179 results about "Navigation model" patented technology

The invention discloses a digital twinning-based high-altitude falling risk state assessment technology for building workers and a mapping method and system. The method comprises the following steps: extracting aqueduct building engineering site space element information and personnel positions by an IFC model, and respectively inputting the information and the positions into an indoor navigation model and a three-dimensional digital model; historical risk data of an aqueduct construction engineering site are collected to analyze a risk assessment index and a preset risk threshold value; constructing a digital twinborn model according to the risk assessment index, the risk threshold value, the indoor navigation model and the three-dimensional digital model; real-time construction site environment data, worker behavior data and equipment state data are collected to update and simulate the digital twin model in real time; evaluating the risk range of the high-altitude falling risk by combining the updated and simulated results with a risk evaluation algorithm; and performing mapping association on the digital twinborn model through the construction site environment data, the worker behavior data and the risk range, performing visual interface presentation on the mapping association part, and designing a risk early warning trigger threshold.

A mapping server may include a processor configured to: receive drive information collected from vehicles that traversed a road segment, the drive information comprising an indication of a first speed-affecting landmark and a second speed-affecting landmark; determine a speed segment, as a subsection of the road segment, based on the first and second speed-affecting landmarks, wherein the drive information received from each of the vehicles further includes one or more indicators of vehicle speed within the speed segment; determine, based on the one or more indicators of vehicle speed, an aggregated common speed profile for the speed segment; store the aggregated common speed profile in an autonomous vehicle road navigation model associated with the speed segment; and distribute the autonomous vehicle road navigation model to one or more autonomous vehicles for use in navigating along the speed segment.

The invention provides a visual language navigation and model training method, device, equipment and medium, and the method comprises the steps: obtaining a training sample, processing a to-be-executed instruction and environment information in the training sample through a core model, obtaining a navigation feature corresponding to the to-be-executed instruction, and inputting the navigation feature into an action prediction head. Obtaining predicted action information corresponding to the navigation features, and inputting the navigation features into a thinking chain generation head to obtain predicted thinking chain information corresponding to the navigation features; and then, based on the predicted action information, the reference action information in the training sample, the predicted thinking chain information and the reference thinking chain information in the training sample, training the core model, the action prediction head and the thinking chain generation head so as to take the trained core model and the trained action prediction head as a visual language navigation model. According to the method, the reasoning efficiency and the reasoning accuracy can be improved.

The invention discloses a customs remote intelligent patrol system based on a 5G + unmanned aerial vehicle technology, and relates to the technical field of unmanned aerial vehicle patrol. An unmanned aerial vehicle module is responsible for performing field data acquisition and primary processing according to application scene requirements; the wireless communication terminal provides a high-speed and low-delay data transmission channel through a 5G communication network; the customs command center is responsible for gathering, analyzing, commanding and deciding the collected application scene data. The data analysis module is matched with a high-definition camera to enable collected remote sensing data to be further close to a real image, so that customs supervision work management can be more accurate and stable, an intelligent inspection route is continuously optimized by carrying a path navigation model based on an improved ant algorithm, and the intelligent inspection efficiency is improved. And the intelligent inspection efficiency is improved.

The invention discloses a voice navigation model training method and device, a navigation method and a vehicle-mounted navigation system. The method comprises the following steps: constructing a language database comprising a point-of-interest core word library, an easily confused word mapping list and a corrected sentence pattern template library for correcting navigation points of interest; calling a specified model to generate an annotation data set based on a pre-constructed cue word project and a language database; inputting the labeled data set into a pre-constructed navigation model for iterative training to obtain a target navigation model; the target navigation model is used for performing initial interest point recognition and initial interest point correction on a voice navigation instruction output by a user and determining a target interest point. Therefore, the core word library provides a point-of-interest vocabulary basis, and the easy-to-confuse word mapping list effectively solves the problem of recognition errors caused by non-standard pronunciation of a user and the like. Meanwhile, due to the introduction of the corrected sentence pattern template library, the interest points can be corrected, the navigation failure caused by the input error of the user is reduced, and the navigation precision is improved.

