161results about How to "Reduce randomness" patented technology

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention relates to the technical field of data processing, and discloses a CNN-BiLSTM-MATT time sequence prediction method, which comprises a plurality of feature value inputs and a feature value output. Carrying out data normalization processing; performing normalization processing on the input data, mapping a normalized data set between [-1, 1], and performing reverse normalization processing on the output data after prediction; the hyper-parameters in CNN-BiLSTM-MATT are optimized, the hyper-parameters in CNN-BiLSTM are initialized according to an IHO algorithm, the hyper-parameters comprise the learning rate, the L2 regularization coefficient and the hidden layer number, and the optimal hyper-parameter value is obtained in the iteration process; performing feature extraction: performing convolutional feature extraction by using a convolutional neural network CNN, and taking obtained data features as input of subsequent BiLSTM; and carrying out time sequence analysis. The method overcomes the defects of a current time sequence prediction method. The method can be widely applied to the parameter optimization problem, the convergence degree is improved, and the optimal solution is efficiently found.

A trajectory planning method for optimal cooperative assembly of double mechanical arms comprises the steps that S1, improved DH parameters of the mechanical arms are determined, and the coordinate range of a cooperative space is obtained through a Monte Carlo method; s2, the pose of the optimal assembly position of the double mechanical arms is solved through a particle swarm algorithm within the coordinate range of the cooperation space; s3, on the basis of pose information of end executors of the double mechanical arms, a B-spline interpolation method is adopted to calculate change parameters of all joints of the double mechanical arms, and speed and acceleration curves of all the joints are constructed; S4, an objective function is constructed, and a joint position sequence is obtained through calculation according to the pose of the optimal assembly position and the pose change curves of all the joints of the double mechanical arms; and then an NSGA-II optimization algorithm is adopted to select an optimal track meeting the objective function and the physical constraint condition of the mechanical arm from the joint position sequence. By introducing the operability, the time and energy consumption in the double-mechanical-arm assembling process are reduced, and the stability of the movement track is improved.

The invention provides a channel modeling method with a horizontal coupling distance and based on surface wave transmission. The channel modeling method comprises the following steps: step 1, setting a transmitting end, a receiving end and a metal tube; 2, carrying out initial measurement; step 3, calculating a path loss value according to the measurement data; 4, changing the distance between the transmitting end and the receiving end and repeating the step 3; 5, changing the height difference between the sending end and the receiving end, and repeating the steps 3-4; step 6, constructing a path loss model with receiving end horizontal coupling distance influence; step 7, obtaining a final path loss model; and step 8, inputting the distance d between the transmitting end and the receiving end and the horizontal coupling distance of the receiving end into the final path loss model to obtain a path loss value of a receiving-transmitting end channel under a corresponding condition. Compared with the prior art, the method is more convenient for guiding optimized layout of signal coverage and working nodes in an industrial Internet of Things environment.

The invention relates to the technical field of vehicle control, and discloses a vehicle-mounted semantic standardization method, device and equipment, a medium and a product, and the method comprises the steps: obtaining a pre-trained large language model; obtaining a target original vehicle control command; obtaining a format template used for representing the target standard vehicle control command; forming a cue word according to the target original vehicle control command and the format template; and inputting the target original vehicle control command and the cue word into the large language model, and outputting the converted target standard vehicle control command through the large language model. Based on the ability of the large language model to directly understand the text meaning, the original vehicle control command and the standard vehicle control command are directly paired to generate the training sample of the text style, so that the training sample is input into the large language model for training, the threshold of model training is reduced, and the training efficiency and the training accuracy are improved; and high accuracy and high automation degree of vehicle-mounted semantic standardization are considered.

The invention discloses a dense filtration process cooperative control method, device and equipment and a storage medium. The method comprises the following steps: acquiring historical sampling data, defining a plurality of modeling variables, performing gray correlation analysis on the historical sampling data to obtain a gray correlation coefficient of each modeling variable, and establishing a feed quantity prediction model according to each modeling variable by utilizing a partial least square method; acquiring real-time sampling data, inputting the real-time sampling data into the feeding amount prediction model to obtain a feeding amount prediction value, and establishing an uncertainty optimization model according to the real-time sampling data and the feeding amount prediction value; and performing deterministic form conversion on the uncertainty optimization model to obtain a deterministic equivalence model, and solving the deterministic equivalence model through an opportunity constraint optimization algorithm to obtain a deterministic feeding amount. According to the feeding amount prediction model, the randomness of the feeding amount prediction value is reduced, the problem caused by the randomness of the feeding amount can be solved through the dense dehydration process constraint optimization method, and the optimization effect of the optimization method is further improved.

