52results about How to "Reduce computing pressure" patented technology

The application provides a distributed urban drainage monitoring method, system, device and medium, relates to the technical field of urban drainage monitoring, and comprises the following steps: acquiring monitoring data of a plurality of monitoring nodes in a target drainage area; according to a topological connection relationship of an urban drainage pipe network, the monitoring nodes are divided into at least one local topological subnet; the number of local topological subnets is configured with a corresponding edge computing node; the edge computing node determines a first monitoring result based on the monitoring data of the monitoring nodes and corresponding standard monitoring data; if the first monitoring result is abnormal, the self-state of the monitoring node is pre-checked, if it is determined that the monitoring node has a self-fault, the monitoring data of the monitoring node is reported to a central cloud platform based on the edge computing node; if it is determined that the monitoring node has no self-fault, the monitoring node is marked as a suspected event node; a corresponding edge computing node triggers a distributed collaborative diagnosis process to obtain a second monitoring result and report the second monitoring result to the central cloud platform.

The invention discloses an electric vehicle distributed charging optimization method and system based on a carbon emission sensitive dynamic electricity price, and the method comprises the steps: setting an initial iteration strategy based on a controllable electric vehicle load model, and averagely distributing the needed charging amount to all charging time periods; defining the size of an algorithm convergence criterion according to the required convergence precision and the number of the electric vehicles; constructing a dynamic electricity price containing coupling of a load item and a carbon emission item, and calculating an EVn cost function according to the dynamic electricity price; starting a kth round of iteration, traversing all EVn in each round of iteration, and counting k from 0; other electric vehicle charging power vectors except the EVn are calculated; after each iteration, checking whether a convergence criterion is met or not; if yes, the obtained load curve is an approximate solution of Nash equilibrium, and if not, the charging strategy is updated repeatedly; and after the load curve under the dynamic electricity price mechanism is obtained, the load curve under the time-sharing static electricity price with the same electricity price level is obtained through calculation. The method is suitable for electric vehicle group charging management in a community scene.

This application relates to the field of sensor technology, providing a force sensor and its fabrication method. The force sensor has a disk-shaped structure, including four irregularly shaped elastic beams connected between an outer support ring and a central support platform, and multiple strain gauges on the main surface of the elastic beams. The main surface is parallel or substantially parallel to the disk surface of the disk-shaped structure. Each of the four elastic beams includes four straight arms. The two ends of the first straight arm are orthogonally connected to the second and third straight arms, respectively, and the middle of the first straight arm is orthogonally connected to the fourth straight arm. The fourth straight arm is located on either side of the second and third straight arms. The outer ends of the fourth straight arm and the second and third straight arms are each connected to the central support platform or the outer support ring. Because the irregularly shaped elastic beams include multiple orthogonal straight arms, the strain gauges can be flexibly arranged in a bridging configuration, making the bridging output sensitive only to specific forces or torques, thus improving the decoupling capability of the sensor structure. Simultaneously, placing the strain gauges only on the main surface reduces the mounting difficulty.

The application relates to the technical field of storage computing, and discloses a system, a method and an electronic device for directly searching data in a storage medium, the system comprising a large-scale vector database, a nearest neighbor search operation unit and a host computer, the large-scale vector database is arranged in the storage medium and is used for storing a vector set; the nearest neighbor search operation unit is arranged in the storage medium; the host computer is provided with a CPU and a cache unit; when the nearest neighbor search operation unit receives an NNS_CSD instruction sent by the CPU, the vector of the database is read, similarity measurement is carried out, and the search result meeting the condition is sent to the cache unit, the application directly searches data in the storage medium, the NNS_CSD instruction is used to start the calculation in the storage medium, the nearest neighbor search operation unit in the storage medium is used for vector reading and similarity measurement, compared with a traditional architecture, the migration of data is greatly reduced, and the overall performance is improved.

This invention discloses a maximal clique enumeration method based on MPICH parallel computation for social network streaming data. This method utilizes a structural inversion clique tree (ICTree) to store maximal cliques in the graph. Parallel programming of the algorithm is achieved through the high-performance and widely portable message passing interface MPICH. As the graph structure changes under the streaming data, multiple processes compute in parallel and update the inversion clique tree (ICTree) structure, enumerating maximal cliques in the graph and all maximal cliques in the subgraph formed by nodes within a certain index interval in real time. This invention does not distinguish between edge addition and subtraction changes in the graph during maximal clique enumeration, enabling maximal clique enumeration under mixed edge addition / subtraction updates and large-batch updates. Furthermore, the use of a predefined number of processes for parallel computation solves the real-time processing problem caused by large data volumes, improving the time efficiency of maximal clique enumeration.

