267 results about "Information support" patented technology

The invention discloses a network security situation awareness and analysis platform based on AI, and relates to the technical field of network security situation awareness and analysis, and the platform comprises a multi-source data collection module which integrates flow, logs, assets and threat intelligence data, and carries out encryption transmission and standardization; the data preprocessing module purifies and optimizes data, and guarantees data quality and sensitive information security; the AI situation awareness analysis module extracts features through a deep learning model, dynamically evaluates the situation and identifies threats; the threat early warning and decision-making module triggers graded early warning and generates a targeted emergency response scheme; the visual display and interaction module displays information in multiple dimensions and supports query and report generation; and the data storage and tracing module adopts a mixed storage architecture, so that the data security and traceability are ensured. The platform integrates multi-source data and realizes situation accurate perception and intelligent decision by means of an AI technology; the early warning is accurate, the visual interaction is convenient, and the intelligent and efficient level of network security protection is comprehensively improved.

The invention discloses an intelligent fire-fighting AI collaborative early warning system and method fusing GIS and multi-source data, and relates to the technical field of fire-fighting safety, and the system comprises a GIS space modeling module which is used for constructing a digital twinborn model through integrating a building BIM model, fire-fighting facility distribution, a population density thermodynamic diagram, sensing equipment layout and real-time environment data; the multi-source data fusion module is used for integrating sensing equipment data, real-time environment data, meteorological information, manually reported hidden dangers and a historical fire case library, and outputting the data after standardization processing; the AI collaborative early warning engine module is used for accessing standardized data, realizing building fire dynamic risk assessment and outputting graded warning information; the 3D simulation drilling platform is used for calling the digital twinborn model, receiving graded alarm information and supporting fire spreading simulation, emergency drilling and plan optimization; and the block chain evidence storage module is used for storing alarm information and drill records. According to the invention, risk assessment and accurate prediction of the fire hazard can be realized.

The invention discloses a behavior recognition method and device based on dynamic context, equipment and a medium, and the method comprises the steps: obtaining a dynamic context graph through cross-modal modeling among multi-modal data, fusing long / short-term events in a monitoring video through dynamic upper and lower graphs, carrying out the cross-modal semantic connection, carrying out the multi-modal feature fusion, and carrying out the multi-modal feature fusion. Determining a gating strategy vector and a gating weight matrix according to a splicing result; and performing multi-modal fusion according to the gating strategy vector, the gating weight matrix and the multi-modal sequence feature to obtain a multi-modal fusion feature, and performing abnormal behavior identification on the monitoring video based on the multi-modal fusion feature. Richer information support is provided for decision making through feature fusion, so that the decision making accuracy in a complex scene is improved. The method can be applied to security and protection monitoring scenes in the financial field or the medical field, so that the abnormal behavior recognition accuracy in the security and protection monitoring scenes is improved.

The invention discloses an intelligent agent system and a control method thereof, and relates to the technical field of natural language processing. The system comprises an execution layer, a planning layer and an auditing layer, the planning layer identifies and extracts innovation task meta-information, constructs a task tree to complete operations such as hierarchical clustering, and determines a task execution path; the execution layer calculates the semantic similarity between the content of the knowledge graph and the user innovation question, determines the optimal question and answer, extracts heuristic information, and decomposes the heuristic information into sub-questions to construct a dependency graph; generating an answer set based on the graph solving sub-problems, and integrating answers by using a genetic algorithm to obtain an overall solution; and the auditing layer performs multi-dimensional scoring on the scheme, and determines a structured scheme report and optimization suggestions for users to use according to a scoring result. The method has the capabilities of structured reasoning, problem recursive decomposition and scheme closed-loop optimization, can generate a multi-dimensional evaluation result, and efficiently realizes systematic modeling of a clear path for analogy heuristic information support problem solution.

The invention provides a tunnel three-dimensional geologic body modeling method and system based on in-hole camera shooting and while-drilling lithology perception, and relates to the technical field of tunnel and underground engineering and intelligent geological information processing.The method comprises the steps that firstly, a drill hole wall optical image sequence and while-drilling engineering parameters are synchronously collected and preprocessed respectively; then establishing a depth-time mapping relation to realize accurate alignment of the multi-source data in a depth domain; then fracture intelligent identification and parameter extraction are realized through an improved U-Net network, and lithology classification is completed by using an XGBoost model; feature level and decision level fusion is carried out on the fracture features and the lithologic features, and rock mass quality grades are generated; and finally, constructing an integrated geologic model fusing lithologic distribution and a three-dimensional fracture network by adopting an implicit modeling and discrete fracture network technology. According to the method, the problem of splitting of in-hole visual information and while-drilling physical information is solved, automation and precision of geological recognition are improved, and reliable geological information support is provided for tunnel construction safety and support design.

