559 results about "Diagnostic data" patented technology

The invention discloses a multi-source heterogeneous medical data fusion and intelligent diagnosis method, and relates to the technical field of medical data processing and intelligent diagnosis, and the method comprises the specific steps: firstly, synchronously collecting medical images and clinical text data of a patient, and carrying out the correlation and integration to form a heterogeneous diagnosis data set; performing standardized feature extraction to obtain a feature set in a unified format; then constructing a parallel model, fusing features by using a cross-modal attention alignment technology, and guiding correction by means of a knowledge graph; and finally, the cross-modal diagnosis features are input into the reference model, automatic focus positioning is realized through processing, and a visual marker graph is output. Heterogeneous data of medical images and clinical texts are synchronously integrated, and the diagnosis feature reliability is improved through standardization processing, feature fusion and the like; a focus sensing mask is generated through comparison with a normal model, a multi-scale feature fusion technology is combined to realize automatic and accurate positioning of the focus, a large amount of labeled data is not needed, the process is simplified, and the diagnosis efficiency and accuracy are improved.

A navigation equipment real-time fault diagnosis method based on cross-modal attention fusion belongs to the technical field of power grid equipment fault diagnos.The method comprises the steps that firstly, the type of real-time diagnosis data capable of being obtained by navigation equipment is systematically analyzed, and the real-time diagnosis data is divided into four modals of sensor data, electrical data, acoustic data and text data; after data cleaning is completed, preliminary feature extraction is implemented. Then, constructing a fault diagnosis model based on a cross-modal attention mechanism, constructing a training data set by using the joint features, and carrying out neural network model training; after model training is completed, real-time fault diagnosis of the navigation equipment can be realized through a data pre-processing technology and a fault diagnosis model based on acquired data. According to the scheme, a multi-modal real-time sensing system is constructed, deep feature interaction is realized in combination with a cross-modal attention mechanism, and closed-loop diagnosis capability is established. And core guarantee is provided for real-time state sensing and accurate fault positioning of navigation equipment.

A system and method includes connecting an interface device to a port of a vehicle to be in communication with an electrical system of the vehicle for monitoring the vehicle, and running a selected one of multiple available calibration and / or repair programs via the interface device to generate a calibration / repair data file of the vehicle, where the data / log file is generated in one of a plurality of possible native file formats. A report generation tool acquires screen captures and / or screen recordings to document the data / information displayed during the steps of a calibration / repair procedure. An evaluation tool extracts calibration / repair data from the data / log file, where the evaluation tool is configured to extract calibration / repair diagnostic data from data / log files in each of the plurality of possible native file formats, and outputs the calibration / repair diagnostic data to a database in a common format from which detailed calibration / repair diagnostic reports are generated.

The invention discloses a thyroid ultrasound image diagnosis method based on deep learning, and the method comprises the following steps: collecting a thyroid ultrasound original image set, and carrying out the preprocessing; performing focus segmentation on the standardized thyroid ultrasound image set; performing morphological constraint and boundary refinement; calculating the blood flow direction, blood flow velocity and blood flow power of each thyroid focus area and neighborhood; generating a preliminary fusion feature map based on a feature adaptive deep learning network, and fusing the preliminary fusion feature map with the thyroid focus blood flow feature vector set; obtaining a thyroid focus detection list through thyroid focus benign and malignant discrimination branches; and generating thyroid focus structured diagnosis data records based on the thyroid focus detection list, and writing the thyroid focus structured diagnosis data records into a computer-aided diagnosis system. According to the method, deep learning and multi-modal blood flow features are fused, intelligent diagnosis of the thyroid focus is achieved, and the method has the advantages of being high in precision, high in interference resistance and structured in result.