The invention provides a literature and blog exhibition hall virtuality and reality fusion guide system and method based on digital twinning, and belongs to the technical field of exhibition hall guide. Data extraction is performed on a literature and blog exhibition hall, literature and blog exhibition hall compound data is acquired and preprocessed, and a literature and blog exhibition hall compound data set is generated and subjected to analogue simulation; the method comprises the following steps: constructing a tourist sensory analysis model, a tourist behavior analysis model and a cultural relic state analysis model, constructing a cultural relic exhibition hall guide model based on the tourist sensory analysis model, the tourist behavior analysis model and the cultural relic state analysis model, performing model distillation to obtain a lightweight cultural relic exhibition hall guide model, and deploying the lightweight cultural relic exhibition hall guide model to each terminal of a cultural relic exhibition hall. A tourist guide decision is generated based on the light-weight cultural relic exhibition hall guide model, virtual-real fusion guide is performed on the cultural relic exhibition hall based on the tourist guide decision, a guide result is obtained, and the light-weight cultural relic exhibition hall guide model is optimized and adjusted according to the guide result. And a cultural relic exhibition hall guide decision giving consideration to cultural relic safety protection and visit experience of the tourists is provided for the tourists.

The invention relates to the field of data processing, and discloses a neurosurgery operation data processing method for precision medical treatment, which is used for solving the problem that mark points cannot be captured due to the fact that a navigation camera is shielded when intraoperative data is collected. The method comprises the following steps: acquiring preoperative image data, intra-operative navigation image data and spatial information of an intra-operative reference mark point, constructing a preoperative three-dimensional navigation model through the preoperative image data, obtaining a navigation coordinate system, obtaining an intra-operative image frame sequence through the intra-operative navigation image data, carrying out spatial registration, extracting intra-operative navigation information, and obtaining an intra-operative image frame sequence. The method comprises the following steps: acquiring intraoperative navigation information, judging whether occlusion occurs in an operation or not according to the intraoperative navigation information, if yes, generating missing image frame data during occlusion through a dynamic trajectory prediction model, and effectively judging the missing image frame data according to a newest instrument space pose, thereby effectively improving data integrity and coherence, improving operation data accuracy and improving operation accuracy. The operation risk is reduced.

A system for mapping road segment free spaces for use in autonomous vehicle navigation. The system includes at least one processor programmed to: receive from a first vehicle one or more location identifiers associated with a lateral region of free space adjacent to a road segment; update an autonomous vehicle road navigation model for the road segment to include a mapped representation of the lateral region of free space based on the received one or more location identifiers; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

The invention relates to the technical field of endoscopes, and provides a spectral image processing method for a three-dimensional endoscope. The method comprises the following steps: synchronously acquiring single-band spectral images returned by multiple sensors of the endoscope; after the single-band spectral image is corrected, a two-dimensional spectral image is generated by combining multi-level fusion of the spectral weight; constructing a three-dimensional point cloud coordinate set through parallax calculation; mapping the two-dimensional spectrum fusion image to a three-dimensional point cloud based on a back projection sub-pixel mapping algorithm to obtain a three-dimensional spectrum point cloud; and performing Poisson fusion driven inter-block splicing fusion on the three-dimensional spectrum point cloud, and outputting an interactive three-dimensional navigation model. The technical problem that image details are lost and three-dimensional reconstruction is inaccurate due to the fact that spectral image fusion precision is insufficient in an existing three-dimensional endoscope image processing method is solved, registration and reconstruction of high-precision three-dimensional spectral point clouds are achieved through multi-modal image collaborative correction and optimization fusion, and the image fusion precision is improved. And the accuracy and the real-time performance of navigation in the endoscope are improved.

The invention discloses a detectability quantitative model and a filtering method for an underwater multi-source autonomous navigation system, and belongs to an underwater robot integrated navigation and integrated positioning technology. The method comprises the following steps: firstly, respectively carrying out error modeling on SINS, DVL and USBL sensors based on an SINS / DVL / USBL integrated navigation model; establishing a multi-dimensional detectability quantitative model from four dimensions of coverage rate, accuracy rate, real-time rate and availability rate; and finally, based on the established detectability quantitative model, the detectability of the sensor is incorporated into an adaptive Kalman filtering algorithm so as to optimize the problem of computing resource allocation during data fusion. Compared with a traditional detectability method, the underwater environment interference is considered to establish a multi-dimensional detectability quantitative model, a corresponding algorithm is proposed to dynamically allocate computing resources, and the precision and robustness of the underwater vehicle multi-source autonomous navigation system are further improved.

