frame, and the four stand columns are fixedly connected with the same supporting plate, an impeller body can be stably positioned, so that position deviation of the impeller body in the planing process can be prevented; and meanwhile, when the impeller body is planed, the position of the impeller body can be conveniently changed, so that when the impeller body is planed, good convenience can be provided, and when the impeller body is planed, generated waste chips can be collected, so that good convenience is achieved.

The invention discloses a method and device for measuring the comprehensive value of an electricity-hydrogen coupling system, and relates to the technical field of novel energy storage, and the method comprises the steps: obtaining the related data of the electricity-hydrogen coupling system; an electricity-hydrogen coupling system comprehensive value measurement model considering external characteristics is constructed, and the electricity-hydrogen coupling system comprehensive value measurement model takes the minimum total operation cost of the electricity-hydrogen coupling system as a target by setting a target function and constraint conditions; inputting related data into a comprehensive value measurement model of the electricity-hydrogen coupling system, and comparing various cost changes before and after coupling hydrogen energy storage of the power system; and comprehensively evaluating contributions of the electricity-hydrogen coupling system in the aspects of economy, environmental benefits and power grid stability according to quantitative results of various cost changes of the system before and after configuration of the hydrogen energy system, and obtaining the comprehensive value of the quantified electricity-hydrogen coupling system. The energy utilization efficiency is improved, the operation cost is reduced, and the system stability is improved.

The invention discloses a service system and method based on an unmanned aerial vehicle, the service system comprises a support platform, a unified management and control and service platform and a business platform, the support platform is used for providing basic data and a data processing module; the unified management and control and service platform is used for providing functional modules and open capability services of the unmanned aerial vehicle, the open capability services are determined according to the functional modules, and the business platform is used for providing business application scenes of the unmanned aerial vehicle. According to the embodiment of the invention, unified management and control, resource sharing and deep integration with services of the unmanned aerial vehicles can be realized; and meanwhile, data security and operation security are effectively guaranteed, and cost reduction and benefit increase are realized.

The invention relates to the field of underwater topographic survey, in particular to an unmanned ship track planning system for underwater data acquisition, which comprises a GPS positioning module, a data acquisition module, a data processing module, an initial path planning module and a path adjustment module, and is characterized in that the GPS positioning module is used for obtaining the positioning of an unmanned ship and the positioning of a final destination of the unmanned ship; the data acquisition module is used for acquiring data; the data processing module is used for performing primary processing on the data; the initial path planning module is used for planning an initial path of the unmanned ship; and the path adjusting module is used for adjusting the path of the unmanned ship. According to the invention, the preset coordinates are generated through the initial path planning module, so that planning of the moving path of the unmanned ship is facilitated, it is ensured that the effective range of the collection range of the unmanned ship can spread over the whole river channel, and various data required for constructing the three-dimensional terrain model of the water bottom of the pond can be acquired.

The invention discloses a power system source-load-storage scheduling method considering demand response and a related device, and belongs to the technical field of power system scheduling. The method comprises the following steps: establishing each scheduling unit model of the novel power system; each scheduling unit model comprises a distributed power supply model, a thermal power generating unit model, an adjustable load demand response model, a composite energy storage model and a carbon transaction model; based on each scheduling unit model, establishing a power system source-load-storage scheduling model considering demand response; and solving the power system source-load-storage scheduling model considering the demand response to obtain an optimal scheduling scheme so as to regulate and control the output of each scheduling unit of the power system. According to the invention, multi-element adjustment of different load side resources is fully utilized to realize source and load coordinated interaction, the elasticity of the power grid and the capability of accepting new energy power generation are enhanced, and the construction of a novel power system with flexible interaction between new energy and demand side resources is promoted.

The invention discloses a hidden danger identification marking method for a ground subsidence disaster of a power transmission line tower foundation. Comprising the steps of power transmission line tower foundation land subsidence disaster investigation, research area remote sensing image data query, optical remote sensing data interpretation, InSAR data interpretation, field investigation, optical image data interpretation mark analysis, InSAR interpretation mark analysis and field investigation mark analysis. According to the method, the multi-source remote sensing technology and the field investigation are utilized to carry out the method for identifying the potential ground subsidence hazard of the power transmission line tower foundation on the regional scale, and each step and the adopted method in identifying the potential ground subsidence hazard hazard of the power transmission line tower foundation by utilizing the multi-source remote sensing technology and the field investigation are standardized; the working efficiency, the recognition precision and the economic benefit of the hidden danger recognition of the ground subsidence of the power transmission line tower foundation can be practically and effectively improved; and the technical process of power transmission line tower foundation land subsidence disaster investigation can be effectively standardized.

The invention relates to a cloud disk snapshot capacity determination method and device, computer equipment, a medium and a product, and the method comprises the steps: determining a target storage block corresponding to a snapshot chain of each application scene in response to a trigger instruction, and determining the cloud disk snapshot capacity corresponding to each application scene according to the target storage block corresponding to each snapshot chain. By determining the cloud disk snapshot capacity corresponding to each application scene, storage resources can be reasonably allocated and resource configuration can be optimized according to the respective accurate snapshot capacity use condition, and the situation of resource waste or insufficiency is avoided. Meanwhile, the enterprise user can better plan the cloud storage budget and the resource use strategy according to the accurate snapshot capacity information in each scene, the resource utilization efficiency is improved, and the operation cost is reduced.

The utility model discloses a vertical case unpacking machine, and relates to the technical field of vertical case unpacking machinery. The device comprises a box blank repository, an executing mechanism, a movable suction cup set assembled on the executing mechanism and a box body fixing assembly. The executing mechanism drives the movable suction cup set to suck a box body I on one side of a box blank in the box blank repository, take the box blank out of the box blank repository and move the box blank to the box body fixing assembly, and the box body fixing assembly fixes a box body II on the other side of the box blank. The box body II on the other side refers to the box body II located on the opposite side of the box body I sucked by the movable suction cup set in the box blank forming state; the executing mechanism drives the movable suction cup set to integrally do curve track motion, the movable suction cup set sucks the box body I to drive the box body I to do curve track motion, and the box blank is opened and formed. The vertical case unpacking machine can improve the case blank forming effect and the case blank forming success rate, and is particularly suitable for case blanks made of soft materials, case blanks which become soft due to large humidity or case blanks which are repeatedly used.