The invention provides a temporal-mandibular joint disease intelligent diagnosis method and device based on multi-modal data, and the method comprises the following steps: obtaining multi-modal medical data, and extracting the vector representation of each piece of multi-modal medical data to form a knowledge vector library; extracting entities, entity relationships and events from the multi-modal medical data, and establishing a disease diagnosis knowledge graph based on the entities, the entity relationships and the events; and obtaining to-be-diagnosed data, and matching the to-be-diagnosed data with the disease diagnosis knowledge graph in the knowledge vector library to obtain an optimal diagnosis method. According to the scheme, the knowledge vector library and the disease diagnosis knowledge graph are constructed by acquiring the multi-modal medical data, feature extraction and analysis are performed on different modal data in multiple modes, the different modal data are integrated into the knowledge graph, and meanwhile, the optimal diagnosis result is acquired based on multi-mode matching weighted sorting of the to-be-diagnosed data, so that accurate and intelligent diagnosis is realized.

The invention relates to the technical field of Tokamak plasma diagnosis and data fusion, in particular to a multi-modal fusion Tokamak high-frequency Thomson scattering reconstruction method. According to the technical scheme, the method comprises the following steps: acquiring multi-modal diagnosis data on a Tokamak device, wherein the multi-modal diagnosis data comprises high-frequency auxiliary diagnosis data and low-frequency Thomson scattering data; and performing feature engineering and physical enhancement processing on the high-frequency auxiliary diagnosis data. According to the method, through multi-modal diagnosis data fusion and interpretable KAN network modeling, the Thomson scattering time resolution is greatly improved on the premise that hardware is not transformed, and high-fidelity reconstruction of sub-millisecond transient processes such as ELM and magnetic island evolution is achieved; the method has physical interpretability, can reveal the implicit relationship between diagnostic quantities, enhances the robustness under complex experimental conditions through strategies such as modal loss, and provides key high-frequency diagnostic support for Tokamak high-performance operation and real-time control.

The invention discloses a power distribution network single-phase grounding reason diagnosis method based on confidence and ambiguity evaluation. The method comprises the following steps: acquiring original diagnostic data corresponding to a waveform to be detected; determining a confidence level corresponding to the original diagnosis data according to a preset grading rule based on the similarity distance corresponding to each of the plurality of neighbor sample waveforms in the original diagnosis data; determining a plurality of candidate reasons based on the single-phase grounding reasons corresponding to the plurality of neighbor sample waveforms; based on the number of the plurality of neighbor sample waveforms, determining occurrence frequencies corresponding to the plurality of candidate reasons; determining target ambiguity based on the occurrence frequencies corresponding to the candidate reasons and the reason label semantic association matrix; and based on the confidence level and the target ambiguity, adaptively generating a diagnosis result. According to the method, the technical problem that the information completeness of the conclusion output by the existing analysis method is insufficient, so that an objective judgment basis for the conclusion reliability is lacked when the operation and maintenance personnel make decisions is solved.

The invention discloses an ultrasonic auxiliary system for automatic recognition and diagnosis of emergency fracture, and relates to the technical field of acoustic fracture detection. The auxiliary system comprises an ultrasonic image acquisition module used for acquiring ultrasonic image data of a fracture position of a patient; the processing module is used for processing the ultrasonic image data and outputting identification data; and the diagnosis decision module is used for outputting diagnosis data based on the identification data of the processing module. Through the arrangement of the region focusing analysis unit, resource allocation is optimized and calculated by gradually reducing the analysis range, the primary detection layer quickly eliminates intact regions and outputs primary fracture region marks, the intermediate classification layer performs soft tissue reaction quantitative analysis on the primary fracture regions and generates marked regions, and the advanced positioning layer processes the marked regions. The resource consumption is reduced in combination with sub-pixel-level analysis, and the architecture enables system resources to be quickly concentrated in a fracture area, so that the processing time efficiency of fracture recognition is reduced.

This invention relates to methods and systems relating to medical diagnostics. In particular, the invention relates to a method of processing data from a medical diagnostic system which comprises a detector arrangement configured to detect interaction of high energy particles with tissue. The method comprises receiving diagnostic data associated with interaction of high energy particles with tissue, wherein the diagnostic data is generated as a result of interaction of the high energy particles and the tissue being detected by the detector arrangement of the medical diagnostic system. The method further comprises processing the received diagnostic data with a trained machine-based classifier in order to facilitate and / or assist a medical diagnosis, wherein the trained machine-based classifier is trained at least with digital diagnostic data generated by a digital twin of the medical diagnostic system, wherein the digital twin is a computer-implemented simulation of at least part of the medical diagnostic system.