The invention relates to the technical field of blank visual inspection, in particular to a blank defect visual inspection method and system for a plastic extruder, and the method comprises the steps: carrying out the region segmentation of a real-time image, extracting a first-stage defect region, completing the preprocessing, and selecting a specific analysis path according to the defect type. Extracting multi-level defect features and forming a structured feature vector; the reliability degree of each feature is judged through feature integrity judgment and feature quality evaluation, real defects are recognized by adopting multi-stage judgment logic, defect type classification is carried out, and quality influence level multi-stage judgment logic and a branch processing strategy are given, so that accurate detection, classification and analysis of the defects of the plastic extrusion blank are realized. The detection accuracy and adaptability are remarkably improved, the optimal processing path can be automatically selected according to different production conditions and defect types, meanwhile, potential systematic problems are identified through defect correlation analysis, and comprehensive and in-depth information support is provided for quality control.

The invention discloses a distributed photovoltaic optimization coordination control system and method, and relates to the technical field of distributed photovoltaic power generation, and the control system comprises a multi-dimensional data collection module, a self-adaptive cluster division module, a robust dominant node election module, a three-dimensional voltage state evaluation module, and a hierarchical coordination control execution module. According to the distributed photovoltaic optimization coordination control system and method, real-time operation data, meteorological prediction data and power grid load prediction data of each node are continuously monitored, comprehensive information support is provided for a control strategy, and when it is detected that voltage abnormity is caused by sudden change of illumination intensity in a certain area, the real-time operation data of each node, meteorological prediction data and power grid load prediction data are monitored. And immediately finishing cluster structure adjustment in an extremely short time by adopting a fuzzy clustering algorithm with constraint conditions based on the electrical distance and the voltage fluctuation trend characteristic value, and ensuring that the node voltage change characteristics in the same cluster are highly consistent.

The invention relates to the technical field of ship energy consumption prediction, and discloses a ship energy consumption interval prediction method and system based on a Gaussian quantile regression model.The method comprises the steps that firstly, a ship navigation historical data set is preprocessed, a model input feature set is screened, a Gaussian process quantile regression model with a radial basis function as a kernel is constructed, and hyper-parameters are optimized; and after the prediction performance of the subset evaluation point is verified through the model, an upper quantile prediction model and a lower quantile prediction model are respectively established according to a target confidence level, and finally a prediction interval is synchronously output and an evaluation report is generated. According to the method, through combination of Gaussian process processing nonlinear relation and quantile regression estimation condition distribution, high-precision point prediction is provided, meanwhile, prediction uncertainty can be quantized, an energy consumption prediction interval corresponding to a target confidence level is output, and more comprehensive and reliable information support is provided for ship energy efficiency management and operation decision making.

The invention relates to a recording method and system of an electronic medical record, and aims to comprehensively and systematically collect health information of a patient through diversified input terminals, improve information input efficiency, ensure information integrity and promote team cooperation. The system comprises a medical professional terminal, a patient direct related terminal and other patient related terminals, supports information input in various forms of pictures, characters, videos, audios and the like, and covers various input devices such as portable devices, professional examination instruments and the like. And the information storage module ensures accurate homing of the data and supports cross-mechanism data integration and sharing. The system also has the functions of electronic medical record automatic generation, treatment tracking, symptom and sign extraction, intelligent analysis and early warning and the like, provides efficient and accurate information support for medical services, optimizes medical resource configuration, and enhances medical experience of patients. The method is suitable for modern medical management, and particularly has remarkable advantages in the aspect of improving the intelligence and integration level of an electronic medical record system.

