1795 results about "Telemetry" patented technology

The present invention discloses a system and method for performing artificial intelligence (AI) safety red-teaming with integrated policy fuzzing and adversarial prompting to systematically identify, characterize, and mitigate unsafe or non-compliant behaviors in AI models. The disclosed invention automates the process of generating, executing, and analyzing adversarial test cases through coordinated functional units comprising a policy fuzzing unit, an adversarial prompting unit, an execution sandbox, a telemetry processing unit, a scoring and triage processor, and a cryptographic provenance processor. The system applies grammar-driven and reinforcement-based fuzzing techniques to vary policy descriptors, model configuration parameters, and instruction hierarchies, while a learned adversarial prompt generator synthesizes contextually coherent adversarial prompts optimized for maximum policy violation likelihood. The generated prompts and policy vectors are executed in an isolated, instrumented sandbox that records input-output interactions, timing characteristics, and intermediate representations.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

A system for adaptively caching network communication protocols enhances efficiency across heterogeneous device environments through a multi-level cache architecture with device-capability-based tiers. The system collects endpoint telemetry data including device capabilities and operational constraints to classify endpoints and generate context-aware protocol variants optimized for specific device types. Protocol optimization opportunities are determined through structural analysis of message patterns and state transitions. The system performs protocol deduplication by identifying functionally equivalent variants and maintaining canonical representations to reduce cache redundancy. Cache synchronization across distributed nodes uses enhanced Merkle tree structures with protocol normalization processing. The system predicts communication needs based on historical patterns, network context, and endpoint constraints, enabling proactive cache management tailored to device capabilities. Integration with event-driven data communication systems enables seamless protocol selection and translation while maintaining compatibility between diverse endpoint types, from high-performance servers to resource-constrained IoT devices.

In some implementations, a cybersecurity threat detection and mitigation system is provided. The system refines an artificial intelligence (AI) model with a corpus of historical data that represents security events that occurred, queries that were submitted by security analysts in response to the security events, and actions that were performed for mitigating the security events. Telemetry data that corresponds to behavior and performance of a computer network is collected and provided to the AI model. Based on the telemetry data, the AI model predicts a potential security threat to the computer network and performs an assessment of risk to the computer network. When the assessment of risk to the computer network indicates that the potential security threat is an actual security threat, a security alert that corresponds to the actual security threat is triggered. Other embodiments are described and claimed.

A system and method for facilitating collection of Road User Charges (RUC) using a digital currency within a Distributed Ledger Technology (DLT) network are disclosed. The system generates secure digital wallets for Road Users and Facility Owners, receives trip, position, distance, and cost data from vehicles, sensors, and facility systems, and encodes settlement logic into smart contracts. These contracts automatically execute jurisdiction-specific disbursements across blockchain, directed acyclic graph (DAG), or hashgraph frameworks, ensuring scalability and auditability without reliance on centralized tolling infrastructure. An AI Activity Module processes multi-source telemetry—including vehicle sensors, GNSS / PNT data, roadside infrastructure, and third-party traffic feeds—to detect congestion, forecast conditions, optimize routing, and dynamically adjust segment-level RUC pricing in real time. The integration of AI-driven adaptive pricing with distributed ledger settlement provides a decentralized, infrastructure-agnostic framework for secure, transparent, and verifiable road use fee collection across multiple jurisdictions, distinct from conventional tolling systems.

The present invention relates to an intelligent, context-aware battery management system embedded within a mobile device that dynamically allocates power resources based on real-time user behavior, system state, and environmental context. It incorporates a smart recognition engine that analyzes sensor-derived telemetry data to compute behavioral deviation scores, enabling the system to anticipate abnormal or emergency-prone conditions. A continuous activity classification module contextualizes user motion and geolocation to inform power policy decisions. Upon detecting significant behavioral anomalies or critically low battery conditions, an emergency mode subsystem is triggered, restricting device operations to essential functionalities while preserving energy for critical communication and navigation tasks. The system also establishes a secure, lightweight emergency communication tunnel for relaying essential metadata, including GPS and behavioral indicators, to predefined response servers.