The invention discloses a mobile robot autonomous search navigation method based on a visual language large model. The method comprises the steps of receiving a natural language instruction of a user, and synchronously collecting an RGB image and a laser point cloud; two-stage visual language reasoning is adopted, and target semantic analysis and existence judgment are completed in sequence; when the target does not exist, autonomous exploration is executed based on the point cloud data and is parallel to target judgment, and an exploration-judgment closed loop is formed; after determining that the target exists, switching to a visual language navigation model, generating an incremental motion instruction, and guiding the robot to approach the target; target state monitoring is continuously carried out during navigation, if the target is lost, a dynamic backtracking mechanism is triggered, the robot is driven to return to the historical state of the recently confirmed target, and navigation is restarted. The method does not need to depend on a prior map, can execute efficient and robust autonomous search and navigation tasks on edge equipment through task collaboration, model quantification and state backtracking, and is suitable for complex scenes such as industrial inspection and emergency rescue.

The invention discloses a ship navigation model deployment method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a plurality of feature ship sets, and obtaining the ship data of the feature ship sets; wherein each characteristic ship set comprises a plurality of ships with the same characteristics; training an initial navigation model based on the ship data of the plurality of feature ship sets to obtain a plurality of basic navigation models; in response to a received model request of a target ship, determining a basic navigation model matched with the target ship from the plurality of basic navigation models as a target basic navigation model according to the ship data of the target ship; and deploying the target basic navigation model to the target ship, so that the target ship trains the target basic navigation model based on the ship end data to obtain a target collaborative navigation model. The method can achieve the end-cloud cooperation of the autonomous navigation model of the ship, improves the performance and adaptability of the model, and provides powerful support for the safe and efficient navigation of the ship.

An SMES joint planning method considering carbon emission comprises the following steps: step 1, building a ship integrated energy system model based on a multi-energy coupling principle, the model comprising an AC / DC hybrid ship power system architecture model, a ship energy equipment model and a ship navigation model; 2, establishing a double-layer optimization planning model considering carbon emission by adopting the ship integrated energy system model obtained in the step 1, optimizing equipment configuration at the upper layer of the model by taking the minimum total investment cost as a target, and optimizing an equipment scheduling strategy at the lower layer by taking the operating cost and the carbon emission as double targets; and step 3, solving the double-layer optimization planning model considering the carbon emission obtained in the step 2 based on a mixed integer linear programming method, and realizing collaborative optimization of optimal capacity configuration and an operation strategy of the system through a CPLEX solver.

The invention discloses a low-altitude unmanned aerial vehicle autonomous cruise method and system based on cloud edge basic model collaboration, and the method comprises the following steps: S1, collecting an environment RGB image through an airborne monocular camera of an unmanned aerial vehicle, and carrying out the preprocessing of the image, and obtaining a preprocessed gray image; s2, a neural scheduler based on deep reinforcement learning generates a scheduling instruction according to the environment data and the network state reasoned by the navigation model at the previous moment; s3, generating a preliminary flight instruction; s4, generating an optimized flight instruction; s5, generating a structured flight instruction; and S6, the unmanned aerial vehicle executes the preliminary flight instruction or the optimized flight instruction or the structured flight instruction. According to the invention, through dynamic on-demand cooperation and intelligent scheduling of the end-edge-cloud three-level model, resource consumption and delay are substantially reduced, and high-robustness and high-safety autonomous cruise of the unmanned aerial vehicle in a complex open environment is realized.

Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a host vehicle includes at least one processor programmed to: receive, from a camera of the host vehicle, one or more images captured from an environment of the host vehicle; analyze the one or more images to detect an indicator of an intersection; determine, based on output received from at least one sensor of the host vehicle, a stopping location of the host vehicle relative to the detected intersection; analyze the one or more images to determine an indicator of whether one or more other vehicles are in front of the host vehicle; and send the stopping location of the host vehicle and the indicator of whether one or more other vehicles are in front of the host vehicle to a server for use in updating a road navigation model.

