12 results about "Baselining" patented technology

The invention relates to the technical field of computers and automation, and discloses a whole-station equipment information integration and SCCD baseline construction method, which comprises the following steps of: firstly, defining a whole-station comprehensive configuration description (SCCD) model embedded with a multi-dimensional constraint verification mechanism for uniformly describing various equipment information; secondly, compiling and generating a logic self-consistent SCCD baseline model based on a high-level policy intention model (PIM); meanwhile, equipment operation configuration is collected in real time to generate an SCCD mirror image model; then, performing dual verification diagnosis, including performing configuration drift comparison on the baseline model and the mirror image model, and performing logic violation analysis on the mirror image model by applying an embedded constraint rule; and finally, according to the diagnosed configuration drift and logic violation, a repair action is automatically generated and executed, and self-healing closed-loop control of the configuration is realized. According to the method, through unified modeling, intention driving and closed-loop self-healing, continuous consistency, accuracy and reliability of full-station configuration are ensured.

The invention relates to the technical field of test design, in particular to a key factor mining-based accommodating and arranging condition optimization method, medium and equipment, which are characterized in that accommodating and arranging conditions are generated by analyzing a target text, an initial simulation queue is constructed in combination with database retrieval, and baseline feature distribution is analyzed, so that representative research crowds can be obtained without real object recruitment; the test cost is reduced; a panoramic object feature matrix is constructed by integrating multi-dimensional data, the limitation of a single feature is broken through, and comprehensive data support is provided for key factor mining; the contribution degree of each characteristic variable to the preset key outcome is analyzed based on the matrix, and the key characteristic variables are positioned, so that the experience driving is changed into the data driving, and the scientificity and the pertinence of the accommodating and arranging condition design are improved; the accommodating and arranging conditions are dynamically adjusted through the closed-loop optimization process, it is ensured that the target accommodating and arranging condition set gives consideration to queue representativeness, feature pertinence and distribution rationality, and the design scientificity and success rate of clinical tests are improved.

The invention discloses a double-index measurement method and related equipment for reuse efficiency of a basic development framework, and the method comprises the steps: obtaining the business development workload, baseline workload and newly-added workload of a target project, and calculating the reuse net saving amount and a reuse efficiency index; obtaining a business code line number and a frame component code line number of the target project, obtaining a component comprehensive score, mapping the component comprehensive score into a conversion weight according to a preset mapping interval and a mapping parameter, and further calculating a code conversion reuse rate index; and combining the multiplexing efficiency index and the code conversion multiplexing rate index to determine a multiplexing efficiency measurement result, and calibrating the mapping interval and the mapping parameter through historical data. According to the embodiment of the invention, the authenticity and rationality of the multiplexing efficiency measurement result can be effectively improved by calculating the dual-measurement index and calibrating the code conversion process based on the historical data. The method can be widely applied to the technical field of multiplexing performance evaluation.

The invention relates to the technical field of software engineering and software quality assurance, and provides a method and a system for evaluating influence of quantitative code change on a program implementation effect. The method comprises the following steps: acquiring code difference information between a to-be-tested code branch and a baseline branch, analyzing and extracting structured information of a changed code through a large language model, and calculating a basic influence value of the structured information of the changed code; calling a program behavior feature library based on the structured information of the change code, and calculating a dynamic adjustment coefficient; calculating the final influence score of each dimension according to the product of the basic influence value of the structured information of the change code and the dynamic adjustment coefficient; the final influence score of each dimension comprises a core function influence score, a performance efficiency influence score, a data consistency influence score, an interface compatibility influence score and a security influence score; and generating an influence analysis report according to the final influence score of each dimension. According to the method, the basic influence value determined by the change type is multiplied by the dynamic adjustment coefficient determined by the module criticality and the historical risk, so that the code change influence is accurately and dynamically quantified.

The invention discloses a program performance prediction method based on code graph structure fusion and feature engineering enhancement, and aims at solving the problem that a traditional performance analysis tool is low in efficiency due to dependence on dynamic execution. According to the method, a static analysis framework named GFTrans is constructed, the running time of the code can be predicted without running the code, and then the performance bottleneck of the code is positioned. The method is characterized by comprising the following steps: extracting a control flow diagram and a data flow diagram through static analysis, and encoding the control flow diagram and the data flow diagram into a unified semantic sequence by adopting an embedding technology based on anchor point marking; and meanwhile, six-dimensional core statistical features are designed and fused with the semantic features by utilizing a dynamic gating mechanism, so that the code structure complexity and the behavior pattern are comprehensively captured. On a high-quality data set constructed based on a real GitHub project, GFTrs are superior to a traditional baseline model at the accuracy rate of 78.64%, and single reasoning only needs 12 milliseconds, so that real-time performance early warning and accurate bottleneck positioning in a development stage are realized, and the software optimization efficiency is remarkably improved.