A predictive incident management system, consisting of: a sensor input module configured to receive heterogeneous telemetry data streams from mechanical, thermal, electrical and cyber sources; a temporal correlation control unit operationally coupled to the sensor input module, wherein the temporal correlation control unit is configured to normalize received data into a uniform time series envelope that includes identifiers, microsecond-precision timestamps, metric names, values, and context markers, and is further configured to compute sliding window-cross-sensor correlation matrices, event motifs, and lead-lag dependencies across multiple time granularities; a scalable rule processor that is communicatively linked to the control unit for temporal correlation, wherein the rule engine includes an in-memory runtime environment for processing complex events and a domain-specific declarative language, and is configured to apply rules that reference primitive sensor metrics, derived correlation features, and motive-based early warning vectors to classify, escalate, or resolve predicted incidents; A historical repository that is communicatively connected to both the temporal correlation control unit and the rule engine. The repository is configured to store tagged event histories, correlation motif dictionaries, rule versions, and rule origin metadata to ensure the verifiability and explainability of predictions; and An incident response interface is operationally connected to the rule engine. The incident response interface is configured to trigger automated workflows, including the generation of tickets for IT service management, chat ops notifications, the execution of orchestration playbooks, and direct machine control via industrial protocols. the system is configured to perform predictive analyses based on temporal correlations between sensors and to execute context-aware, rule-based incident management in real time.

A system for detecting service interruptions in a cable broadband network using telemetry-based anomaly detection, wherein the system comprises the following: a telemetry acquisition unit configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements, including cable modems, amplifiers, optical nodes and cable modem termination systems (CMTS), wherein the telemetry data includes the signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels and latency statistics; a preprocessing and harmonization module that is operationally coupled with the telemetry acquisition unit, wherein the module is configured to normalize heterogeneous telemetry streams by adjusting sampling rates, synchronizing timestamps, interpolating missing data, and filtering out false outliers; an anomaly detection unit that is communicatively connected to the preprocessing and harmonization module, wherein the unit comprises a hybrid detection framework with statistical prediction models and machine learning models, wherein the statistical prediction models include ARIMA or Holt-Winters models to predict the expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble evaluation subsystem within the anomaly detection unit, configured to combine the outputs of the statistical prediction models and the machine learning models to generate anomaly probability evaluations with adaptive confidence intervals; an interruption classification module configured to receive anomaly probability values ​​and correlate anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module differentiates between transient anomalies and service-impairing interruptions based on a multidimensional correlation; and an alerting interface configured to transmit outage alerts with severity, root cause metadata, and geolocation to a network management system so that the operator can intervene.

A system and method for a hierarchical thought supervision network with adaptive processing capabilities. The system processes data through a base graph layer of interconnected computational nodes, a telemetry layer for real-time monitoring, and one or more supervision layers composed of supervisory nodes. The base layer handles thought processing and management, while the telemetry layer continuously tracks operational metrics to evaluate processing efficiency. Supervisory nodes adapt network operations by optimizing thought encodings, generating new nodes when needed, and pruning inefficient nodes based on performance objectives. A telemetry layer continuously tracks processing efficiency using adaptive kernel functions and topology-aware distance metrics. The system maintains effective processing while dynamically adjusting to computational demands through coordinated supervision across multiple layers. This approach enables real-time network adaptation while optimizing performance and efficiency across the system.

A system for context-sensitive orchestration of autonomous agents in cloud platforms, consisting of: a hardware-based orchestration device configured for integration into a distributed cloud infrastructure; a context inference engine within the orchestration device, wherein the context inference engine is configured to receive and aggregate real-time telemetry data from a variety of distributed nodes, including at least one system-level parameter, at least one application-level parameter, and at least one environment parameter; a semantic inference module within the context inference engine, configured to generate a context-related state representation by correlating the parameters using a knowledge graph-based model of interdependencies; an optimization unit for machine learning within the orchestration device, which is communicatively connected to the context inference engine and is configured to predict resource requirements and operational states using reinforcement learning models trained on historical and real-time data streams; a policy-driven orchestration controller configured to translate the contextual state representation into actionable orchestration decisions by applying dynamic orchestration policies stored in a domain-specific policy repository; and a distributed agent interaction bus configured to delegate orchestration decisions to a variety of autonomous agents deployed on the cloud platform.