The invention discloses a cascade learning type multi-source fusion positioning method and system under satellite denial. The method comprises the following steps: firstly, constructing a system sensor mathematical model which comprises an INS error model, a WSS / INS integrated navigation model and a GPS / INS integrated navigation model; then designing a neural network model based on a cascade learning CL algorithm; when the satellite is available, training a neural network model based on a cascade learning CL algorithm; when the satellite is denied, the neural network model which is completely trained and is based on the cascade learning CL algorithm is utilized to predict the pseudo GPS position, and the GPS blank is filled up. The system comprises a sensor mathematical model building module, a CL algorithm model building module, a training module and a positioning prediction module. The method improves the positioning precision of the vehicle in the GPS failure period, is excellent in performance especially in a complex environment and under long-time interruption, reduces the positioning error, improves the overall performance, and has important application value for vehicle positioning in the technical fields of intelligent transportation and automatic driving.

The invention provides a power transmission line adaptive inspection method and system, and belongs to the technical field of power transmission inspection. According to the real-time image, a light-weight visual navigation model is adopted to determine and recognize a tower, and the unmanned aerial vehicle flies to the tower top of the tower in an imitated mode according to the tower shape features of the recognized tower; wherein in the simulated flight, a GPS and a visual SLAM are adopted to independently solve a pose, and an optimally estimated fusion height is output through extended Kalman filtering fusion; adopting an identification algorithm for the tower to obtain tower parameters; and determining a routing inspection waypoint plan according to the tower parameter matching database, and shooting a routing inspection image of the tower key equipment according to the routing inspection waypoint plan. According to the invention, algorithms of target identification, visual tracking, automatic obstacle avoidance, path correction, adaptive dimming, automatic focusing, multi-angle shooting and the like are integrated, and power transmission line inspection with low difficulty, high efficiency and accurate identification is realized.

The invention relates to an instruction type-independent visual language navigation method, which comprises the following steps of: 1, mixing various types of instructions to form a mixed instruction text # imgabs0 #, carrying out text coding on I, and classifying through an instruction discriminator to obtain a classification probability p < omega > as an expert routing basis in an expert network; step 2, a cross-modal hard branch routing expert network CrossMoE is constructed; and step 3, based on an LLM instruction specification, performing instruction specification on the mixed instruction text I by using a large language model LLM to obtain an instruction element set I ', and the like. The method disclosed by the invention has the superior technical effects that the processing capability of a system on various types of instructions can be effectively improved, and the defect that the performance of a traditional navigation model is reduced when the traditional navigation model processes the various types of instructions and faces complex and diversified instructions is overcome; complicated operation steps are avoided, man-machine interaction is more natural and smoother, and particularly, the use threshold of a non-technical user system is greatly reduced.

The invention discloses an auxiliary operation positioning system based on AI intelligent navigation, and the system comprises an image data collection module which is used for obtaining the original image data and operation planning information of an operation part of a patient; the navigation model construction module is used for constructing a three-dimensional navigation model based on the original image data and generating a target key point and a target path through a preset AI model based on the operation planning information; the positioning and tracking module is used for acquiring the position information of the surgical instrument in real time in the surgical process and mapping the position information into the three-dimensional navigation model; and the real-time positioning correction module is used for acquiring continuous image frames acquired by the surgical instrument in real time, acquiring intraoperative motion deviation through optical flow estimation based on real-time image data, and correcting the target key point and the target path based on the intraoperative motion deviation. According to the method, the intraoperative motion deviation is obtained in real time through optical flow estimation, target positioning is corrected, and the accuracy and reliability of operation positioning can be improved through the real-time dynamic correction mechanism.

The invention discloses an unmanned ship autonomous navigation method and decision making system based on laser radar real-time perception, and relates to the field of unmanned ship autonomous navigation.The unmanned ship autonomous navigation method comprises the steps that unmanned ship state information, terminal position information and two-dimensional laser radar data are collected, and position information of a terminal relative to an unmanned ship is calculated; normalizing the state information of the unmanned ship and the position information of the terminal point relative to the unmanned ship, and fusing with radar feature information extracted from the normalized two-dimensional laser radar data; inputting the fused information into a trained unmanned ship autonomous navigation model, outputting an expected yawing angle and an expected speed of the unmanned ship, and calculating the propelling speed of each propeller by combining a kinematic model and a kinetic model of the unmanned ship; and judging whether the unmanned ship arrives at a terminal point, if so, ending navigation, and otherwise, continuing the navigation process. According to the method, autonomous learning navigation can be realized in an unknown or dynamic environment, and the method has higher environment adaptability and decision optimization capability.

