2777 results about "Analysis data" patented technology

The invention discloses an intelligent management system for energy consumption optimization and fault self-diagnosis of cleaning equipment, and relates to the field of intelligent maintenance of the cleaning equipment, and the system comprises the steps: obtaining three groups of core parameters, i.e., a historical vibration spectrum, a motor current harmonic component and a bearing temperature gradient, constructing a dynamic failure mode knowledge graph, performing time sequence correlation analysis on the historical fault data to obtain failure mode analysis data; establishing a multi-dimensional analysis platform, identifying a high-risk component, and updating a fault threshold value; introducing a service time attenuation factor and a working condition correction coefficient, establishing an aging degree quantitative model, and calculating an aging coefficient; and constructing and developing an energy consumption-reliability joint optimization module, and adjusting equipment operation parameters. The method has the advantages that the dynamic knowledge graph and the time sequence analysis model are constructed by integrating multi-source sensor data, precise diagnosis and self-adaptive threshold adjustment of the coupling fault of the cleaning equipment are achieved, aging evaluation and task scheduling optimization are combined, the energy consumption efficiency is improved, and the maintenance cost is reduced.

The invention relates to methods and systems for integrating generative artificial intelligence (AI) capabilities into Software as a Service (SaaS) platforms. It comprises maintaining AI agents with varying credentials, enabling their interaction with alphanumeric data in table structures, and implementing a hierarchical access control scheme. The system displays table structures, provides interfaces for user inputs, and allows AI agents to be added as platform users. The generative AI agents can analyze data, identify actions, and perform tasks autonomously. The invention also includes methods for proactive information gathering, interactive analysis of AI outputs, and management of AI resources as limited assets. This approach enhances SaaS functionality by enabling AI-driven task completion, data analysis, and decision-making while maintaining data security and user-specific access controls.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

The invention discloses an engineering project supervision real-time visual decision-making platform based on multi-source heterogeneous data fusion, and relates to the technical field of engineering supervision. The visual decision platform is in communication connection with a data acquisition module, a project risk feature module, a risk identification model construction module, a real-time data stream processing module, a risk early warning module and a decision support module. According to the invention, the risk in the engineering project is accurately identified in combination with historical multi-source heterogeneous data and project risk characteristics, and meanwhile, the real-time data stream processing module can analyze data in real time, identify potential risks and problems and generate early warning information, so that the accuracy and timeliness of risk identification are greatly improved, and the risk identification efficiency is improved. According to the invention, powerful decision support is provided for supervisors, and through the preset early warning level and the early warning signal, managers can rapidly understand the risk condition and take corresponding measures, thereby effectively reducing the project risk.

Systems and methods include discovering and classifying any of data discovered by inline cloud inspection, data stored across one or more cloud services, and data stored across one or more endpoints; continuously monitoring access to and usage of classified data, wherein the monitoring is performed in real-time and includes analyzing data access patterns, user behaviors, and application interactions; evaluating a security posture of the classified data by identifying misconfigurations, compliance violations, excessive permissions, and vulnerabilities; and enforcing one or more security policies based on the evaluated security posture.

The invention discloses a short-time rainfall prediction method based on radar image and reanalysis data fusion, and the method comprises the following steps: collecting radar images and reanalysis data at continuous times, and generating input data in a unified grid format through spatial interpolation, time alignment and standardization processing; respectively extracting spatial and temporal features of the radar image and the reanalysis data by using a dual-channel encoder, and carrying out weighted fusion through a channel attention mechanism to generate a fusion feature tensor; inputting the fusion features into a ConvLSTM (Convolutional Long Short-Term Memory Neural Network), modeling a spatio-temporal evolution process of a rainfall system, and outputting a preliminary rainfall prediction image in 0-3 hours in the future; constructing a residual learning network, and performing deviation correction on the preliminary prediction result based on historical residual and observation information; when the radar image input is missing, the completeness of the input structure is maintained through the replacement feature generation module; generating a rainfall intensity image or a probability graph in 0-3 hours in the future; the method supports visual output.