The invention discloses a power distribution network area low voltage prediction method, and relates to the technical field of voltage prediction, and the method comprises the steps: S1, data preprocessing; s2, establishing a GA-BP neural network load prediction model; s3, GA-BP load prediction model simulation is carried out; s4, establishing an LSTM neural network voltage prediction model; s5, performing model simulation and result analysis; and S6, voltage prediction and treatment suggestion. According to the power distribution area low voltage prediction method, firstly, a GA-BP neural network is utilized to predict area loads, influence factors such as temperature, humidity, date types and different moments in one day are fully considered, and predicted load data are utilized as input to construct an LSTM model to predict area voltages; the transformer area low voltage is effectively predicted through the GA-BP-LSTM combined model, the model is high in fitting degree and low in prediction error, the low voltage degree of a user is reflected in real time, and more information support is provided for follow-up transformer area low voltage treatment.

The invention discloses a bank data analysis processing method and system, particularly relates to the technical field of bank data processing, and is used for solving the problem that multi-source data is difficult to trace uniformly. Multi-service system records are accessed through a unified time reference and a subject identifier, scattered records are recombined into event frames with event types, time and subject sets based on event anchor point rules, conflict resolution is performed on multi-source same-name fields according to authoritative source priorities and delay propagation thresholds, main field values and alternative field values are reserved, and multi-source same-name fields are obtained. On this basis, an event frame table, a state sequence table and a participating entity table are constructed, indexes are established according to subject identifiers, time intervals, event types and state codes, retrieval and statistics around customer events are achieved, data consanguinity among source fields, event frame fields and analysis fields is synchronously established when the analysis fields are generated, and the data consanguinity among the source fields, the event frame fields and the analysis fields is established; and in combination with the version information, tracing of index sources in a rule change and late data scene is supported.

The invention discloses an AI-based logistics document intelligent identification and filling system, and the system comprises an image collection module which is used for obtaining the image information of a logistics document, supporting a plurality of image input modes, such as scanner, mobile phone photographing, camera capturing and the like, carrying out the preprocessing of a collected image, including the operation of image enhancement, denoising, graying, binaryzation and the like, and obtaining the image information of the logistics document; the image quality is improved, and preparation is made for follow-up recognition; the document type identification module is used for carrying out document type judgment on the preprocessed document image based on a convolutional neural network (CNN) algorithm in deep learning, and can automatically identify common logistics document types; the invention relates to the technical field of logistics information, and the AI-based logistics document intelligent identification and filling system can efficiently and accurately identify various logistics documents, realize intelligent filling and improve the automation level and accuracy of logistics document processing.

The invention discloses a geographic information element analysis method and system based on a multi-modal large model. According to the method, the flexibility and pertinence of the analysis process are remarkably improved through a dynamic self-adaptive multi-modal fusion mechanism. Through a task sensing module and real-time contribution degree calculation, key data types in different scenes can be automatically identified, the dynamic adjustment enables an analysis process to better meet actual requirements, interference of irrelevant data is avoided, the capture precision of a target geographic phenomenon is directly enhanced, and the accuracy of target geographic phenomenon capture is improved. And the comprehensiveness and the reliability of an analysis result are enhanced through deep collaborative fusion of multi-modal data. Various types of data are complemented and cooperated through unified feature space mapping and dual-drive weight adjustment, not only are multi-dimensional features of geographic information covered, but also limitation of a single data source is reduced through real-time verification and iterative optimization, and a finally output analysis result is closer to a real geographic environment. And a more solid information support is provided for subsequent decision making.

The invention discloses a vehicle data transmission method and device, electronic equipment and a storage medium. The method is applied to a data sending end, and comprises the following steps: in response to a received data transmission instruction, determining to-be-transmitted data corresponding to the data transmission instruction and a data receiving end; determining a target compression algorithm based on target compression configuration information and the multi-dimensional decision information, the target compression configuration information being used for representing compression configuration information jointly supported by the data sending end and the data receiving end, the multi-dimensional decision information at least comprises two of the following: a network resource parameter, a computing resource parameter and a data attribute parameter of the to-be-transmitted data; compressing the to-be-transmitted data by using the target compression algorithm to obtain processed data; and transmitting the processed data to a data receiving end. According to the invention, the technical problem of low transmission efficiency and adaptability of vehicle data transmission in the prior art is solved.