The application discloses a distributed multi-version upgrade method and device, equipment and medium, and relates to the technical field of distribution. The method comprises the following steps: in response to a version upgrade operation event of a target object, metadata of the target object is marked, multi-modal verification and incremental extraction are performed, version upgrade data and a version upgrade type of the target object are obtained, wherein the target object comprises system data, a second-opening script and application source code; a baseline version of the target object is obtained from a multi-modal version database according to a version identifier in the version upgrade data; a target version of the target object is determined according to the version upgrade data and the baseline version based on the version upgrade type; compatibility verification is performed on the baseline version and the target version, a compatibility evaluation report of the target version is obtained, and the target version is deployed according to the compatibility evaluation report. The technical scheme has the advantages of improving the reliability and efficiency of system version iteration.

The invention relates to a GPU progressive optimization method for neural operator backbone network reasoning, and belongs to the technical field of electronic information. Comprising the steps of S1, initializing constraints and tolerances; s2, baseline measurement is carried out to obtain baseline execution time and a reference result; s3, obtaining a pre-estimated segmentation value based on the working set model and expanding the pre-estimated segmentation value; s4, carrying out KSweep evaluation and consistency check, and carrying out optimization; the process of scanning and measuring the candidate set one by one is called KSweep; s5, fixing, newly adding fusion reasoning optimization, carrying out consistency verification, if the consistency verification is passed, outputting and storing optimal configuration, and directly loading and executing in an operation stage; otherwise, returning or adjusting and recalibrating, and returning to the step S3. According to the method, the multiplexing distance between adjacent layers is shortened, and the round-trip overhead of intermediate activation between the video memory and the cache is reduced, so that the reasoning efficiency is improved.

The invention relates to an organizational structure change influence evaluation and optimization method and system, and belongs to the technical field of organizational management, and the method comprises the steps: obtaining multi-source heterogeneous data of an organizational structure, mapping entity elements in the data into nodes through a vertical field large model, mapping a business coupling relation into spring edges connected with the nodes, and carrying out the optimization of the multi-source heterogeneous data; constructing a baseline net rack representing the initial state of the tissue; the system constraint is analyzed, unchangeable spring edges are marked in the net rack, and safety boundaries are delimited for the other spring edges; identifying a to-be-adjusted area based on the service attribute state of the spring edge, and generating candidate adjustment operation in the security boundary; verifying the transient net rack after the operation is executed, wherein the verification comprises the steps of performing increment balance solution based on multi-agent simulation in a local range and synchronously verifying system constraints; performing compliance reinforcement processing on the net rack passing the verification to generate a steady-state net rack; and evaluating the change of the steady-state net rack relative to the baseline net rack, and outputting an executable optimization scheme in combination with implementation constraint.

The invention discloses a full-link quality control baseline accurate generation method and system, relates to the technical field of link control, and is used for solving the problem of insufficient consistency between special check results. The method comprises the steps of generating a target scheme object in combination with an engineering implementation stage and a construction activity scheme, performing structured analysis and disassembly on the target scheme object to form construction elements, identifying construction stage attributes and technical attributes of the construction elements to complete classification, and scheduling a corresponding special checking method according to a classification result. And checking the construction elements and generating a checking state, and evaluating the quality available characteristics of the target scheme object in the current implementation stage according to the checking state, so that the construction scheme generates a stable quality control baseline in the corresponding implementation stage, and the construction scheme provides clear quality reference output in different implementation stages.

A device may receive a machine learning model, training data, and test data, and may perform a unit test on the machine learning model to generate unit test results. The device may perform regression tests on the machine learning model, with the training data and the test data, to calculate model scores, create graphs, determine inference delays, and identify missing points for the machine learning model. The device may perform scale and longevity tests on the machine learning model, with the training data and the test data, to identify additional missing points and calculate a resource utilization for the machine learning model. The device may update the machine learning model, to generate an updated machine learning model, based on the unit test results, the model scores, the graphs, the inference delays, the missing points, the additional missing points, or the resource utilization.

Controlling physical entities by re-baselining protocol data includes simulating one or more activities of a physical environment based on a digital twin model of the physical environment. Based on the simulation, an anomaly associated with the one or more activities executed by each physical entity of the plurality of the physical entities is identified. The protocol data is updated based on the identified anomaly to control at least one of the plurality of entities. Virtual environment data is generated for the physical environment rendered on a user device to facilitate workers to adjust based on the updated protocol data.

Computer-implemented methods for predicting baselines of time- series data are provided. The methods can include: using processing circuitry to generate a compact learned representation (CLR), calculating a similarity score; and using the processing circuitry to calculate a similarity score between a target CLR and a plurality of historical CLRs. Systems for dynamic baselining are also provided that can include: a data storage repository; a processing circuitry; and memory storing instructions that, when executed by the processor, cause the system to perform a computer-implemented method. Systems and methods for operating a building in the built environment are also provided, the systems and methods can include: a data storage repository; processing circuitry; and memory storing instructions that, when executed by the processor, cause the system to use the processing circuitry to generate a compact learned representation (CLR) for a time interval from a raw time-series data profile that includes External Environmental Data and Internal Environmental Data.