The embodiment of the invention discloses a multi-agent collaborative data visualization analysis method and device and a medium, and relates to the technical field of visualization analys.The method comprises the steps that a natural language analysis request input by a user is received, task splitting is conducted on the natural language analysis request through a preset task planning agent, and the task splitting result is obtained; generating corresponding task plan information, obtaining real-time operation data of a preset functional agent cluster, carrying out agent distribution on the plurality of sub-tasks based on the real-time operation data and the task plan information, determining a target functional agent corresponding to each sub-task, and sending the target functional agent to a server; the target function agent comprises any one of a data analysis agent and a visual display agent; and executing the corresponding data analysis subtask through the data analysis agent to obtain a data analysis result, performing agent cooperative verification on the data analysis result, and after the verification is passed, generating visual analysis data corresponding to the data analysis result through the visual display agent.

The present disclosure is directed to enabling asynchronous analytics via an intercept device. The intercept device may intercept a request transmitted by a user device to a host server, the request indicating data of interest to be provided by the host server in a response; transmit the request to the host server; asynchronously extract analytic data from the request; transmit the analytic data to an analytics server for performance of analytics on the analytic data; receive, from the analytics server, a result of performance of the analytics on the analytic data; determine, based at least in part on analyzing the result, whether the response from the host server is to be modified; selectively effect modification of the response based at least in part on determining whether the response from the host server is to be modified; and transmit the response to the user device. Various other aspects are contemplated.

The invention discloses a method and system for encrypting sensitive data based on a large model, relates to the field of data security, and solves the problem that a traditional fixed encryption strategy lacks dynamic adaptability. An encryption strategy container is generated through a data evaluation server, an evaluation module analyzes data content characteristics through a large model, a multi-dimensional sensitivity evaluation matrix and a use scene-risk level association model are established, and meanwhile the performance of an encryption module is tested; calculating a risk level according to a data sensitivity weight and a scene risk coefficient, triggering an alarm if the risk level exceeds a threshold value, otherwise, comparing a standard scene risk model to adjust encryption algorithm parameters, and generating a scene self-adaptive dynamic encryption strategy in combination with an encryption efficiency curve after performance compensation; and finally, the encryption gateway executes encryption. The method can improve the resource utilization rate through dynamic adaptive encryption, guarantees the stable efficiency through real-time performance compensation, improves the perspectiveness through intelligent prediction, reduces the safety risk and management cost through whole-process protection, and is suitable for a multi-industry sensitive data protection method.

The invention provides a dynamic security risk assessment and intelligent response system and method based on AI, relates to the field of dynamic security risk assessment and intelligent response, and improves the accuracy and response speed of security protection. The method comprises the following steps: firstly, collecting multi-dimensional security data, carrying out data preprocessing by utilizing an AI technology, and outputting a structured security data stream; then analyzing the data flow based on an AI model, identifying cross-dimension attack features, generating real-time threat intelligence, updating a risk judgment threshold value, generating a security assessment report, automatically generating and executing hierarchical response measures according to the assessment report, feeding back a response execution effect, performing dynamic adjustment, and generating a new security assessment report; and finally, automatically selecting a data transmission mode and rate according to the new security assessment report, and adjusting the acquisition frequency of the sensor. According to the method, through dynamic optimization and an intelligent response mechanism, the recognition and defense capability on complex attacks is remarkably improved.