The invention relates to the technical field of natural language processing, in particular to a verifiable large model retrieval enhancement generation system and method based on an evidence chain, and the method comprises the steps: receiving an initial query, recognizing the fuzziness and information gap of the initial query in combination with an associated retrieval document, and generating a supplementary query set; based on the initial query, the supplementary query and the corresponding retrieval document, generating candidate answers with references and verifying the information supportability of the candidate answers; for the candidate answers passing the verification, extracting support information and constructing a hierarchical attribution mapping relation; and integrating the information to form to-be-evaluated information, and if the current verified to-be-evaluated information meets a preset sufficiency condition, integrating the generated preliminary answer and the to-be-evaluated information to synthesize a target answer. The method effectively overcomes the defects that a traditional RAG system is fragmented in information integration, has one-sided fuzzy query and answer, is low in attribution efficiency and excessively depends on retrieval content, and has the advantages of answer comprehensiveness, verifiability and deployment lightweighting.

The invention relates to a monitoring and early warning system and method for a working rope crossing a blocking net, and belongs to the technical field of rope state monitoring, and the system comprises a data collection and transmission module which is used for collecting multi-source state data of acoustic emission, tension and the like of the working rope and forming a structured data stream; the rope state intelligent analysis module is used for carrying out AE signal depth analysis and multi-modal feature fusion risk assessment based on the data stream, and constructing and updating a rope digital twinborn model; the damage feature and risk knowledge base is used for storing reference information to support intelligent analysis; the early warning decision and response module is used for performing graded early warning and residual service life (RUL) prediction according to the analysis result and knowledge base information and providing maintenance suggestions; the system optimization and self-learning module is used for coordinating system work and self-learning an optimization model and a knowledge base based on operation data; accurate monitoring, early damage identification, intelligent risk assessment and timely early warning of the state of the working rope can be realized, and the use safety and reliability are remarkably improved.

The invention relates to the technical field of target positioning, in particular to a cross-view object-level geographic positioning method and system based on heterogeneous space contrast loss, and the method comprises the steps: inputting a query image and a reference image into a geographic positioning model, and obtaining the position of a target object in the reference image in the query image through the geographic positioning model; according to the geographic positioning model, a double-branch pseudo-twin network is adopted to extract global features of an input query image and local features of a reference image, fusion features are focused on a local area of a target object in feature fusion through space attention, and the position of the target object on the reference image is output by generating a prediction frame. And the geographic positioning model adopts a heterogeneous space contrast loss constraint model training process. According to the invention, the method achieves the precise positioning of the position of the target of interest through combining with the fine-grained features in the image, effectively improves the cross-view object-level geographic positioning precision, and provides technical support and position information support for the application fields of urban planning monitoring, emergency rescue scheduling and the like.

The invention discloses an MOT anomaly detection method and system based on multi-source heterogeneous data, and relates to the technical field of mobile operation terminal anomaly detection, and the method comprises the steps: enabling MOT equipment to report time series data and log data through a distributed cooperation mechanism, carrying out the missing value filling and standardization preprocessing of the time series data, and carrying out the detection of the log data; processing the log data, performing time dimension alignment fusion on the preprocessed time sequence data and the converted event sequence data to form a unified multivariable time sequence, dynamically determining the size of a sliding window based on the meta-feature of the fused time sequence, dividing a data capture time dependency relationship according to the window, and obtaining the time sequence of the sliding window. And inputting the fused data in the window into an anomaly detection model for state judgment. The method improves the precision and efficiency of anomaly detection, has high practical value and popularization potential, can achieve efficient and reliable monitoring and fault positioning in a complex industrial scene, and provides timely and accurate information support for managers.

Systems, methods, apparatuses, and computer program products for enhanced location services in 5G are provided. One method may include storing, at a network node, at least one of data network access identifier(s) (DNAIs), an indication of serving area(s), and / or slice information that is supported by a location management function (LMF) with or in a LMF profile. The method may also include transmitting, to a consumer network function (NF), the LMF profile with the at least one of DNAIs, indication of serving area(s), or slice information supported by the LMF.