The invention relates to the technical field of photovoltaic inverters, in particular to a photovoltaic inverter controller parameter identification method and system, and the method comprises the steps: building a mathematical model of a photovoltaic inverter controller, and determining controller parameters needing to be identified; the mathematical model is converted into a difference equation through transformation decoupling operation; the input and output quantity of an inverter controller is obtained in real time to serve as the input quantity of an identification system, and the preset performance index minimization serves as the optimal adaptive value estimation of an optimization algorithm; and stopping iteration when the parameters of the controller to be identified reach preset precision, and outputting estimated values of the parameters of the controller. The method has the advantages that the optimal solution is found through the idea of minimizing the residual module value, the randomness of identifying the parameters of the controller is greatly reduced, the accuracy of the identification result is improved, online identification and synchronous identification of all the parameters can be achieved, and step-by-step identification of all the parameters is avoided.

A straight line fitting method for image detection in a complex interference environment comprises the following steps: firstly, carrying out interference point deletion on a feature point set of an environment image, then carrying out multiple times of random sampling, forming a total point set by using associated points through distance operation, obtaining a plurality of straight line models through initial fitting, and carrying out linear fitting on the linear models; and then calculating an interior point proportion in the total point set according to the linear model, finally selecting the total point set with the maximum number of interior points, and continuously reducing a residual threshold in multiple iterative calculations to improve an interior point proportion threshold by dynamically adjusting a feedback mechanism of the residual threshold when the interior points are calculated. In the initial stage of iteration, more points can enter the model to ensure the diversity and robustness of the model, and the threshold value is gradually tightened along with the convergence of the model, so that the interior points conforming to the model are screened out more accurately. And finally, performing secondary sampling on the inner points and performing straight line fitting on an inner point set subjected to secondary sampling again to eliminate potential error inner points and further improve the fitting precision.

The invention discloses a distributed photovoltaic power prediction method and system based on time sequence data reconstruction. The method comprises the following steps: collecting a plurality of single-class distributed photovoltaic power curves; adopting improved photovoltaic power data empirical mode decomposition to decompose the plurality of single-class distributed photovoltaic power curves into sub-power curves with different frequencies; the sub-power curves of different frequencies are reconstructed; a neural network is adopted to predict the reconstructed sub-power curves, and then superposition and summation are carried out on predicted values of the sub-power curves to obtain distributed photovoltaic power data of a certain type; according to the method, photovoltaic power is classified into single-class curves of different scenes according to different seasons and weathers, and sub-power curve division is performed by using improved empirical mode decomposition, so that the randomness and volatility of original data can be reduced to the greatest extent before prediction, a neural network can be conveniently and efficiently trained, and the prediction efficiency is improved. And outputting accurate photovoltaic power time sequence prediction data.

The invention discloses an electric vehicle charging load prediction method combining an attention mechanism and multi-scale features. The method comprises the following steps: firstly, combining a whale optimization algorithm containing a differential variation factor and a nonlinear convergence factor with variational mode decomposition, and decomposing an electric vehicle charging load sequence aggregated according to hour-level granularity to obtain a load component; establishing a multivariate linear model to realize meteorological characteristic screening; then, an electric vehicle charging load prediction model combining an attention mechanism and multi-scale features is established, and deep learning is carried out on load components and meteorological features; and finally, adding load component prediction results to obtain a final electric vehicle charging load prediction value. Aiming at the problems that the existing electric vehicle charging load data feature mining is insufficient and the model is insufficient in important feature capturing capability, the hidden information in the electric vehicle charging load data can be fully mined, the features of different levels and different time scales are extracted, and the accuracy of load prediction is improved.