The invention discloses an unmanned ship measurement and control system and method based on Beidou artificial intelligence, and relates to the technical field of unmanned ship measurement and control. According to the Beidou-based artificial intelligence unmanned ship measurement and control method, Beidou positioning information, navigation state parameters and water area image data are obtained in real time in the cruising process of an unmanned ship; inputting the image data into an AI visual recognition model to judge whether an obstacle exists or not; if there is an obstacle, extracting the features of the obstacle and constructing an obstacle spatial distribution map; generating a plurality of avoidance paths based on the distribution map and the navigation state parameters, and selecting an optimal path; according to the method, the image of the water area in front of the ship body is input into the AI visual model, feature extraction, target detection and depth estimation are combined, the spatial position and boundary information of the obstacle are extracted, and the spatial distribution diagram is constructed under the ship body coordinate system; and the obstacle identification precision and the positioning modeling capability are improved.

A system for automated code modification improves application performance in cloud environments by integrating telemetry analysis with large language model (LLM)-driven reasoning. The system collects contextual information about a target application, including metadata, source code, and configuration files, and correlates it with real-time telemetry data related to application performance. Based on this data, the system constructs a structured LLM prompt using a predefined schema, which is transmitted to an LLM. The prompt instructs the LLM to recommend modifications to source or configuration files that may enhance performance, along with natural language explanations for those recommendations. In response to receiving the LLM's response, the system extracts the proposed code or configuration changes and associated rationale, and presents them to a user via a client device interface.

A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

System and methods are disclosed for providing unforgeable telemetry on computer platforms. Mathematical modeling and theorem proving are utilized to guarantee the integrity of telemetry probe execution flow and trigger, thereby preventing circumvention and tampering of logged probe data. In contrast to current state-of-the-art solutions that rely implicitly on the operating environment, this approach provides a sound and complete assurance of telemetry output. The system enables organizations to map unforgeable telemetry probe data to industry and government cybersecurity regulatory controls, ensuring compliance therewith. This invention addresses the shortcomings of existing solutions, including their vulnerability to sophisticated attacks, operational complexity, and inability to provide unforgeable telemetry data, thereby providing a reliable and accurate monitoring output in the presence of cyberattacks on computer platforms.

A symbolic neuroadaptive control system is disclosed for real-time arbitration, consent, and ethical modulation of artificial intelligence agents operating in wearable computing environments. The system integrates multimodal biometric telemetry—including high-resolution EEG signals—with a symbolic kernel that performs logic-driven arbitration over cognitive, emotional, and ethical states. Using Coq-verified invariants and zero-knowledge biometric consent tokens, the system constructs a deterministic symbolic execution graph, gating AI outputs based on internal user states such as trauma, stress, or intentionality. Unlike conventional black-box BCI models, the invention routes EEG-inferred affective-symbolic tokens through a formal ethics layer that enforces real-time interrupt control, utility bounding, and trust verification. The kernel enables AGI systems to defer or modify behavior based on user-state alignment, granting sovereign agency over all downstream actions. This neuro-symbolic architecture redefines the interface between human cognition and intelligent machines, enabling emotionally conscious, morally verifiable, and symbolically transparent AI governance in dynamic, high-stakes contexts.The present invention relates to artificial intelligence and neurotechnology, specifically to a real-time, neuro-symbolic operating system kernel that converts electroencephalography (EEG) signals into structured symbolic data for use in emotional cognition, ethical prioritization, autonomous agent dispatch, and real-time telecommunications routing. The invention bridges brain-computer interface (BCI) inputs with symbolic AI architectures to enable ethically aligned machine response during cognitively or emotionally intense events.