The invention relates to a multi-modal active safety margin evaluation and checking method and device and a storage medium, which are applied to the technical field of electric power spot market, and the method comprises the steps: constructing a state judgment-index self-adaption evaluation framework, and when a system is in a risk state, calculating an interval overlapping index to quantify the risk exposure degree; when the system is in a safe state, a safe distance index is calculated to quantify safe redundancy, the multi-modal index system overcomes the defect that information is unilaterally evaluated by a traditional single index, refined and differentiated evaluation on the degree of risk and the degree of safe redundancy is achieved, and more comprehensive information support is provided for scheduling decision making; by comparing the calculated index with the preset threshold value, an active checking signal which can be directly fed back to a market clearing model is generated, or a clear suggestion is provided for a dispatcher, so that the problem of disjunction between evaluation and decision in the prior art is solved, and integrated intelligent cooperation from state perception, quantitative evaluation to closed-loop correction is realized.

The invention discloses a real-time obstacle sensing method for a sonar image, and relates to the technical field of sonar image processing, and the method comprises the following steps: collecting a time sequence sonar image flow of a target water area, carrying out the inter-frame alignment processing of the time sequence sonar image flow, and screening out an effective sonar image with a signal-to-noise ratio meeting a preset threshold value; a suspected obstacle area in the effective sonar image is extracted, and morphological features, gray level distribution features and texture features of the suspected obstacle area are obtained to form a suspected feature database; and performing similarity matching on standard features in the suspected feature database and the obstacle feature database to obtain a feature matching degree, so as to intuitively present the position, the type and the movement track of the obstacle by constructing an obstacle real-time distribution map under a three-dimensional coordinate system, and integrate a safety distance early warning mechanism, thereby improving the safety of the obstacle. And timely and comprehensive obstacle information support can be provided for underwater operation or navigation, and the operation safety and efficiency are ensured.

The invention is suitable for the technical field of image restoration, and provides a space controllable generation guided unified image restoration method and model, and the method comprises the following steps: S1, image extraction: employing a pre-training model to extract a depth map and text description from a degraded image, the depth map being extracted through a pre-training depth map extraction model Depth-Anything-V2, and the text description being extracted through a pre-training depth map extraction model Depth-Anything-V2; the text description is extracted through a pre-training text description model CogVLM2, and the depth map is used for controlling the overall space of the reference map to be consistent with the degradation map. According to the unified image restoration method and model guided by spatial controllable generation, the spatial controllable reference generation model guided by a multi-granularity condition is introduced by improving the quality of a reference image and improving the restoration controllability, and the model can generate an intermediate reference image which is consistent in spatial structure and does not contain degradation on the basis of multi-granularity condition input; information support is provided for final restoration, degradation information can be effectively filtered out, and high-quality generation of the reference image is achieved.

The invention provides a social media data-driven urban ponding depth inversion and precise positioning method. The method comprises the following steps of: 1, capturing and preprocessing multi-source social media data; step 2, performing ponding depth inversion based on image recognition; 3, accurately positioning the ponding position based on the text and the large model; extracting character information in pictures and videos by means of a picture character recognition technology in a large model, recognizing typical buildings therein, and assisting in positioning a flood position; screening out effective place information and time data related to ponding by adopting a large model; the identified location information is converted into specific location coordinates and detailed addresses by combining a geocoding technology, so that high-precision positioning is realized; and 4, outputting a comprehensive result: generating structured disaster information, and supporting direct calling of an emergency platform. Through combination of social media data and a space-time distribution model, ponding depth inversion is realized, quantitative data of the ponding depth is acquired, a disaster site is rapidly positioned, and decision making and flood control and disaster relief work are supported.

According to the multi-source retrieval and agent collaborative decision-making medical question-answering system and method, a three-layer retrieval framework is constructed, a local medical knowledge base vector retriever, a PubMed intelligent retriever and an academic deep crawling device are integrated, dynamic fusion of multi-source heterogeneous medical information is achieved, and the medical question-answering efficiency is improved. Through the synergistic effect of the three, the information coverage range is remarkably expanded, more comprehensive and multi-dimensional evidence support is provided for various medical problems, the retrieval efficiency and accuracy of a professional database are effectively improved, and the use threshold of non-professional users is reduced. Meanwhile, the technical scheme of the invention supports dual-scene task processing, can meet diversified medical information requirements, provides reliable reference for diagnosis and treatment decisions of medical staff, also provides structured and high-quality information support for subject investigation of medical researchers, and powerfully promotes intelligence and precision of medical information processing.