The invention discloses an intelligent file cabinet file query method based on the RFID technology, and relates to the technical field of intelligent file management.The internal space of a file cabinet is divided into small grid units through virtual grid modeling, each grid has a clear number, preliminary positioning of each RFID tag is achieved, and the file query efficiency is improved. According to the method, the file is not limited to an approximate area any more and is accurate to a specific grid, multi-antenna cooperative reading is combined with multiple reading and analysis of signal intensity and phase angles, the positioning accuracy is further improved, errors can be corrected according to signal differences and phase angles through a maximum likelihood estimation model, more accurate file position query is achieved, and the positioning accuracy is improved. According to the technical scheme, labels in a coverage area are read for multiple times in a multi-antenna cooperative reading mode, interference is reduced through signal calibration between the antennas, and errors caused by signal reflection, interference or overlapping are reduced by carrying out weighted average on signal strength and correcting differences between the antennas through a least square method.

The invention discloses an old block three-dimensional form intelligent updating optimization method based on rigid-flexible rule driving. The method comprises the steps of collecting vector data of a target old block and design specifications and standard text data of a city where the target old block is located; establishing a rule map of urban design rigid industry specification requirements; a Mask R-CNN coding technology is used to automatically label vector data of a space structure, a function layout, a road system and a blue-green system of the design, a knowledge graph of a classic city design result rule is formed, and a rule graph oriented by city design flexible professional knowledge is established; establishing multi-layer network association of an old block three-dimensional morphological graph network, a rigid rule graph and a flexible knowledge graph; and outputting the optimized three-dimensional form scheme of the target old block to a space holographic sand table, and finally outputting an optimized three-dimensional form graph network and a visualization scheme of the old block. According to the method, the three-dimensional form of the old block can be optimized based on rigid rule and flexible knowledge hybrid driving.

The invention discloses a coal-to-electricity project-oriented distribution network economic operation evaluation system and method in the technical field of power systems. The method comprises the steps of obtaining power distribution system indexes; subjective weighting is carried out on the power distribution system indexes through an analytic hierarchy process to obtain index subjective weights; objective weighting is carried out on the power distribution system indexes through a CRITIC method, and objective weights of the indexes are obtained; and after combined weighting is carried out based on the index subjective weight and the index objective weight, calculating a distribution network comprehensive evaluation score after coal-to-electricity project reconstruction, and evaluating the reconstruction effect. According to the method, the analytic hierarchy process and the CRITIC method are adopted for combined weighting for the power distribution network of the coal-to-electricity project, subjective and objective factors are comprehensively considered, the prejudice of a single factor is avoided, and the accuracy and reliability of an evaluation result are improved.

The invention discloses a regional load prediction method, system, equipment and medium, and particularly relates to the technical field of power load prediction, and the technical key points are as follows: a plurality of load nodes are clustered in sequence according to weather data, population flow data and urban power consumption characteristics by using a third-order clustering analysis method to obtain a tree-type clustering cluster topological structure; a representative load node is selected from each cluster subset, average correlation coefficients between influence features in weather data of each representative load node and load data are calculated, and the influence features of the first three maximum average correlation coefficients are selected as influence input features of the representative load node; performing modal decomposition on the load data representing the load nodes by using an EMD decomposition method to obtain a plurality of connotation modal components and residual terms; and inputting the influence input feature of each representative load node and the plurality of decomposed connotation mode components into a load prediction model to obtain a load prediction result corresponding to each representative load node.

The invention provides a mobile platform short-term photovoltaic power generation power prediction method and device based on weather type division, and the method comprises the steps: collecting photovoltaic data and meteorological data at different moments in a to-be-measured time period based on a built photovoltaic power generation power prediction mobile platform, the meteorological data comprises rainfall data at each moment; correcting the rainfall data at each moment, and updating meteorological data according to the corrected rainfall data at each moment; performing similar day clustering according to the photovoltaic data and the updated meteorological data, and determining a weather type; and according to the weather type, the photovoltaic generation power in the time period is predicted based on a pre-constructed CNN-LSTM combined deep learning model, so that the randomness and volatility of single-machine photovoltaic output can be reduced, and the precision of photovoltaic generation power prediction can be improved.