The invention discloses a vehicle diagnosis service method and system based on dynamic protocol adaptation and context awareness, and the method comprises the steps: analyzing a diagnosis request, and determining a service range and a dependency relationship required by the diagnosis request; obtaining multi-dimensional context information, and based on the obtained information and the dependency relationship, executing a routing decision to select an optimal service instance and a protocol path; dynamically calling a protocol adapter through a protocol adaptation matrix based on the service instance and the protocol path, converting the diagnosis request into a protocol message, and sending the protocol message to a vehicle network; and receiving the original diagnosis data returned by the vehicle network to generate a complete diagnosis report. According to the method, flexible calling of the protocol adapter is realized through the dynamic protocol adaptation matrix, the service range and the dependency relationship are analyzed in combination with the service governance engine, and the routing decision is optimized by utilizing the multi-dimensional context information, so that the method has the advantages of dynamic and flexible protocol adaptation, accurate and intelligent service analysis and global optimization of the routing decision.

The invention provides an interaction method and system for thromboembolism risk assessment, and can be applied to the technical field of artificial intelligence. The method comprises the steps that a first server side responds to an inquiry request sent by a first terminal, and inquiry diagnosis data and historical clinical data are sent to a second server side; the second server obtains an initial risk assessment score according to the inquiry diagnosis data and the historical clinical data; under the condition that the initial risk assessment score is greater than a preset risk threshold value, the second server side sends inquiry diagnosis data and historical clinical data to a second terminal; the second terminal determines risk influence factors according to the inquiry diagnosis data and the historical clinical data; the second server obtains the detection result, and performs hospital thromboembolism risk assessment on the detection data by using a Bayesian network to obtain a target risk assessment result; and the second server determines a rehabilitation guidance strategy according to the target risk assessment result and the discharge diagnosis result, and sends the rehabilitation guidance strategy to the first terminal.

The invention discloses a marine environment spring layer characteristic diagnosis data processing method, and aims at complex dynamic characteristics of a land frame edge sea spring layer to realize accurate diagnosis through multi-source data cooperation and an intelligent algorithm. The method comprises the following steps: acquiring and preprocessing field observation, remote sensing, sonar, terrain and tidal ocean current data; extracting spatio-temporal characteristics based on a CNN-LSTM hybrid network, and generating a dynamic spring layer threshold; fusing multi-source data by adopting an improved D-S evidence theory, and constructing a three-dimensional spring layer characteristic field; predicting an internal wave in combination with an attention mechanism model, and optimizing a preorder model by using sonar data. The method breaks through the limitation of a fixed threshold value, realizes spring layer dynamic identification, three-dimensional reconstruction and internal wave prediction, improves the diagnosis precision of a complex sea area, and provides key technical support for underwater detection, engineering safety and environmental research.

An automotive diagnostic method aimed at focusing diagnostic data retrieval and analysis on a possible fault condition includes receiving vehicle data from a vehicle at a data acquisition and transfer device (DAT) and analyzing the vehicle data to identify a possible fault condition. A drive cycle associated with the identified possible fault condition is identified, and driving conditions are monitored to identify completion of the identified drive cycle. Upon completion of the identified drive cycle, vehicle data is analyzed to identify an abnormal monitor. Vehicle data is also analyzed to identify an abnormal value of In-Use Monitor Performance Ratio (IUMPR) associated with the abnormal monitor. When an abnormal value of IUMPR is identified, a likely diagnostic condition associated with the identified abnormal IUMPR is further identified.

The invention provides a task execution method based on an intelligent agent, and relates to the technical field of artificial intelligence. The method comprises the steps that an execution plan is generated based on task description of a to-be-processed task, the execution plan comprises a plurality of sub-steps with an execution sequence, and each sub-step has a corresponding sub-step description; aiming at each target step, determining a tool to be called from a preset tool library by utilizing target step description corresponding to the target step, and retrieving reference memory data from the memory data; calling the to-be-called tool in combination with the reference memory data to obtain a tool calling result; and if the tool calling result is invalid, acquiring calling diagnosis data of the tool to be called, updating the execution plan into a new execution plan by utilizing an invalid type in the calling diagnosis data, and executing the task to be processed according to the new execution plan. When the tool calling result is invalid, the execution plan can be flexibly adjusted according to the specific invalid type, normal execution of the task is ensured, and then the success rate of task execution can be increased.