The invention provides a cellular data low-frequency trend self-adaptive extraction method, which is used for automatically extracting an evolution trend from nonlinear and strong-fluctuation cellular flow data. According to the method, firstly, missing values in an original sequence are restored through Akima spline interpolation, and continuity and smoothness of a data structure are restored; and then constructing a joint objective function to minimize the permutation entropy of the candidate trend and maximize the Pearson correlation coefficient of the candidate trend and the original sequence, and then guiding the adaptive confirmation of the order and modal quantity parameters of a B spline in TVFEMD decomposition, thereby extracting a low-frequency trend component with the highest representation force, and realizing unsupervised and automatic trend extraction. Finally, the provided method can perform adaptive modeling under various time scales such as day, week, month, year and the like, outputs trend components of corresponding time scales, and can be applied to application scenes such as capacity planning, holiday and festival load analysis, energy-saving scheduling and service prediction in a cellular network. The method has the advantages of generalization, full-automatic parameter adjustment and the like, the trend extraction efficiency and stability of the cellular data are remarkably improved, and reliable trend information support is provided for intelligent operation and maintenance and decision optimization of the cellular data.

We support crisis management responses so that organizations can take appropriate actions when unforeseen circumstances occur. [Solution] The crisis management public relations system has one or more processors, which receive events related to the organization's crisis and, in response to the event, generate crisis management information regarding the actions the organization should take using AI. The crisis management information may include information on the contacts of relevant parties who should be shared information about the crisis, information supporting the basis for that contact information, information that comprehensively outlines the actions the organization should take in response to the crisis, and information that encourages external reporting of the completion of the crisis response.

The present embodiment relates to a training data creating device including one or more processors that perform processing of generating training data for machine learning. The one or more processors plot a plurality of endoscopic images of a training device in the feature space based on individual features, determine classes for the endoscopic images, and set a set region for each of the determined classes. The one or more processor newly generate training data so that the number of pieces of training data newly generated in a first region which is an intersection region of the set regions for each class is greater than the number of pieces of training data newly generated in a second region which is a region excluding the first region from a union region of the set regions for each class.

The present disclosure provides a discovery method of a near field communication controller, an AMF, a system and a storage medium, wherein the method comprises: the AMF receiving DDNMF request information sent by a terminal, the information comprising at least one Prose application ID; the AMF sending DDNMF discovery request information to the NRF if it is determined that no target DDNMF information corresponding to the Prose application ID is stored; the AMF receiving DDNMF discovery response information returned by the NRF, the information comprising target DDNMF information supporting the Prose application ID; and the AMF returning the target DDNMF information to the terminal according to the DDNMF response information. The present disclosure solves the problem of the terminal determining a suitable DDNMF in a multi-DDNMF scenario, can enhance the networking expansion capability of near field communication, and improves the user experience.

The application discloses a kind of based on image coding technique's transformer area load component identification method, this method is first by analyzing the cluster characteristics of different types of load components, filters and determines the characteristic power curve of each component, and power curve is encoded into two-dimensional image using gram angle field method;Then, based on the measured data of a small number of users, using the minority class oversampling technique to generate the similar power curve of each load component, and then generate the training data set of load component identification model;On this basis, input load component image, use the model training using synthetic data set, identify the power ratio of each load component in transformer area by the model after training. The present application does not need meteorological, environmental and other external factors data, under the actual measurement of current user power data, effectively improve the identification accuracy of transformer area load component, provide accurate information support for load forecasting, demand response and other measures of distribution network.

A method and apparatus for transmitting automatic repeat request acknowledgement feedback information supporting multicast service, the method comprising: receiving N pieces of first downlink control information (DCI) and M pieces of second DCI in at least one first time unit, the first DCI being DCI of a first type, the second DCI being DCI of a second type, each first DCI comprising a first downlink allocation index (DAI), each second DCI comprising a second downlink allocation index (DAI) of a second type, the first DAI comprises a first downlink allocation index counter (C-DAI), and the first C-DAI indicates the cumulative number of the DCI of the first type transmitted by scheduling a downlink data channel; each piece of second DCI comprises a second downlink allocation index (DAI), the second DAI comprises a second downlink allocation index counter (C-DAI), and the second C-DAI indicates the cumulative number of the DCI of the second type transmitted by scheduling a downlink data channel; and receiving N first downlink data channels and M second downlink data channels in at least one second time unit, generating a codebook of automatic repeat request acknowledgement (HARQ-ACK) feedback information, and sending the codebook in a third time unit.