The invention discloses an intelligent passenger car obstacle avoidance path planning method based on a genetic algorithm and extended RRT, and belongs to the technical field of intelligent car path planning. Firstly, a sampling flexible boundary is set according to a global path, an RRT algorithm sampling space is reduced, and a feasible path is guided to be close to the global path through a heuristic extension node; designing a metric function for search tree nodes to select father nodes, establishing a passenger car simplified model to detect collision, then constructing a path optimization area, solving an optimal obstacle avoidance path by using a genetic algorithm, and finally smoothing the path by using a Bezier interpolation method; according to the method, the obstacle avoidance local path can be quickly planned in consideration of the path quality and safety, the safety of the vehicle during driving is remarkably improved, and the accident rate of the vehicle is effectively reduced.

The invention belongs to the field of ball ultra-precision machining, and particularly relates to a ball controllable orbital transfer circulating mechanism for a horizontal ball grinding machine and a grinding method. According to the technical scheme, the ball controllable orbital transfer circulating mechanism for the horizontal ball grinding machine comprises a material disc, a ball outlet sliding rail and a ball inlet sliding rail, and the material disc, the ball outlet sliding rail and the ball inlet sliding rail are each of an upper-layer structure and a lower-layer structure; a grinding mechanism of the horizontal ball grinding machine comprises a flat plate and a grinding plate, a material plate is connected with the upper portion of the grinding plate through a ball outlet sliding rail, the material plate is connected with the lower portion of the grinding plate through a ball inlet sliding rail, and a circular conveying mechanism of balls is formed. According to the ball controllable orbital transfer circulating mechanism for the horizontal ball grinding machine and the grinding method, balls can be controllably and alternately ground in the inner grinding orbit and the outer grinding orbit, the grinding area envelope lines of the balls are roughly the same, the sphericity of products of the same batch is more uniform after the balls are machined, and the grinding quality is improved. And the equal cutting probability of the product is improved.

The invention discloses an interactive document structured information extraction method and device, electronic equipment and a storage medium, and particularly relates to the field of document processing. The interactive document structured information extraction method comprises the steps that a document is recognized, and a document format is obtained; matching the document format with a sample format stored in a knowledge base, selecting a matched format, and executing a corresponding task instruction to extract document structured information; the user performs error correction on the extraction result; outputting an accurate result and verifying the consistency of the extraction result and the accurate result; querying a similar task instruction in a knowledge base according to the accurate result, and generating a new task instruction; and executing the new task instruction on the document format to obtain a test result, and verifying the consistency of the test result and the accurate result. According to the method, dynamic learning and optimization can be realized, the accuracy and robustness of information extraction are remarkably improved, the method has strong instant learning ability and adaptivity, and the learning and training cost is reduced.

The present invention discloses a trigger merging method, system, medium and product based on K-means clustering. The method of the present invention comprises obtaining all triggers in the same clock domain in the layout completed design and putting them into a list; taking a trigger from the list as the current cluster center, calculating the distance from other triggers to the trigger; if the distance is less than T1, adding the trigger to the class corresponding to the current cluster center; if the distance is less than T2, deleting the trigger from the list; repeating the above steps to finally obtain K Cluster centers are assigned to triggers according to the cost value. K The present invention aims to reduce the number of clock tree leaf nodes and thus reduce the line length of the clock network and the number of clock buffers required on the clock network, thereby reducing the area and power consumption of the clock network and better controlling the clock deviation by automatically selecting and determining the number and position of the initial cluster centers and merging the clustered triggers.

This application provides a method, apparatus, and electronic device for planning an escape route for an automated valet parking vehicle. The method includes: acquiring reference lines, multiple anchor points, and scene type information of the scene where the vehicle is located; calculating the reference line cost of a search point based on the reference lines; calculating the anchor point cost of the search point based on the multiple anchor points; adjusting the weights of the reference line cost and anchor point cost according to the scene type information; and executing a path planning algorithm based on the adjusted reference line cost and anchor point cost to generate an escape route for the automated valet parking vehicle. This method aims to improve the search efficiency of the path planning algorithm.