The invention provides an animation generation method and device, electronic equipment and a storage medium. The method comprises the following steps: determining an original layer set for generating animations in a to-be-drawn layer set of a current frame; according to the hierarchical relationship of the image layers, each original image layer in the original image layer set is superposed in sequence through downsampling processing to obtain a reference image layer, and the resolution of the reference image layer is lower than that of the image layers in the original image layer set; an animation layer corresponding to the current frame is determined according to the reference layer, and the resolution ratio of the animation layer is higher than that of the reference layer.

The invention provides a control method based on a charging station energy management system, and the system comprises an energy management host and a plurality of energy management sub-controllers which are in communication connection with the energy management host. Comprising the steps that S1, the energy management host receives state information sent by the multiple energy management sub-controllers respectively, and the state information comprises the state of charge SOC, the required power Np and the buffer distribution proportion B of the corresponding electric equipment; s2, the energy management host obtains the total power MPW of the charging station, obtains the total power APW of the upper limit value of the energy buffer area distributed to each energy management sub-controller, and calculates the residual distributable power RPW; in the charging peak period, when the multiple sub-controllers carry out rapid power adjustment at the same time, the response time can still be stably kept at the millisecond level, so that rapid suppression can be achieved during instantaneous load fluctuation, and the problems of transformer overload and power distribution imbalance are effectively prevented.

The invention discloses a complex dynamic industrial visual positioning method and system based on multi-agent collaboration, and relates to the technical field of machine vision, distributed visual agent nodes are deployed in an industrial dynamic operation scene, and node microsecond-level time synchronization, space joint calibration and communication networking are completed; the nodes synchronously collect industrial images according to a preset time sequence, and a standardized data set is generated through preprocessing; dynamic target feature extraction is completed based on an improved deep convolutional neural network, and cross-node feature matching is completed in combination with epipolar constraints; a multi-view geometric constraint model is constructed to solve the three-dimensional pose of the target, and a positioning result is generated through dynamic interference compensation optimization; and outputting a positioning result, completing closed-loop feedback, and dynamically adjusting node parameters to realize multi-agent collaborative global optimization. According to the invention, the precision and the anti-interference capability of visual positioning in a complex dynamic industrial scene are improved, the multi-node load balancing is optimized, and the visual positioning operation requirements of various industrial dynamic scenes can be met.

The application discloses a kind of multi-source big data acquisition and pre-processing database construction method and system, it is related to database technical field, the present application completes multi-source heterogeneous data acquisition using edge-center two-level distributed architecture, and the security of data transmission is guaranteed by transmission encryption and integrity check mechanism, hierarchical pre-processing is carried out to data to realize heterogeneous data semantic alignment and multidimensional fusion;Distributed hybrid storage architecture is constructed, adaptive database index mechanism is set, and through multidimensional data quality evaluation, the incremental update and full-amount synchronization of database are realized.The present application can realize the unified collection of various source data, standardized processing, effectively break the data island barrier, significantly improve data processing efficiency and storage running performance, and stably guarantee the overall data quality and data consistency of database.

The application discloses a kind of based on thing networking's wisdom building energy consumption management and equipment monitoring system, belong to building intelligent management technical field.System uses layered distributed architecture, is composed of perception layer, network transmission layer, edge computing layer, platform application layer, terminal control layer;Perception layer realizes energy consumption, equipment, environmental global data acquisition, network transmission layer uses wired wireless dual-mode communication to guarantee data transmission, edge computing layer realizes local instant processing and emergency control, platform application layer realizes cloud big data analysis, visual management, intelligent strategy generation, terminal control layer realizes equipment automation regulation and control.The application overcomes the defects of traditional building management energy consumption waste, operation inefficiency, data isolation, insufficient intelligence, can realize building energy consumption accurate measurement, dynamic analysis, intelligent regulation and control, equipment full period monitoring, fault early warning, automation operation, effectively reduce building energy consumption, reduce operation cost, improve operation safety, applicable to various office buildings, shopping malls, communities, parks and other buildings, with excellent novelty, creativity and practicality, meet the development needs of green low-carbon building.

