47 results about "Stability index" patented technology

The invention provides an intelligent layout method, system and device for a land survey control network and a medium, and the method comprises the steps: building a space-time dynamic terrain model based on the obtained measurement data of a construction region; generating a candidate control point set in the construction area according to a preset layout density requirement; based on the space-time dynamic terrain model, performing stability analysis on each candidate control point in the candidate control point set to obtain a stability index of each candidate control point; based on the space-time dynamic terrain model, any two candidate control points in the candidate control point set are used as candidate control point pairs for time sequence intervisibility analysis, and time sequence intervisibility indexes of the candidate control point pairs are obtained; establishing a multi-objective optimization model according to the stability index and the time sequence intervisibility index; and solving the multi-objective optimization model by adopting a multi-objective evolutionary algorithm, and outputting a control network layout scheme set. By adopting the method, the point distribution quality of the control network can be improved.

The invention relates to the technical field of program repair, in particular to an executable invariant and differential signal combined automatic program repair method, which comprises the following steps of: receiving a to-be-repaired program and a test set, and calling a large language model to generate a plurality of candidate patches; forming an entry behavior specification list according to the repair intention description; generating a plurality of executable invariant assertions and injecting the executable invariant assertions into the target function or the calling point, and generating an invariant assertion injection record table; generating a test case covering the boundary condition and the abnormal scene based on the large language model; collecting an execution signal when the candidate patch is operated; converting the patch difference into semantic editing features, calculating an editing stability index, and calculating an overall semantic consistency score of the candidate patches according to the support degree of the standard bar; and calculating an overall comprehensive score of the candidate patches, and outputting an optimal patch according to the overall comprehensive score of the candidate patches. According to the method, the proportion of overfitting patches can be effectively reduced, the patch repairing accuracy is improved, the interpretability is high, and the universality is good.

The invention relates to a servo hydraulic multi-working-condition parameter self-adaptive switching and correcting method. The method comprises the steps that system real-time data flow including real-time hydraulic pressure, executing mechanism speed and control response deviation is monitored and collected; identifying the current working condition based on the real-time data and indexing the reference control parameters; running a parallel response evaluation program to generate a dynamic stability indicator and determine an expected performance threshold; the dynamic stability index is compared with an expected performance threshold value, and when parameter mismatch is confirmed, a temporary compensation coefficient is generated to correct the reference control parameter in real time; and monitoring the correction offset, and updating the working condition-parameter mapping database when the aging judgment condition is met. The method has the advantages that manual parameter adjustment can be replaced, automatic switching of millisecond-level working conditions is achieved, the problem of parameter mismatching caused by equipment aging is effectively solved through a model self-evolution mechanism, and the control precision and stability of the servo hydraulic system in the whole life cycle are ensured.

The present invention discloses a method for evaluating production line stability based on a fault tree, comprising the following steps: obtaining multiple parameters of each device on the production line collected by sensors over a period of time, calculating the process capability index corresponding to each parameter, inputting the obtained process capability index corresponding to each parameter of each device into a pre-trained multiple linear regression model to obtain a stability index for the device, and inputting the stability index of each device into a pre-built fault tree-based stability analysis model to obtain a final stability judgment result for the device. The present invention can solve the technical problem that existing machine learning methods are complex for production lines, require long training times, and are unable to adapt to the real-time requirements of production environments in actual deployment and operation.

The application discloses a flexible circuit board production quality monitoring method and system based on data feedback, collects physical parameters such as line width, line spacing, hole diameter, roundness and copper thickness of developing etching, drilling and copper plating processes, and workshop temperature and humidity and dust concentration data, generates a single variable control chart for single process physical parameters, generates a multivariate T 2 Control chart for multiple coupling parameter processes, calculates process stability index and comprehensive fluctuation index, inputs abnormal data into a BP neural network, identifies abnormal mode types and outputs characteristic parameters, generates parameter correction instructions according to the abnormal mode and the characteristic parameters, adjusts equipment parameters in real time, recalculates the process capability index based on the adjusted data, triggers secondary feedback if the process capability index does not meet the standard, and dynamically adjusts the control chart threshold value according to the standard deviation and mean value of the process capability index of continuous batches. The application comprehensively covers key production parameters, discovers abnormalities in time, accurately and comprehensively detects, adjusts in real time to ensure stability and controllability, and improves product quality.

The invention discloses a bearing anti-gluing design method based on internal structure parameter optimization, and relates to the technical field of bearing design. The problems that an existing bearing anti-gluing design mainly depends on experience, collaborative optimization of internal structure parameters is lacked, and the gluing risk is difficult to quantitatively predict are solved. The method comprises the following steps: firstly, constructing a basic database containing key internal structure parameters and typical service conditions of a bearing, obtaining gluing failure characteristic data through test or numerical simulation, and calculating a quantitative index-gluing risk factor for comprehensively representing a gluing risk; on this basis, an optimization model with the goal of minimizing a gluing risk factor and maximizing a bearing rated dynamic load and an operation stability index is established, a multi-objective optimization algorithm is adopted to carry out collaborative optimization on key structure parameters, and finally an optimal parameter combination is selected from a Pareto optimal solution set. According to the method, the bearing gluing risk can be quantitatively predicted and effectively reduced in the design stage, and the operation reliability of the bearing under harsh working conditions is remarkably improved.