The present application relates to the technical field of bridge quality detection, especially to a continuous beam bridge different span loading load test method, comprising the following steps: determining a control section on the continuous beam bridge; measuring the axle load and the wheelbase of the test vehicle; calculating and analyzing the interval area of the calculated bending moment change effect function with the moving vehicle; analyzing and calculating the interval area of the measured bending moment change effect function with the moving vehicle; comparing the ratio of the interval area of the calculated bending moment change effect function with the moving vehicle and the interval area of the measured bending moment change effect function with the moving vehicle through the calibration coefficient to determine whether the bearing capacity requirement is met. The continuous beam bridge different span loading load test method can effectively reduce the arrangement of measuring points and the need for test vehicles, and quickly and accurately judge the bearing capacity state of the bridge by calculating the bending moment change effect function with the moving vehicle and the measured bending moment change effect function with the moving vehicle.

The invention discloses a ship aging management object screening method, and the method comprises the following steps: 1), obtaining a ship composition element diagram for a ship product to be subjected to aging management; 2) constructing an aging management demand degree comprehensive evaluation model; 3) obtaining the evaluation grade of each evaluation index of the evaluation object; 4) constructing an evaluation decision evaluation matrix, and performing aging management priority evaluation; 5) determining a solution with the highest priority for elements in the evaluation matrix; 6) calculating the similarity of the cloud model; 7) calculating a correlation coefficient Ri based on cloud model similarity; 8) performing aging management priority evaluation on the m evaluation objects in the ship composition element graph according to the correlation coefficient Ri based on the cloud model similarity; and 9) obtaining the sequence of the aging management priorities of the basic elements in the whole ship range. According to the ship aging management object screening method based on the cloud model theory, fuzziness and randomness generated in the subjective evaluation process are reduced.

A self-adaptive multi-objective evolution method for optimizing the capacity of an off-grid wind and light hydrogen production and energy storage system comprises the following steps: firstly, according to an energy storage system model, taking unit hydrogen production cost, wind and light abandoning penalty and load shedding penalty as minimization objectives; secondly, the capacity of each component of the wind-solar power generation unit, the power storage unit and the hydrogen and oxygen storage unit is used as a decision variable, and the system is set to conform to the actual constraint; thirdly, initializing a population; individuals in the population are initialized according to the constructed search space, so that the individuals meet constraint conditions; then adaptively selecting a search strategy; the strategy adaptive network selects a proper search strategy based on individual decision variables and objective function values, and evolves a new sub-population; and fusing the parent population and the child population to calculate the non-dominated level and the congestion degree of all individuals, determining parameters of the reward updating strategy adaptive network according to the non-dominated level, and screening out a new population. According to the method, the randomness and blindness of algorithm search can be reduced, and the search efficiency of the algorithm is enhanced.

The invention relates to the field of microbiological examination laboratory equipment, and discloses a drying and disinfecting device and method for a specimen glass slide for microbiological examination. The device comprises a machine shell, a motor and a controller, the detachable rotating shaft is in transmission connection with the driving motor; the support frame is detachably arranged on the detachable rotating shaft and forms a plurality of mounting surfaces; the glass slide mounting plate is detachably mounted on the mounting surface, and a plurality of glass slide mounting stations are arranged on the glass slide mounting plate; the drying module comprises a drying lamp; the disinfection module comprises an ultraviolet disinfection lamp; and the control module is electrically connected with the driving motor, the drying module and the disinfection module and is used for executing a drying program and a disinfection program. The problems that in an existing glass slide processing method, the drying capacity and uniformity are difficult to consider at the same time, the drying process is uncontrollable, and low efficiency and safety risks are caused by separation of the drying process and the disinfection process are effectively solved.

This application provides a model training method, a coarse ranking method, an apparatus, an electronic device, and a storage medium. The method includes: inputting model training samples into a coarse ranking model to be trained; encoding user feature data and object feature data of multiple candidate objects to obtain corresponding user feature vectors and object feature vectors corresponding to each candidate object; fusing the user feature vectors and object feature vectors to obtain predicted conversion rate and predicted click-through rate; fusing the user feature vectors with the object feature vectors corresponding to any two candidate objects according to the ranking results to obtain a ranking factor; calculating the loss of the predicted conversion rate, predicted click-through rate, and ranking factor to obtain the loss value of the coarse ranking model to be trained; updating the model parameters of the coarse ranking model to be trained based on the loss value, and iteratively training the coarse ranking model until it converges to obtain the coarse ranking model.