The invention relates to the field of wireless communication networks, and particularly discloses a wireless sensor data transmission and storage method based on distributed nodes, which comprises the following steps: extracting feature vectors of local sensing data by nodes; local structure response field reconstruction is carried out in cooperation with the feature vectors of the adjacent nodes, and the abnormal confidence degree of own data is evaluated; according to the field reconstruction quality and the abnormal confidence coefficient, only a compressed state report or a complete diagnosis data packet is selected to be transmitted intelligently; and the node learns a state evolution trend based on historical data, and autonomously generates and adjusts a future monitoring strategy. According to the method, the defects of high resource consumption, shallow monitoring depth and stiff strategy of a traditional method are overcome, the resource utilization efficiency is greatly improved, the monitoring depth and reliability are remarkably enhanced, and autonomous optimization and prospective operation and maintenance of the system are realized.

The invention discloses a multifunctional cardiovascular monitoring system which comprises an electrocardiosignal acquisition module, a conduction block feature extraction module, an arrhythmia classification processing module, a cardiac function parameter calculation module, an intelligent diagnosis analysis module and a monitoring data output module. The electrocardiosignal acquisition module acquires and transmits original signals, the conduction block feature extraction module extracts feature parameters by using an exclusive algorithm, the arrhythmia classification processing module obtains classification results through a multi-dimensional classifier, and the cardiac function parameter calculation module generates calibration parameters by using a dynamic calibration model in combination with the classification results. The intelligent diagnosis analysis module generates diagnosis data through comprehensive analysis of the auxiliary platform, and the monitoring data output module processes the data and then outputs the data. The system realizes module cooperation and data interaction, improves the monitoring efficiency, guarantees data accuracy by using a professional algorithm and a model, and provides reliable support for clinical cardiovascular monitoring.

The disclosure provides a method for constructing a knowledge graph for vehicle fault diagnosis. The method can comprise creating a database of vehicle components; creating a database of abnormal signals; creating a fault knowledge triple from existing unstructured fault diagnostic data and semi-structured fault diagnostic data; and associating the abnormal signal, the vehicle component and the repair suggestion based on the fault knowledge triples to construct the knowledge graph for vehicle fault diagnosis.

The invention discloses a traditional Chinese medicine syndrome type matching method based on cross-modal attention fusion and a knowledge graph, and the method comprises the steps: obtaining multi-modal diagnosis data of a patient, the multi-modal diagnosis data being used for representing four kinds of diagnosis information collected by a doctor through watching, smelling, asking and cutting, including visual diagnosis data, pulse condition time sequence data and text data; inputting the multi-modal diagnosis data into a trained intelligent traditional Chinese medicine analysis system to obtain a syndrome type matching result of the patient; the intelligent traditional Chinese medicine analysis system comprises a feature extraction and fusion module and an evidence fusion derivation module, and the feature extraction and fusion module is used for extracting features of different modal diagnosis data to obtain multi-modal diagnosis features. And the cross-modal attention operation module is used for carrying out cross-modal attention operation on the multi-modal diagnosis features to fuse the features of different modals to obtain a unified semantic representation, and the evidence fusion derivation module is used for generating an evidence type matching result according to the semantic representation. The method is high in accuracy and good in effect.

The invention relates to the technical field of multi-source heterogeneous data analysis, in particular to a multi-source heterogeneous data-oriented adaptive analysis model construction method and system, and the method comprises the steps: collecting a multi-source heterogeneous data family of a plurality of bolts, and screening alternative diagnosis data to generate a multi-modal key feature vector; judging whether the multi-modal key feature vector is abnormal or not based on the multi-modal key feature vector and the clustering feature vector; obtaining an abnormal multi-modal key feature vector, and generating a fusion feature vector based on a deep learning model; generating an initial diagnosis result based on the fused feature vector; comparing the diagnosis accuracy with a preset threshold value, and judging whether to trigger deep learning model retraining or not; and correcting the attention center of gravity based on the error feature vector and the clustering feature vector, and generating a confidence diagnosis result to position abnormal dimensions and abnormal data. According to the method, the multi-source heterogeneous data analysis efficiency and the anomaly detection accuracy in the bolt tightening process of the battery pack production line are improved.