The invention relates to a transformer area operation and maintenance method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: an edge control layer receives equipment state information uploaded by power distribution Internet of Things equipment in a to-be-operated and to-be-maintained transformer area, obtains a state diagnosis result and alarm information of the power distribution Internet of Things equipment according to the equipment state information, and sends the state diagnosis result to the power distribution Internet of Things equipment according to a time interval corresponding to an alarm level of the alarm information; and uploading the equipment state information, the state diagnosis result and the alarm information to a cloud, generating an operation and maintenance operation instruction for the power distribution Internet of Things equipment by the cloud based on the equipment state information, the state diagnosis result and the alarm information, returning the operation and maintenance operation instruction to an edge control layer, and sending the operation and maintenance operation instruction to the edge control layer under the condition that the digital signature of the operation and maintenance operation instruction passes verification. And the edge control layer operates and maintains the power distribution Internet of Things equipment according to the operation and maintenance operation instruction. The edge control layer is introduced to execute filtering and processing of the equipment information, data uploading is executed according to the preset time interval, operation is executed by the cloud end in sequence, and therefore the computing pressure of the cloud end is reduced.

The application discloses an unmanned aerial vehicle obstacle avoidance method based on digital twinning in an unstructured scene, which comprises the following steps: S1, constructing a geometric perception digital twinning system; S2, collecting unknown obstacle information and performing geometric synchronization; S3, performing dynamic modeling and discretization on a virtual unmanned aerial vehicle; S4, constructing a pseudo-control input and a linear prediction model; S5, generating a safe flight corridor; S6, performing local re-planning; S7, establishing a disturbed system and an error system; S8, constructing a robust positive invariant set and performing constraint tightening; S9, constructing a space adaptive tube MPC optimization problem; S10, performing online space adaptive adjustment; and S11, outputting a control instruction and maintaining virtual-real synchronization. By mapping unknown obstacles in a physical space to a virtual space in real time, a safe flight corridor is constructed, local re-planning and space adaptive tube MPC are combined, and the unmanned aerial vehicle can realize safe obstacle avoidance, high-precision trajectory tracking and low control energy consumption in an unstructured environment.

The invention discloses an oil field video monitoring early warning method. The method specifically comprises the following steps: acquiring a monitoring video shot by monitoring equipment connected with an analysis server in real time; identifying an abnormal behavior in the monitoring video and determining an abnormal event type; generating alarm data according to the abnormal event type, the abnormal behavior occurrence time and the place; sending the alarm data to a business server; wherein the alarm data is used for indicating the service server to determine an early warning level and send early warning information to a user level corresponding to the early warning level. The invention further discloses an oil field video monitoring early warning device and a storage medium. The problem that in the prior art, the number of video monitoring devices in an oil field video monitoring system is large, and a service server cannot adapt to the video monitoring devices is solved.

The application discloses a kind of based on partial quantification's hybrid expert network optimization method, it is related to information technology field, including the following steps: S1, selected data sample set, carries out hybrid expert network sampling;S2, establishes the corresponding relationship of subnet and data set, selects high-frequency subnet and corresponding data set;S3, with corresponding data set to selected high-frequency subnet is iterated quantization processing.The application obtains the corresponding relationship of different data set and different subnet in hybrid expert network by carrying out data flow sampling to the reasoning process of hybrid expert network, then carries out quantization optimization to different subnet on corresponding data set, to reduce the calculation burden required for the overall optimization of hybrid expert network, to improve the service throughput of entire network.The data set corresponding to the subnet used frequently is used to carry out quantization processing on the subnet, i.e.to avoid quantization processing on the entire network, simple and efficient, improve the performance of entire network.

The invention discloses an electronic terminal transfer function test system and method based on analog-to-digital conversion. Relates to the technical field of electrical testing and automatic control, in particular to an electronic terminal transfer function testing system and method based on analog-to-digital conversion. Input and output signals of a controlled object are synchronously acquired and processed, and high-precision extraction of amplitude and phase is realized by combining sine frequency sweep excitation with parameter fitting calculation, so that frequency response characteristics of a system in a target frequency band are obtained. The system comprises a main control board, an upper computer and a controlled object, one end of the main control board is connected with the upper computer, and the other end is connected with a controlled object; the upper computer is used for generating a test instruction and sending the test instruction to the main control board; the main control board is used for synchronously acquiring a test excitation signal output by the main control board and a response signal output by a controlled object; and the controlled object receives the test excitation signal output by the main control board and outputs a response signal. And the upper computer generates frequency response characteristics according to the synchronously acquired signals.