The invention discloses a robust adaptive fusion filtering astronomical attitude determination method and system, and belongs to the technical field of radio navigation. The method comprises the following steps: constructing an integrated navigation model of a starlight and inertial integrated navigation system based on Lie group description; obtaining an observation residual error and an observation residual error covariance of a corresponding mode; robust and adaptive mode parallel sub-filtering is carried out, and posterior states and covariances in the two modes are updated; updating the mode probability; and fusing the updated posterior state with the covariance to obtain fusion output of parallel sub-filtering, taking the fusion output of the parallel sub-filtering as final estimation of the starlight and inertial integrated navigation system based on Lie group description, and outputting an astronomical attitude determination result containing an azimuth angle, a pitch angle and a roll angle. The high-precision astronomical attitude determination method has strong adaptability, strong robustness and accuracy when coping with complex noise and measuring outliers, and can realize high-precision astronomical attitude determination of starlight and inertial integrated navigation in a complex dynamic environment.

The invention discloses a visual representation continuous vision language navigation model and method based on knowledge and historical perception, the continuous vision language navigation model comprises a topological graph construction module, a cross-modal planning module and a path control module, the topological graph construction module comprises an extraction module, a filtering module, an interaction module and an aggregation module; the extraction module is used for extracting an RGB image feature fr, a depth image feature fd, a first knowledge feature fk, a first historical feature fh and a navigation instruction fi; the filtering module calculates a correlation matrix among a first knowledge feature fk, a first historical feature fh and a navigation instruction fi to obtain a weighted second knowledge feature # imgabs0 # and a weighted second historical feature # imgabs1 #, and the interaction module interacts the second knowledge feature # imgabs2 # and the second historical feature # imgabs3 # with the instruction to obtain a multi-perception fusion feature fusion; the aggregation module aggregates the multi-perception fusion feature fusion, the RGB image feature fr and the depth image feature fd to obtain a visual representation fimg; according to the visual language navigation method provided by the invention, rich visual representations related to the navigation instruction are constructed, and the navigation capability of the intelligent agent is improved.

The invention innovatively provides an engineering height measurement method and system for an inertial navigation system based on a barometer as error compensation, engineering equipment is modeled, the modeling comprises the shape and characteristics of the equipment and the installation position of the inertial navigation system, different sensor parameters are adjusted through a simulation model, and the engineering height measurement accuracy is improved. Observing a starting jumping rule and a stopping jumping rule of the sensor, analyzing error performance of the sensor under different moving distances, and obtaining an inertial navigation simulation model through a simulation training strategy; collecting data of an inertial navigation system and a barometer in real time; inputting the inertial navigation data into the simulation model, and performing simulation correction on the inertial navigation data according to the inertial navigation data and a simulation algorithm; and the error compensation module is used for acquiring the corrected inertial navigation data and barometer data, acquiring an inertial navigation height through an inertial navigation model, calculating and acquiring an engineering height through a data fusion algorithm, and performing error compensation on the inertial navigation model by utilizing the engineering height through an error compensation strategy.

The invention relates to the technical field of surgical operations, in particular to an AR-based intelligent vision use method applied to surgical operations. The method comprises the following steps: S1, acquiring an anatomical structure image of a patient in real time through AR glasses equipment, and performing multi-modal fusion registration on the anatomical structure image and preoperative CT, MRI and 3D ultrasonic image data to generate a dynamic three-dimensional surgical navigation model; according to the method, multi-mode image fusion is combined with dynamic three-dimensional navigation, submillimeter intraoperative positioning is achieved, the misoperation risk is remarkably reduced, the intraoperative adjustment frequency is reduced through eye-controlled amplification and real-time early warning, and the operation time is shortened; meanwhile, the virtual screen and microscope linkage technology relieves the burden of the cervical vertebra and reduces the neck load of medical staff, and compared with a traditional teaching mode through holographic labeling and operation tracks, the training period of doctors can be obviously shortened, idle equipment can be automatically recognized, and the surgical accuracy, efficiency and medical resource popularity can be comprehensively improved.