The invention provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention relates to an intelligent study and judgment method for abnormal behaviors based on a data flow scene, and aims to solve the technical problem that the data flow scene of data security of an existing digital power grid lacks artificial intelligence technology deep fusion management for physical natural problems under the large background of multi-source heterogeneous energy fusion. The method is characterized in that a unified operation interface of the method is provided with an abnormal state model, and rapid fault diagnosis, data credibility evaluation and automatic data path reconstruction are realized through the abnormal state model so as to store and recover power grid security data; the model covers links of data acquisition, analysis decision, execution recovery and closed-loop feedback, realizes data fusion, anomaly detection and repair by means of digital twinning, edge intelligence and an LSTM neural network, forms a trusted data sequence, supports research and judgment and outputs a recovery instruction; and the decision instruction is issued to the SDN / NFV controller, the data toughness is improved through path switching, and normal operation of the power grid is quickly recovered.

Systems and methods for providing assistance to a surgeon during an implant surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining a screw bone type based on the surgeon's input. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for a screw are determined based on the salient points of the areas of interest. Successively, a maximum screw diameter and a length of the screw are determined based on the salient points. Thereafter, the screw is identified and suggested to the surgeon for usage during the implant surgery.

The invention discloses a vehicle fault diagnosis method, device and equipment, a medium and a product. The method comprises the following steps: acquiring real-time vehicle diagnosis data, and extracting a diagnosis vector of the vehicle diagnosis data through a pre-trained language model; determining fault association knowledge according to the diagnosis vector and a fault vector database; constructing a fault diagnosis decision diagram according to the fault knowledge graph, the maintenance manual and the historical work order; based on a graph neural network model, extracting a fault reasoning sub-graph from the fault diagnosis decision graph according to the diagnosis vector; converting a node relationship in the fault reasoning sub-graph into a natural language reasoning chain; according to the vehicle diagnosis data, the fault association knowledge and the natural language reasoning chain, constructing prompt words; and generating a fault diagnosis result based on a large language model and the cue word. According to the embodiment of the invention, a more efficient and accurate solution can be provided for remote vehicle fault diagnosis.

The invention provides a closed-loop reinforcement learning diagnosis method and system based on a high-fidelity virtual clinical environment, and the method comprises the steps: 1, carrying out the structural processing of basic information and an examination record set of a patient, and obtaining a standardized examination and diagnosis data set; 2, training to obtain a high-fidelity clinical environment system; and step 3, based on the high-fidelity clinical environment system, obtaining a diagnosis agent through reinforcement learning training. A simulation examination result highly consistent with the state of a patient can be generated based on real electronic health record data, a diagnosis agent performs multiple rounds of interaction in a virtual environment, autonomously explores a diagnosis path, and performs strategy optimization through a double-reward mechanism, so that the crossing of a diagnosis model from static prediction to dynamic decision is realized, and the diagnosis efficiency is improved. And the diagnosis accuracy and the reasonability of examination recommendation are greatly improved.

A diagnostic system for assessing engine wear utilizes data from sensors in a vehicle engine compartment and electronic data from a vehicle communications network to provide a comprehensive diagnostic evaluation. The system includes a sensor device with an audio sensor for capturing engine sound, an accelerometer for detecting vibration, and a near-field communication (NFC) tag for sensor information. The sensor device communicates sound and vibration data to a remote server, while a data acquisition and transfer (DAT) device connected to the vehicle's diagnostic port transmits engine performance metrics. A Dynamic Time Warping (DTW) algorithm aligns time-series data from the sensor and vehicle metrics to identify engine problems. The system supports cloud-based diagnostics and enables optimal sensor placement using geo-location or positional data. Applications include smartphone integration, Wi-Fi 6 communication for high-speed data transfer, and scan modes for varying diagnostic detail levels.