The invention discloses an error correction system and method for marine environment observation data. The error correction system comprises a data acquisition module, a compensation modeling module, an error analysis module, a data compensation module, a data fusion module and a feedback optimization module. The data acquisition module acquires ADCP flow measurement, GPS, IMU, temperature, pressure and water quality monitoring data. And the error analysis module detects a drift error by using Kalman filtering and a hidden Markov model, and analyzes an environmental error based on a long short-term memory network. The data compensation module optimizes a compensation model and improves data precision. And the data fusion module fuses multi-sensor data by adopting a particle filter and a Bayesian optimization algorithm. The feedback optimization module dynamically adjusts the weight of the compensation model according to the error correction data, and improves the adaptability of the system. According to the invention, the accuracy of ocean current monitoring data is improved, and more reliable data support is provided for underwater environment research.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

The invention relates to the technical field of reservoir evaluation, in particular to a deep learning-based reservoir evaluation model construction method and system. The method comprises the following steps: acquiring logging data and core analysis data of a target work area, and respectively carrying out abnormal value processing and missing value filling to obtain complete logging data and complete core data; calculating reservoir pore structure parameters based on the complete core data; and inverting the macroscopic porosity of the reservoir based on the complete logging data. According to the method, the accuracy, the stability and the robustness of reservoir evaluation are remarkably improved by fusing the core data and the logging data and combining the pore structure parameters, the macroscopic porosity and the geological attention mechanism, the problems of data missing and small samples are solved, and the interpretability and the geological consistency of the model are enhanced.

The invention discloses a block chain-based carbon emission factor data traceability management system, particularly relates to the technical field of cross-supply-chain carbon data management, and is used for solving the problem that cross-enterprise / cross-industry effective association of carbon emission data under an existing single-chain architecture cannot be realized. The problems that the whole life cycle carbon emission data of the same product is repeatedly calculated and accounting boundaries are overlapped are solved; the method comprises the following steps: acquiring a carbon emission factor data packet of each participant through a data acquisition module; the block evidence storage module writes the data packet into a block chain to generate a non-tampering block; the cross-chain identification module creates a cross-chain traceability identifier containing a product batch number and an arrangement stage mark for the block; the coherence detection module analyzes data coherence during cross-chain request, and if the data coherence is lower than a threshold value, cross-chain verification based on matching of the mark and the batch number is triggered; the batch verification module executes repeatability verification on data in different stages of the same batch after verification is passed; and the traceability integration module integrates the verification data to generate a global carbon footprint traceability record and stores the global carbon footprint traceability record to a cross-chain shared account book.

The invention discloses an euphausia superba fishery change prediction method based on an ocean circulation-sea ice-species distribution coupling model, and relates to the technical field of ocean science. Euphausia superba fishery historical observation data, satellite remote sensing data and reanalysis data are collected, euphausia superba resources and environmental data are integrated, and the euphausia superba fishery change prediction method based on the ocean circulation-sea ice-species distribution coupling model is obtained. Constructing a polar region resource environment database covering total elements of physics-biology-environment; and based on an FVCOM model, constructing an ocean circulation-sea ice coupling model of a surrounding sea area of the peninsula of the south pole, and simulating and verifying physical environment characteristics of a target sea area. By constructing the ocean circulation-sea ice-species distribution coupling model, integrating multi-source data and analyzing a cooperative driving mechanism of a multi-scale physical process to euphausia superba resource change, compared with a traditional method, the change of an euphausia superba fishery can be simulated and predicted more accurately, and the method has the advantages of being high in practicability and the like. And the problem of inaccurate prediction caused by limited space-time coverage rate of observation data is effectively solved.

The invention provides an ocean intelligent forecasting large model construction method, and relates to the field of ocean forecasting, and the method specifically comprises the following steps: obtaining multi-source ocean observation data, and constructing a high-resolution ocean analysis data set which is subjected to quality control, space-time registration, standardization and data set division processing through multi-source observation data fusion and numerical mode assimilation; constructing a basic prediction model, and performing multi-scale fusion on the frequency domain enhanced features and the spatial local features by using the basic prediction model; the trained basic prediction model is operated in a set area range, and an output result of the basic prediction model is recovered to an original physical quantity value through inverse standardization; and comparing the rolling output of the basic prediction model with observation data or a high-resolution mode result through a correction module, learning an error, outputting a correction quantity, and superposing the correction quantity with an original prediction result to obtain a prediction field. According to the technical scheme, the problem that the ocean forecasting model in the prior art cannot meet the requirement of a complex application scene is solved.