The application provides a large model-based ontology modeling and dynamic evolution method and system, which comprises the following steps: constructing an ontology hierarchical structure of a target field; receiving a multi-source heterogeneous business data stream in real time, preprocessing the received data, performing entity alignment and standardization on the preprocessed data, and dividing the data into data partitions corresponding to the ontology hierarchical structure; performing multi-round extraction and supplement of ontology knowledge on the distributed business data of each level to generate a candidate knowledge set to be inserted into the ontology; performing multi-dimensional conflict detection on the candidate knowledge set and the current ontology to retain legal candidate knowledge; calculating an ontology update prediction impact value, and if the ontology update prediction impact value is less than a hierarchical safety threshold, inserting the candidate knowledge. The application reduces labor costs, improves ontology modeling efficiency and architecture standardization degree, improves knowledge mining accuracy and reasoning ability, eliminates semantic ambiguity and logical errors generated by ontology iteration and evolution, and the ontology evolution whole process is traceable.

The invention relates to the technical field of virtual power plants, in particular to a virtual power plant energy scheduling method and system based on distributed optimization, and the method comprises the steps: constructing a distributed energy node state sensing network, collecting the operation state data and load prediction data of each distributed energy unit in real time, and transmitting the data to a distributed optimization scheduling center; based on an improved distributed model predictive control algorithm, establishing a multi-target energy scheduling optimization model, and decomposing a global optimization problem into local sub-problems of each node; parallel iterative solution of each sub-problem is realized through a distributed collaborative solution mechanism, and a scheduling scheme is dynamically corrected; and based on the real-time operation deviation and the external environment change, adaptively adjusting a scheduling strategy and issuing the scheduling strategy to each energy unit for execution. According to the invention, distributed energy resources are integrated through a distributed optimization architecture, efficient collaborative scheduling of virtual power plant energy is realized, the energy utilization rate is improved, the operation cost is reduced, and the system operation stability and flexibility are enhanced.

The application discloses an interactive method and device, a terminal device and a computer readable storage medium. The method comprises: acquiring video stream data played by a user; identifying the video stream data and extracting a key scene; when the key scene is not a predetermined scene, sending scene video data corresponding to the key scene to a cloud end, and interacting with the user based on first interactive content data returned by the cloud end, wherein the cloud end is used to generate the first interactive content data based on the scene video data. The method of the application can reduce the computing pressure of the client through the functional division and collaborative cooperation of the client and the cloud end, improve the accuracy of scene identification and the relevance of interactive content, realize efficient and intelligent interaction in a complex video playing scene, and improve the user experience.

The application provides a front-end reasoning deployment method and system based on a TensorFlow lightweight model, comprising: in a client, creating a background thread independent of a main thread; in the background thread, performing a model loading step, the model loading step comprising: judging whether a new version of a deep learning model needs to be obtained from a server through a local cache verification mechanism, and obtaining or using the deep learning model cached locally according to the judgment result; the main thread and the background thread interact through an asynchronous communication mechanism to perform reasoning calculation based on the deep learning model in the background thread, and send the reasoning result from the background thread to the main thread. The application realizes low-delay real-time reasoning: the reasoning process is completely completed in the local client of the user, and data does not need to be uploaded to the server, eliminating the delay caused by network transmission and greatly improving the real-time performance of artificial intelligence functions.

The present application relates to the technical field of pedal data processing, and particularly relates to a method and system for replacing pedal data in a playing file. The method comprises: obtaining a playing file of a piece of music; obtaining a score file corresponding to the piece of music; generating an insertion node according to the score file and a pedal insertion rule, and / or according to the playing file, wherein the pedal insertion rule comprises a chord rule; selecting replacement data of a pedal in response to a selection signal of a user, wherein the selection signal comprises a specified style or a specified pedal file; and inserting the replacement data into the insertion node to form a new playing file. The present application can meet the demand of users who want to reference pedal data of others by replacing pedal data, and greatly improves the efficiency of music production, teaching assistance, performance analysis and the like.