Embodiments manage context repositories in computing environments to enhance automated security analysis. Embodiments obtain context records containing supplemental information associated with security events and integrates them into prompts for large language models (LLMs) to generate severity scores for event classification. Embodiments apply criteria to invalidate outdated or unreliable context records based on age, source reliability, and usage frequency, then modifies prompts and repositories accordingly. Enhanced prompts incorporate context record summaries and entity relationship mappings to improve subsequent event analysis. Embodiments dynamically evaluates context records through quality filters, consolidates duplicates, and maintains audit trails with provenance tracking. User interfaces are dynamically transformed based on telemetry metrics and user feedback to optimize analyst workflows. Embodiments enable organizations to maintain curated, high-quality context repositories that continuously improve AI-assisted security analysis while reducing false positives and enhancing incident response effectiveness.

Monitoring and configuring storage systems using generative AI, including: receiving, from a user, a request associated with a storage system directed to a generative artificial intelligence (AI) model; accessing, in response to the request, telemetry data generated by the storage system; and generating, using the generative AI model and based on the telemetry data, a response to the request

The invention relates to the technical field of power monitoring system network security and real-time micropatch hot deployment, and discloses a power disaster recovery system-oriented micropatch non-inductive deployment engine, a resource scheduling method, a system, equipment and a medium, and the method comprises the steps: capturing system events through a kernel eBPF probe, and carrying out feature extraction and model reasoning; generating and transmitting an encrypted scheduling token; loading and verifying a patch fragment by a patch agent, inserting a jump instruction through a kernel interface to redirect an execution stream, and maintaining multi-kernel cache consistency; fusing multi-source telemetry data to carry out fusing judgment, realizing network isolation and calling a key service to cancel a key; and collecting runtime indexes and performing trend prediction, triggering a recovery or rollback operation according to a result, and storing an operation result and data through a block chain. According to the method, through combination of deep fusion of multi-source heterogeneous data, dynamic reasoning of a knowledge graph and strategy optimization of reinforcement learning, efficient perception and defense of a complex attack scene of a digital power grid are realized.

Systems and methods are disclosed for real-time anomaly prediction using near real-time data. The invention addresses delays in telemetry data collection from infrastructure components, by collecting metrics and logging this data in real-time. Extracted logged data undergoes initial analysis to identify patterns and anomalies, followed by cleaning to remove noise and errors. Feature engineering enhances the data, creating or modifying features to improve machine learning model performance. The system calculates weighted means of previous data values and computes first and second-order differences to capture immediate changes and trends. These calculations adjust the extrapolated value to accurately reflect current conditions. The adjusted data is integrated into the dataset and validated. The validated data trains and tests a machine learning model, which is then finalized and deployed for real-time anomaly detection. This system ensures accurate and timely anomaly prediction, enabling automated incident response to maintain the reliability and performance of infrastructure components.

The invention discloses an in-orbit pointing deviation correction method for a two-dimensional mechanical antenna oriented to a panel satellite spot beam. The method comprises the following steps: acquiring an azimuth angle, a pitch angle and telemetry information of an antenna pointing to a ground station in real time in an in-orbit manner; reversely calculating a gain difference value between the actual signal and the theoretical signal; judging whether the gain difference meets the pointing precision requirement or not, and if not, inversely calculating the deviation angle of the actual pointing vector and the theoretical pointing vector and the actual pointing vector; a Gauss-Newton method is adopted to solve an optimal theoretical pointing vector; resolving an installation matrix pointed by the matching theory and adjusting an antenna installation matrix; and repeating the steps to form a closed loop until the gain difference meets the precision requirement. According to the method, three-dimensional installation matrix correction is adopted to replace traditional zero correction, closed-loop feedback and nonlinear optimization are combined, multi-source error high-precision compensation is achieved, the engineering feasibility is high, the in-orbit multi-satellite batch correction requirement of the panel satellite is met, and the communication system reliability and the data transmission rate are remarkably improved.