Methods and systems for managing operations of a data processing system are disclosed. To manage operations of the data processing system, a management controller of the data processing system may obtain diagnostic data from hardware resources of the data processing system. The management controller may provide the diagnostic data to a device operation manager. The management controller may obtain a response from the device operation manager indicating a level of anomalousness of the diagnostic data with respect to a baseline for the data processing system. In a first instance of the obtaining where the level of anomalousness meets criteria, the management controller may obtain an action set for updating the operation of the data processing system. The management controller may perform the action set to update an existing operating state of the data processing system to reduce a likelihood that a hardware resource becomes impaired.

The invention belongs to the technical field of fault detection and recovery, and particularly relates to a fault self-diagnosis and self-adaptive recovery method for an embedded controller, which comprises the following steps of: starting a fault self-diagnosis process by adopting a periodic triggering mode and an event triggering mode, and acquiring, processing and generating a diagnosis data source with time sequence characteristics in real time; step-by-step anomaly recognition is carried out through a three-level anomaly recognition mechanism, an anomaly feature set is extracted, cooperative fault positioning is carried out in combination with a weighted cosine similarity algorithm and fault tree analysis, fault types and severity levels are judged, and a fault positioning report is generated; the optimal self-adaptive recovery strategy is matched to carry out preliminary recovery, the strategy is divided into light, medium and deep levels according to serious levels and is provided with priorities, and the scheme with the minimum influence on the core service is preferentially selected; the recovery effect is verified through multi-dimensional comparison, and processing is carried out according to the recovery effect. According to the method, the real-time accurate diagnosis, intelligent positioning and self-adaptive recovery of the fault of the embedded controller are realized, and the reliability and self-healing capability of the system are improved.

Systems and methods for assisting a surgeon with implant during a surgery are disclosed. A method includes defining areas of interest in diagnostic data of a patient and defining an implant type. Post defining the areas of interest, salient points are determined for the areas of interest. Successively, an XZ angle, an XY angle, and a position entry point for an implant are determined based on the salient points of the areas of interest. In spinal procedures, a maximum screw diameter and a length of the spinal screw are successively determined based on the salient points. Based on determined length and diameter, a spinal screw and a matching screw guide is determined. Thereafter, the spinal screw and the screw guide is printed using a Three-Dimensional (3D) printer. Such printed spinal screw and screw guide could be used by the surgeon during the spinal surgery.

The invention discloses a mechanical fault diagnosis method and system based on federal field generalization, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting real data; a global denoising diffusion probability model is trained, synthesis data are obtained through synthesis of the global denoising diffusion probability model, and the synthesis data and the real data are mixed to form balance data; inputting the balance data into a local model and a global model, and extracting features by the global model by using a heterogeneous feature extractor to form a feature vector with a fixed length; training a local model and a global model by using the local features and the feature vectors based on a bidirectional knowledge distillation technology; aggregating parameters of the local model, and re-establishing a global model; and inputting to-be-diagnosed data into the global model to obtain a fault diagnosis result. According to the method, the problem of triple heterogeneity in 3H-FDG is systematically solved through step-by-step optimization of data, features and model levels, and high efficiency of cross-domain fault diagnosis is ensured while data privacy is protected.

A method including: interpreting a user input; generating, based on the user input, a first agnostic vehicle diagnostic command; translating the first agnostic vehicle diagnostic command to a first adapted vehicle diagnostic command; interpreting a first adapted vehicle diagnostic data generated in response to executing the first adapted vehicle diagnostic command; translating the first adapted vehicle diagnostic data to first agnostic vehicle diagnostic data; generating a second agnostic vehicle diagnostic command based at least in part on the first adapted vehicle diagnostic data; translating the second agnostic vehicle diagnostic command to a second adapted vehicle diagnostic command; interpreting second adapted vehicle diagnostic data generated in response to executing the second adapted vehicle diagnostic command; translating the second adapted vehicle diagnostic data to second agnostic vehicle diagnostic data; and generating a state value for the vehicle based at least in part on the second adapted vehicle diagnostic data.