The application particularly relates to a kind of distributed radiation source warning equipment angle measurement fusion processing methods, comprising: obtaining the interrupt number sent by each distributed warning equipment, alarm report;According to the time information, radiation source detection data are screened to alarm report, to obtain the multiple alarm reports belonging to the same radiation source, and for direction finding fusion processing;According to the signal amplitude parameter of radiation source in alarm report, the multiple alarm reports belonging to the same radiation source are screened, to obtain first alarm report, second alarm report;According to first alarm report, second alarm target report determines the position relationship of corresponding distributed warning equipment, and based on position relationship, the alarm direction of radiation source is direction finding fusion processing to determine the target direction information of radiation source.This method divides angle measurement algorithm into two steps, reduces the distributed warning pressure, also reduces fusion processing pressure.

This invention relates to the field of image recognition and discloses an image segmentation method for dynamic backgrounds, comprising: acquiring an image sequence containing a target frame and historical frames; extracting feature vectors corresponding to each pixel position to form a spatial feature tensor; calculating the second-order temporal variance of the feature vectors within a sliding time window to construct a spatiotemporal divergence matrix characterizing the intensity of fluctuations; using the global median of the spatiotemporal divergence matrix to correct the discrimination threshold and determine a feature decay weight matrix whose weight values ​​are negatively correlated with the second-order temporal variance; multiplying the weight matrix with the target frame at the pixel level to suppress the feature activation amplitude in high-frequency oscillation regions; and outputting a foreground segmentation mask using the processed feature tensor. This invention utilizes the statistical properties of feature fluctuations to decouple the physical quantities of foreground displacement and background oscillations, effectively blocking the transmission of non-rigid background noise and improving the segmentation sharpness and recognition robustness of object boundaries in complex environments.

The application provides a robot welding path planning and obstacle avoidance method and system for complex workpieces, which comprises the following steps: extracting all welds from the workpiece, clustering the welds to obtain a weld grouping result; sorting the welds between each weld cluster and sorting the welds in each weld cluster to generate a welding path for each weld cluster; generating a welding sequence that meets process constraints; generating a safe transition path between the initial position of the welding gun and the first welding point of the welding sequence, between adjacent welds in the welding sequence, and between the last welding point of the welding sequence and the reset position of the welding gun, to obtain a complete welding path; and searching for an optimal welding path in the welding path in the minimum welding path comprehensive cost as the target. G The application solves the welding path planning problem under the combined action of multiple factors such as obstacles, differences in weld position distribution, and multiple process constraints. G The application solves the welding path planning problem under the combined action of multiple factors such as obstacles, differences in weld position distribution, and multiple process constraints.

The utility model belongs to 3D camera technical field discloses an integrated edge calculation function's intelligent 3D camera, including 3D camera module, embedded system and communication module, embedded system integrates CPU, GPU and NPU's processor, wherein CPU is responsible for scheduling, data acquisition and transmission, GPU is responsible for point cloud generation, NPU is responsible for edge calculation, through the collection of three -dimensional data inside the camera, point cloud generation and edge calculation, only the result transmission to external device after processing, significantly reduce data transmission and the processing burden of external device. The utility model compact structure, powerful, has provided efficient edge calculation solution for industrial field, can promote system efficiency and response speed, be applicable to the defect detection, size measurement, robot navigation etc. Variety of scene, have remarkable practical value and popularization prospect.

The application relates to the technical field of file storage management, and discloses a file storage management system of a cloud storage system, which comprises an access adaptation module, a metadata module, a slice version module, a deterministic mapping module, a container management module and a persistence control module. The deterministic mapping module performs stateless calculation based on a file identifier and a data block index number to generate a target container identifier and a virtual slot number. The container management module constructs a buffer container in the memory, ignores the discreteness of the virtual slot, and writes data blocks into a data storage area in a compact appending mode. The persistence control module drives effective data to be written into a disk according to a comparison result of a global version identifier and a historical version. The application eliminates the metadata bottleneck through deterministic routing, converts random I / O into sequential I / O through memory compact reorganization, and solves concurrent conflicts based on a lock-free mechanism, thereby significantly improving the throughput and expansibility of the storage system.