The invention relates to the technical field of migration simulation, in particular to a drainage basin water pollution migration and transformation simulation method and system based on an MT-DHM model. The method comprises the following steps: carrying out data acquisition on a watershed multi-source space, carrying out data preprocessing, and generating a hydrological response unit standard data set; performing vertical hierarchical calculation based on the hydrological response unit standard data set to obtain land runoff vertical analysis data; carrying out land pollutant migration simulation on the land runoff vertical analysis data, and carrying out real-time correction with an iteration step length of 1 h to obtain a dissolved state / adsorption state load matrix; therefore, by constructing a full-process multi-source heterogeneous data fusion and pollution migration simulation mechanism, the problems that a traditional model is insufficient in data uniformity, low in parameter calibration precision and weak in spatial distribution simulation capability are solved, and the precision, stability and management and control operability of drainage basin pollution load calculation are improved.

The invention discloses a virtualized container environment monitoring device and method based on eBPF, and aims to solve the problems of poor isolation, insufficient security and platform adaptation deficiency in a virtualized container environment. According to the method, time sequence data of monitored events in a virtualized container environment are collected in real time through an eBPF technology, the time sequence data are transmitted to a data analysis module through eBPF mapping and message queues, data are analyzed through a long-short-term memory network, the deviation degree of the current time sequence data and historical data is calculated, abnormal behaviors are judged, and an alarm is given. According to the method, the user-defined program is operated under the condition that kernel codes do not need to be modified, intelligent analysis and abnormal behavior recognition are conducted on the collected data, comprehensive monitoring and dynamic detection of complex threats are achieved, the safety, stability and compatibility of the system are improved, and the method is suitable for virtual environments under cloud computing and big data scenes.

The invention discloses a building pedestrian simulation statistics and analysis method, and belongs to the technical field of simulation. According to the invention, a comprehensive pedestrian behavior evaluation and optimization scheme is provided through normal pedestrian circulation simulation and emergency evacuation simulation in combination with statistical analysis, data visualization and an optimization algorithm. The system manages pedestrian density through a KD tree, performs real-time data visualization by using ScottPlot, generates one-dimensional, two-dimensional and three-dimensional charts, and visually displays pedestrian distribution and flow conditions in a building. In an emergency evacuation scene, the system dynamically plans a path through a # imgabs0 # algorithm, and identifies and relieves a potential bottleneck area in combination with a heuristic function of congestion measurement. The simulation system can collect abundant statistical data including evacuation time, people flow distribution, congestion point analysis and the like, and provides a scientific basis for building safety design improvement and emergency plan making.

The invention relates to the technical field of photovoltaic power prediction, and discloses a photovoltaic power prediction method and system, and the method comprises the steps: obtaining the historical operation data of each photovoltaic station, carrying out the abnormal value elimination, expanding the sample data through a generative adversarial network, constructing a first training sample set, and carrying out the variable dimension reduction, screening principal component factors influencing the photovoltaic power to construct a second training sample set; calculating similar days by using the second training sample set, and screening and sorting; establishing a deep learning framework fusing the long short-term memory network, the maximum temperature prediction model and a parameter optimization algorithm, and based on a preset photovoltaic power prediction precision evaluation index, performing model training by taking the number of days of similar days and the weight as independent variables to establish a power prediction model; based on preset reanalysis data and weather forecast data, photovoltaic power prediction is carried out by using the trained power prediction model, precision evaluation and dynamic optimization of model parameters are carried out, and the photovoltaic power prediction precision and adaptability to different scenes are improved.