Systems and methods are provided for collecting anonymized drive information. A processing device may be configured to receive outputs from one or more sensors; determine at least one motion representation for the host vehicle based on the outputs; receive at least one image representative of an environment of the host vehicle; analyze the at least one image to determine at least one road characteristic associated with a road section; assemble first road segment information relative to a first portion of the road section, wherein the first portion of the road section is separated from a starting point associated with a route traveled by the host vehicle; assemble second road segment information relative to a second portion of the road section; and cause transmission of the first road segment information and the second road segment information to a server for assembly of an autonomous vehicle road navigation model.

The invention belongs to the technical field of navigation methods, and discloses a self-error-correction navigation method, which comprises the following steps: on the basis of a basic navigation model, acquiring a model prediction track and a corresponding real track, carrying out time step alignment on the model prediction track and the corresponding real track, defining the moment when the start deviation of the track is greater than a preset threshold value as a track deviation point, and carrying out error correction on the track deviation point; the method comprises the following steps: acquiring navigation data in a preset time before and after a trajectory offset point, inputting a large language model, acquiring error analysis, converting the error analysis into structured training data, training a basic navigation model based on the structured training data, acquiring an optimized navigation model, and finally realizing navigation based on the optimized navigation model. According to the method, the deviation between the navigation action of the basic navigation model and the real track is detected, efficient error analysis is realized through the large language model, and the error analysis is applied to further training optimization of the basic navigation model, so that the navigation model has an autonomous error correction capability, and the reliability of the navigation capability is improved.

The invention provides an unmanned aerial vehicle self-adaptive route planning and dynamic obstacle avoidance method and system for distribution line inspection. According to the method, firstly, a special visual data set is constructed, a target detection network is optimized, and the detection precision is improved by fusing a double attention mechanism and a trainable weight feature fusion module; deploying a multi-sensor fusion system to realize three-dimensional positioning of a target, and inhibiting detection jitter in combination with video stream filtering; designing a deep reinforcement learning obstacle avoidance navigation model based on a TD3 framework, fusing GRU and an attention mechanism to process a sequence state, and realizing safe and efficient navigation through a multi-target reward function; a priority decision-making mechanism is established to synchronously execute feature recognition, attitude adjustment and dynamic obstacle avoidance, and model prediction control is adopted to realize closed-loop path planning. The system shows stable self-adaptive routing inspection and obstacle avoidance capabilities in a complex line environment.

The present invention relates to a robot visual language navigation method suitable for real indoor environments, comprising the following steps: obtaining environmental information and panoramic images; constructing a two-dimensional grid map of the real indoor environment and converting the continuous indoor environment into a discrete map; generating path-command pair training samples and extracting image feature information from the panoramic images, and constructing a visual language navigation dataset for the real indoor environment based on the two; constructing a visual language navigation model that receives language commands and current environmental information commands and outputs the next action selection at the current moment; performing transfer training on the visual language navigation model using the visual language navigation dataset as training data; and implementing the deployment of the visual language navigation function on a robot based on the ROS system. Compared with existing technologies, the present invention has the advantages of being applicable to indoor environments, significantly improving the success rate of visual language navigation tasks for robots in indoor environments, accelerating network model training, and having strong applicability.

A tasked robot for a deep reinforcement learning system using asymmetric self-play for robust multi-robot flocking is provided. The tasked robot includes a reinforcement learning control module and an auxiliary training module. The control module dynamically adjusts the robot's behavior to optimize decisions, featuring a target navigation model, a cluster behavior maintenance model, and a collision avoidance model. The auxiliary training module enhances environmental perception, enabling the robot to predict dynamic changes. The auxiliary training module includes a local environment grid estimation model to generate small-scale maps and a motion prediction model to forecast robot and obstacle trajectories.

A device may determine that transaction account information, associated with a transaction account associated with a user, is to be updated. The device may identify a merchant account that is configured to use the transaction account for a transaction associated with the user and a merchant, and navigate, using a navigation model, a merchant portal associated with the merchant, to access the merchant account. The device may perform, using the navigation model and login credentials for the merchant account, a login operation to access the merchant account via the merchant portal, wherein the login credentials are stored in a credential mapping that indicates an authorization to update the transaction account information to permit the transaction account information to be used in association with the merchant account. The device may update, using the navigation model, a transaction setting of the merchant account to include updated transaction account information for the transaction account.