The present application belongs to the technical field of video big data intelligent analysis and application, in particular to a multi-channel video big data real-time analysis method and system based on cloud edge-end cooperation, the method comprising: acquiring multi-channel video stream of a rail train; based on an edge computing device, automatically decoding the multi-channel video stream, after target detection and classification of the decoded multi-channel video stream, encapsulating the target detection result and video frame data into a json file, and sending the json file as a message to a kafka server in real time; after the kafka server processes the json file sent by the edge computing device, sending the json file to a customer terminal for display. Based on the secondary development of Gsteamer and Deepstream, the present application reasonably encodes and decodes video stream big data, and the present application summarizes the collected multi-channel video to the edge computing device for one-time target detection, thereby improving the real-time performance of video data processing under the condition of reducing the computing pressure of the cloud service center.

The invention discloses a new energy battery recovery system and method based on the Internet of Things. The system comprises a data acquisition layer, an edge calculation layer and a cloud platform layer. The data acquisition layer acquires key parameters of a battery in a running state, a charging state and a standing state in real time through an integrated embedded sensor, and uploads the key parameters to the edge calculation layer and the cloud platform through the Internet of Things; the edge calculation layer adopts a dynamic self-adaptive strategy, filters noise in combination with a TS-SFEA algorithm, analyzes voltage-temperature collaborative change by a Pearson's correlation coefficient, constructs a battery health state parameter matrix, and inputs a pre-training model to predict residual life and health level; the cloud platform layer integrates production and use historical data, records full life cycle information of the battery through a block chain technology, and generates an echelon utilization score and a recovery value index based on an AI evaluation model; and when the cycle count is greater than or equal to 500 times, the system triggers a health state reestimation mechanism, and dynamically updates an OCV-SOC mapping table and a parameter matrix to ensure the timeliness of an evaluation result.

This application discloses a method, apparatus, and electronic device for fine-tuning a large model based on hybrid experts, belonging to the field of artificial intelligence technology. It addresses the problem that current large model technologies struggle to meet the application needs of multi-task scenarios in network management. The method includes: initializing a hybrid expert network for a pre-trained large model, the hybrid expert network comprising a task-level gating network and multiple task-level expert groups (each group containing a character-level gating network and multiple character-level experts), the hybrid expert network being embedded in specific layers of the pre-trained large model; inputting training data and a first prompt word into the pre-trained large model, guiding the pre-trained large model to output sample prediction results through the first prompt word; determining the loss result of the pre-trained large model based on the true label results corresponding to the training data, the sample prediction results, and the target loss function; and fine-tuning the network parameters of specific layers of the pre-trained large model based on the loss result to obtain a network management model for multi-task scenarios.

This invention relates to a training method, system, and readable storage medium for streaming image semantic segmentation models, belonging to the field of image segmentation technology. The training method includes dividing a streaming dataset into multiple data subsets based on a streaming data partitioning strategy; training multiple image semantic segmentation models based on each data subset; wherein each data subset corresponds one-to-one with an image semantic segmentation model, and each image semantic segmentation model has a identifiable category used to identify the data category of the corresponding streaming data; integrating the multiple image semantic segmentation models into a final segmentation model based on the identifiable categories of the image semantic segmentation models and their dynamic voting weights; the final segmentation model identifies the data category of the input streaming data and performs semantic segmentation according to the dynamic voting weights of the image semantic segmentation models. This invention can improve the segmentation performance of streaming image semantic segmentation models using minimal storage and training resources.

The application provides a kind of fusion kinetics aircraft GNC system visual modeling method, it is related to aircraft guidance control system modeling simulation technical field.The method divides aircraft GNC system model into dynamic model, kinematic model, environment model, rudder system model, seeker model, autopilot, target motion model, 3-DOF visual motion simulation model and guidance computer, models each model above and constitutes aircraft GNC system model, is used to solve trajectory when missile flies, required overload and actual overload, seeker tracking curve, instantaneous miss distance, flight attack angle, flight Mach number, rudder deflection angle parameter.The method of the application introduces visual modeling simulation and dynamic data table, considers the coupling influence between each subsystem and related system in GNC system explicitly, is convenient for the simplified modeling and simulation of different configurations guided aircraft, so that aircraft can obtain better flight performance and guidance precision.