The invention relates to the technical field of enterprise management, in particular to an intelligent decision support system and method based on cognitive logic and scenarized semanteme, and the method comprises the steps: obtaining enterprise decision data through multi-source data monitoring, carrying out the preprocessing, and generating cross-modal enterprise scene cognitive information; analyzing cross-modal association among the data, fusing a cognitive logical reasoning rule and a semantic understanding model, and constructing scenarized semantic decision fusion features; training an enterprise decision-making quality evaluation model based on historical cases, performing quality perception on a current decision-making scene, and outputting decision-making quality information; and an execution effect is judged in combination with an expected target, an optimization mechanism is started if the target is deviated, candidate schemes are generated by using a historical knowledge base and a multi-target optimization algorithm, an optimal solution is screened, and intelligent adjustment of a decision scheme is realized. According to the method, multi-source heterogeneous data and cognitive logic are fused, semantic understanding, dynamic evaluation and adaptive optimization capabilities of a decision system are enhanced, and scientificity and real-time performance of enterprise decision are improved.

The invention discloses an intelligent sofa fabric defect detection system and method based on AI image recognition, and relates to the technical field of intelligent fabric defect detection.The system comprises a plurality of modules, an image acquisition module is combined with a multispectral camera, quantum enhanced imaging and adaptive optics are introduced, polarization and terahertz cameras are further arranged, the image quality is improved, and the detection accuracy is improved. Image preprocessing adopts deep learning adaptive filtering and the like; aI identification is combined with meta learning, a knowledge graph and data analysis, time sequence analysis is introduced, a VR / AR technology is adopted for result output, a reinforcement learning algorithm is adopted for feedback control, and fabric flaws can be comprehensively and efficiently detected. The system is high in detection precision, can capture flaws in all directions, greatly improves the detection efficiency, meets the requirements of large-scale production, can deeply analyze data, predicts the quality trend, achieves the real-time and accurate production regulation and control, sample management, remote monitoring and other modules, and brings remarkable benefits to enterprises.

The embodiment of the invention provides a code exception analysis method and device, a storage medium and a program product. In the embodiment of the invention, in the preset plug-in, the global knowledge graph is constructed through the logic syntax tree of the plurality of original business codes, so that intelligent anomaly analysis is carried out on the modified to-be-detected codes, multi-language and heterogeneous programming styles and complex dependency limitation are broken through, and cross-language and cross-file analysis is supported; change code snippets and associated influence code snippets thereof are accurately identified, and a data basis is tamped and analyzed; the analysis depth is deepened and the accuracy is improved through a multi-dimensional anomaly analysis large model and four-dimensional collaborative analysis in combination with an adaptive review rule and a knowledge graph; and finally, performing correction based on the generated repair scheme. Through the technical fusion of an MCP protocol, a multi-AI model, a syntax tree and a knowledge graph, the high efficiency, accuracy and real-time performance of code examination and repair are realized, and the service development quality and the system operation stability are remarkably improved.

The invention discloses a network space security risk intelligent identification method and system. The method comprises the following steps: deploying traffic collection equipment at key network nodes and boundary equipment, and collecting network traffic data and log data; extracting network behavior features and log key fields, and constructing a data feature model; performing vectorization processing based on the data feature model, performing traffic classification by using a deep neural network, and identifying a device type and a device identifier; carrying out noise reduction, redundant information removal and standardization processing on flow data in the data feature model to generate standardized data and storing the standardized data in a vector database; entity extraction is carried out on the standardized data, a knowledge graph is constructed, data distribution is analyzed through a Gaussian mixture model, the probability of abnormal behaviors is calculated in combination with historical data, and attack risks are calculated based on Bayesian decision; and generating an alarm according to the calculated attack risk, tracing the historical record of the attack risk in the knowledge graph, sending a blocking instruction, and identifying the security risk of the cyberspace.