The invention discloses a power distribution network division data model based on power grid load flow spatial distribution and a data algorithm thereof, which introduces a newly constructed load flow spatial distribution data model on the basis of vectorization clustering processing of power distribution network load data. According to the data model, a geometrical and visualized data structure is constructed through a space field of a load vector, data optimization is carried out in the mass center initialization process of a K-means algorithm and / or the mass center iteration process based on the Euler distance, and adjustability and optional comparison of different space distribution scales are adapted. And optimizing grid division of the load-complementary power distribution network under different space distribution scales. According to the method, the grid division efficiency and effect of the power distribution network can be greatly improved.

The invention relates to the technical field of product design methods, in particular to an adhesive mixing reaction equipment design method based on industrial big data, which comprises the following steps: performing stage division according to a set mixing reaction process, and determining a target state feature of each stage; carrying out numerical simulation analysis on the rationality of the target state characteristics of each stage; obtaining discrete state process control quantity according to each target state characteristic; and designing a distribution scheme of equipment sensors according to the process control quantity of each state, and designing structural characteristics of the equipment according to the state characteristics of each target. According to the method, the performance of the equipment can be foreseen in the initial design stage, and time and resource waste is avoided; standardized target state features are established, so that each module is more efficient in later equipment integration, and the debugging time and complexity of the whole equipment are reduced; through the design of the sensor system and the state process control quantity, the equipment can monitor key parameters in real time and provide instant feedback.

The invention discloses a regional geological disaster vulnerability evaluation method, device and equipment and a medium, and relates to the technical field of geological disaster evaluation, and the method comprises the following steps: obtaining a plurality of evaluation indexes used for evaluating the vulnerability of a geological disaster in a target region; dividing the terrain of the target area into a plurality of grids, and obtaining a plurality of evaluation index values of the corresponding terrain in each grid; fitting the plurality of evaluation index values in each grid to obtain a vulnerability value of each grid; taking the discourse domain in the cloud model as an abscissa and the membership degree as an ordinate, and constructing a standard cloud picture for representing the vulnerability level; inputting the vulnerability value of each grid into the cloud model to generate a sample cloud picture, comparing the membership degree of the sample cloud picture with a plurality of membership degrees of a standard cloud picture to obtain a comparison result, and obtaining the vulnerability level of the target area according to the comparison result. According to the method, fuzziness and randomness can be reflected through the difference value of the membership degrees, and the accuracy of vulnerability evaluation is improved.

The present invention discloses a gesture recognition method based on a simple recurrent unit with attention-guided spatial graph convolution. First, the present invention embeds the spatial graph convolution into the gate structure of the simple recurrent unit (SRU), so that the SRU with high-speed parallel computing capability can model the time domain and spatial domain information of complex gestures. Secondly, a finger joint attention guidance module is introduced to give more important finger joints a higher degree of attention. Then, a regularization method of attention-enhanced graph discarding is introduced to alleviate the disadvantage of overfitting of gesture data. The present invention has a high recognition accuracy for fine dynamic gestures while maintaining excellent recognition calculation efficiency, and can have broad application prospects in the fields of human-computer interaction, virtual reality, sign language translation, etc.

The present invention discloses a method for predicting the pore pressure of the formation to be drilled ahead of the drill bit based on the grey prediction theory, applies the grey prediction theory to predict the pore pressure of the formation to be drilled ahead of the drill bit during the drilling process, and constructs a pressure prediction model. This solution can provide on-site drilling technicians with relatively accurate pore pressure information of the formation to be drilled ahead of the drill bit, and can perform a dynamic risk assessment of the drilling operation process based on the prediction result of the pore pressure of the formation to be drilled, and then optimize and adjust the drilling construction measures according to the risk assessment result, thereby assisting the drilling technicians to make quick and accurate decisions and prevent the drilling risk from further deteriorating, and finally reduce the operational risk caused by inaccurate understanding of the pore pressure information, so as to achieve the goal of reducing the drilling risk or risk-free drilling.