The invention discloses a method for confirming the connection relation of switch type equipment based on the topological state of a power distribution network. The method comprises the following steps: carrying out feature extraction on original telemetering data of the power distribution network to obtain telemetering features; performing feature extraction on the original remote signaling data of the power distribution network to obtain remote signaling features; fusing the telemetering features and the telesignaling features to generate fused features, and predicting the switch state of the switch-type equipment according to the fused features to obtain a switch state prediction value; generating a switch state prediction sequence according to the switch state prediction value to calculate the topology state of the power distribution network; and based on the topological state of the power distribution network, judging the connection relation of the switch-type equipment. According to the method, the switch connection relation is objectively deduced through extraction of remote measurement and remote signaling features and fusion prediction and topological state calculation. Data support is provided for power grid planning, and layout and power supply problems are avoided; the problem of fuzzy connection of the old power grid is solved without relying on ledgers or manpower; the method is suitable for complex scenes, and efficiency and accuracy are improved.

Systems and methods are disclosed for anomaly detection using a “detect and collect” cybersecurity monitoring approach. Initially, a cybersecurity monitoring system obtains and analyzes a baseline subset of telemetry data from computing resources to detect potential anomalies indicative of cybersecurity threats. Responsive to identifying such anomalies, the system selectively determines additional, contextually relevant telemetry data for targeted collection. This selective data collection significantly reduces telemetry volumes, enhancing efficiency and scalability. An intelligent data fabric and dynamic security knowledge graph are employed to enrich telemetry data in real-time, enabling comprehensive anomaly characterization, risk scoring, and automated security responses. The disclosed techniques support multimodal and multiresolution anomaly detection, adaptive learning, and rapid threat response within diverse distributed computing environments.

The invention discloses an intelligent alarm positioning method and system based on an unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle monitoring and geographic space information processing, and the method comprises the steps: obtaining a video stream in real time based on the unmanned aerial vehicle, and recognizing a risk point location in a video image; a three-dimensional space positioning model is constructed based on telemetry data and lens angle parameters acquired by the unmanned aerial vehicle in real time. And performing three-dimensional coordinate dynamic solution on the risk point location based on a three-dimensional space positioning model to obtain a world coordinate of the risk point location. And determining and outputting an alarm position of the risk point location based on the world coordinates. An automatic detection and artificial interaction dual-channel mechanism is adopted, an alarm target is accurately identified and positioned in an unmanned aerial vehicle real-time video, a corresponding relation between video pixels and geographic coordinates is established through real-time registration of an unmanned aerial vehicle image and the digital earth, a space coordinate conversion error caused by view angle difference is corrected by using a depth map, and an alarm target is accurately identified and positioned. Accurate conversion from video pixel coordinates to world coordinates is realized, and high-precision positioning support is provided for remote monitoring of the unmanned aerial vehicle.

Systems and methods for autonomous space-mission coordination integrate ground-based prediction with on-orbit execution. A processing system in a ground station may ingest multi-source environmental data, produce near-term nowcasts through an prediction model, rank pending spacecraft tasks against current resource telemetry, select a high-value task subset, convert the subset into time-tagged command packets, and transmit the packets through a communications link. A processing system aboard each spacecraft may receive the packets, merge them into a persistent schedule, and at each time tag slews attitude, activate an imaging or radar sensor with specified parameters, capture data, and store the data in non-volatile memory. The spacecraft may generate quality metrics for the captured data and return the metrics to the ground station.

The invention discloses a fine-grained QoS isolation method and device for NVMe-oF storage, relates to the technical field of data transmission, and mainly aims to solve the problem of tail delay amplification caused by cross-tenant I / O interference in a multi-tenant environment. The method comprises the following steps: acquiring system telemetry data and a multi-tenant I / O request stream in a multi-tenant scene; performing micro-slice processing on the multi-tenant I / O request stream, and importing each obtained micro-slice into a corresponding virtual queue; determining a two-dimensional credit vector for controlling each virtual queue based on the system telemetry data; and on the basis of the two-dimensional credit vector of each virtual queue, performing scheduling and gating release on the micro-slices in each virtual queue on a preset time grid. According to the invention, the micro burst is effectively inhibited at the RNIC near end, the delay amplification path is cut off, the I / O tail delay is reduced, and the service quality predictability in the shared storage environment is improved.