The invention relates to the technical field of data processing, in particular to a computer feature extraction processing system and method fusing multi-source heterogeneous data, and the system comprises a structure correlation mapping module, a density mutation recognition module, a disturbance path analysis module, a path logic matching module and a risk logic output module. According to the method, by optimizing a data processing mode, important features in data can be accurately recognized, manual intervention and experience dependence are reduced, large-scale data analysis and processing efficiency is improved, the accuracy of a data model is enhanced, and by deeply analyzing the fluctuation amplitude and the change trend of the data, potential associated areas and abrupt change grids can be accurately positioned; key disturbance information is effectively identified, path logic matching can reveal a risk behavior mode in data evolution, potential early warning events are marked in time, the efficient processing capacity of the model for a complex data mode is ensured, and particularly, the problems of low efficiency and insufficient precision in a traditional scheme are solved in the face of large-scale data.

The invention discloses an enterprise digital management method based on data mining. The method comprises the following steps: acquiring information flow data and resource allocation data; analyzing according to the information flow data and the resource allocation data to obtain an initial network diagram; performing clustering analysis according to the initial network diagram, and performing path optimization to obtain a dynamic network diagram; performing feature extraction on the dynamic network diagram to obtain prediction analysis data; according to the dynamic network diagram and the prediction analysis data, network nodes are identified, and key nodes and high-risk nodes are obtained; in combination with the key nodes and the high-risk nodes, identifying a key propagation path to obtain the key propagation path; and based on the key nodes, the high-risk nodes and the key propagation paths, predicting key positions where resource bottlenecks and conflicts occur, and generating resource optimization suggestions. According to the method, the dynamic change of the implicit dependency relationship of the enterprise can be identified, high-risk nodes and key propagation paths are positioned, and resource configuration is optimized.

The invention relates to the technical field of centrifugal machine PID control, in particular to a centrifugal machine control method and system for soybean protein production. Obtaining time sequence data of the rotating speed and the torque of the centrifugal machine, analyzing data differences and fluctuation trends, calculating abnormal indexes of state parameters at all moments, accurately identifying abnormal data points and abnormal moments, and positioning rotating speed sudden change key nodes; determining an abnormal time period based on the time sequence characteristic of the abnormal moment, analyzing the relation between the rotating speed and the torque, the similarity of abnormal indexes and the rotating speed fluctuation characteristic in the time period, quantifying the dynamic relation between the two, and formulating an adjustment index; aiming at the problem of control lag caused by fixed parameters of the traditional PID, the PID parameters are dynamically adjusted by combining the abnormal index difference of the rotating speed and the torque at the current moment and the adjustment index determined in the early stage, so that the control strategy is adaptively optimized along with the change of the working condition, and finally, the stable, efficient and accurate control on the rotating speed of the centrifugal machine is realized.

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The embodiment of the invention discloses a level-to-level management strategy generation method and system for an electronic fence based on a 5G base station iron tower, and the method comprises the steps: collecting a multi-source heterogeneous monitoring data set covering an electronic fence warning region through sensor arrays disposed at different monitoring point positions of the 5G base station iron tower; the data comprise environment sensing data and target motion data; performing space-time correlation fusion processing on the data set to generate a basic analysis data set, determining a space relative relationship between the target motion data and the electronic fence boundary and potential influence of environment perception data on the motion behavior of the target object based on the basic analysis data set, and generating risk assessment information; a hierarchical management strategy is made according to the risk assessment information, strategy execution parameters are adjusted by continuously tracking a behavior evolution process of a target object in an electronic fence warning area to realize optimization iteration, and the accuracy and adaptability of electronic fence management are improved.

Methods and apparatus for generating problem fix verification procedures for varying corrections to defects are described. An example method includes obtaining problem analysis data associated with a defect of a first computing system. A first representational vector corresponding to the problem analysis data is generated. Second representational vectors corresponding to respective historical defects associated with one or more second computing systems are obtained. A set of historical problem fix verification procedures associated with the defect is determined, based on the first representational vector and at least one of the second representational vectors. A recommended set of problem fix verification procedures for verifying a correct to the defect of the first computing system is provided to a third computing system. The recommended set of problem fix verification procedures include the ranked set of historical problem fix verification procedures.