The invention relates to the technical field of power distribution equipment, and discloses a centralized station terminal which comprises a main control module, an FPGA module, a remote control bus driving circuit, a remote signaling bus driving circuit, a plurality of remote control expansion circuits, a plurality of remote signaling expansion circuits and a plurality of telemetering expansion circuits. Wherein the main control module is provided with a PCIe interface, the FPGA module is connected with the main control module through a PCIe bus, and the remote control bus driving circuit and the remote signaling bus driving circuit are both electrically connected with the FPGA module. And the plurality of remote control expansion circuits are electrically connected with the remote control bus driving circuit and are used for outputting remote control signals. And the plurality of remote signaling expansion circuits are electrically connected with the remote signaling bus driving circuit and are used for receiving the remote signaling signals. And the plurality of telemetering expansion circuits are directly and electrically connected with the FPGA module and are used for receiving telemetering signals. According to the centralized station terminal, the FPGA module can process the telemetering signals received by the multiple telemetering expansion circuits in parallel, and transmits the collected data to the main control module through the PCIe bus at a high speed, so that the equipment data collection efficiency is improved.

Methods, systems, and computer-readable storage medium for improving the efficiency of charging a fleet of electric vehicle (EV) that follow a prescribed route. The efficiency of charging the fleet of electric vehicle is improved by receiving telemetry data from the fleet of EVs, receiving charger data from a plurality of charges along the prescribed route, determining a charging plan for the fleet of the EVs based on a total cost per distance (TCD) of travel over each of the plurality of route segments that comprise the prescribed route and controlling a particular charger along the prescribed route to charge one of the EVs according to the charging plan.

The invention discloses an in-orbit satellite intelligent fault diagnosis system based on deep learning, and relates to the technical field of in-orbit satellite intelligent fault diagnosis, and the system comprises the steps: carrying out the data regularization processing of multi-source heterogeneous telemetry data generated in the in-orbit operation process of a satellite; capturing a weak abnormal mode at the early stage of the fault; identifying a fault mode in the operation process of the on-orbit satellite; and fault emergency levels are divided according to the fault emergency degrees. According to the method, efficient regularization of on-orbit satellite telemetry data, automatic mining of on-satellite lightweight depth features, online incremental learning recognition of unknown fault modes and intelligent hierarchical scheduling of diagnosis tasks are realized; the accuracy, timeliness, autonomy and generalization ability of on-orbit satellite fault diagnosis are improved, and the technical bottlenecks that a traditional method depends on manual rules, is difficult to deal with novel composite faults, lacks deep data mining ability, is difficult to deploy on a satellite and the like are effectively solved.

Conditions are identified in a telecommunications network based on data collected from the telecommunications network. The data comprises time series telemetry data collected from telecommunications systems in the telecommunications network or live production data from the telecommunications network; and raw error logs collected alongside the time series telemetry data for the telecommunications systems. Outputs from a time series insight generator and a sentiment analyzer are combined to generate an output report indicative of anomalous metrics in the telecommunications network.

The invention provides an interference signal identification and coping method, which is used for a ground control platform, and comprises the following steps: a data receiving step: receiving telemetry data; an information screening step: screening receiver positioning information, a navigation signal carrier-to-noise ratio and receiver state information in the telemetry data; a grid unit generation step: performing grid division on the three-dimensional monitoring space to generate a plurality of mutually independent grid units; an anomaly detection step of determining a suspected interference unit based on a machine learning algorithm; an interference area determination step: determining a statistical space, and determining an interference area based on a machine learning algorithm; an interference situation map generation step of generating an interference situation map based on a machine learning algorithm; and an anti-interference strategy generation step: a first working mode and a second working mode are determined, and the anti-interference capability of the first working mode is higher than that of the second working mode. According to the invention, the problems of slow identification and response to interference signals and untimely response of a receiver in the prior art are solved.