The invention discloses a grid division method and device for a simulation model, electronic equipment and a computer program product. The method comprises the steps that a three-dimensional scanning model of a to-be-simulated object is acquired, and the three-dimensional scanning model is subjected to grid division based on preset grid density; performing multi-physical field modeling on the three-dimensional scanning model to obtain a simulation model to be evaluated; performing simulation calculation on the to-be-evaluated simulation model, and determining calculation time for performing simulation calculation on the to-be-evaluated simulation model and a stability index for measuring a simulation calculation result; performing iterative adjustment on the preset grid density to obtain a target grid density which enables the calculation time to be not greater than a preset time threshold value and the stability index to be not less than a preset index threshold value; and determining the to-be-evaluated simulation model subjected to grid division based on the target grid density as a target simulation model. According to the method, the technical problem that the grid density cannot be accurately selected under the condition of multi-physical modeling in the prior art is solved.

The invention relates to the technical field of stacking and typesetting, in particular to an intelligent stacking and typesetting method and system based on a self-adaptive algorithm, and the method comprises the steps: respectively obtaining a product parameter set and a tray parameter set, and extracting the maximum length, the maximum width and the maximum height from the product parameter set to determine the division step length of a three-dimensional discrete space set; pre-screening coordinate points meeting a threshold value by the system, and inputting the coordinate points into a target function to generate an arrangement scheme set; introducing a support consistency index and a mass weighted average value to generate a unit stability mark set and an overall structure judgment mark; the system generates an operable product set in the arrangement scheme set by traversing the structure dependency relationship, and the user performs adjustment according to the operable product set; and the system screens the arrangement points conforming to the threshold value to form an execution task set and sorts the execution task set, corresponding robot execution vectors are constructed, and an instruction sequence formed by all the execution vectors is coded into a standard action file issued to a robot controller.

The invention provides a stability index management system for a container sign production process. The stability index management system comprises an index system construction module, a production data supervision module and a result display module, according to the method, a container sign production process stability evaluation index system is established, and a manager can know the whole production process more visually through real-time data acquisition and visual display of an analysis result, so that more visual and accurate decision support is provided for the manager, and efficient and stable operation of a factory is facilitated.

The invention provides a backfill process intelligent design method based on multi-dimensional data analysis and optimization control, which comprises the following steps: judging whether a material difference exceeds a preset threshold according to a classified material group, and if so, predicting non-uniform distribution of a soil body through a support vector machine model, and determining coordinates of a potential non-uniform region; side slope instability risk points are extracted from the coordinates of the potential non-uniform area, stress distribution under the backfilling technology is analyzed and simulated through finite elements, and a stress distribution map is obtained; for the stress distribution map, obtaining a material selection scheme, adjusting a material combination proportion through an optimization algorithm, and determining an optimized material matching sequence; generating an inner waste dump three-dimensional model through the backfill process parameters, and evaluating a long-term stability index by adopting a numerical simulation method to obtain a stability evaluation score; and optimized feedback data are extracted from the stability evaluation score, whether the long-term stability requirement is met or not is judged, and if yes, a final backfill process scheme is output to control settlement and slope instability.

The invention provides an evaluation method and device for a time-of-use electricity price making policy, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical load data in a reference period and to-be-evaluated load data in an evaluation period; the reference period and the evaluation period have homogeneous comparability; constructing an undetermined reference load curve and a to-be-evaluated load curve according to the historical load data and the to-be-evaluated load data; adjusting the to-be-determined reference load curve by using the load adjustment coefficient to obtain a reference load curve; and according to the user industry response characteristics and the reference load curve, analyzing the to-be-evaluated load curve, and calculating the following indexes: a net load transfer amount, a peak-valley optimization effect index, a response stability index and an industry differentiation evaluation matrix. According to the embodiment of the invention, the multi-dimensional reference load curve is constructed and the load adjustment coefficient is introduced, so that the interference of external factors such as natural fluctuation and seasonal change of the total power consumption on the evaluation result is effectively eliminated, the evaluation precision is high, and the anti-interference capability is high.

The invention relates to the technical field of gas leakage detection, in particular to an intelligent detection and early warning method and system for gas leakage, and the method comprises the steps: completing the division of monitoring sub-regions of a target region according to the pipeline layout and use scene of a target gas pipeline, and carrying out the unique identification of each monitoring sub-region; according to the method, the monitoring sub-regions are divided by combining the layout and the use scene of the target gas pipeline, the working condition characteristics of each region are extracted, the working condition types are defined, and the data acquisition parameters are adjusted in a targeted manner according to the working condition characteristics, so that the data acquisition is more targeted, the acquisition of invalid data is effectively reduced, and the effectiveness of the monitoring data is improved; the initial stability index is subjected to deviation correction through the working condition characteristic correction model, monitoring errors caused by working condition fluctuation are eliminated, and the stability index can better reflect the actual operation state of each area.

The application provides a device stability analysis method and system, a device and a storage medium, and comprises the following steps: acquiring an action time difference of the same action of different types of products of the same device; calculating a statistical value of the action time difference, wherein the statistical value comprises one or more of an average, a mode, a median and a standard deviation; deriving an action stability index according to the statistical value; and comparing the action stability index with a preset stability index to determine the stability of the device. By comparing the action stability index value with the preset stability index value, it is determined that the device is in a stable state, a relatively stable state or an unstable state, and corresponding maintenance work is performed according to the obtained device stability state result.

This invention provides a cloud service assurance method and system based on eBPF observable data and a prediction model. The method includes: collecting historical observable operational status data of the kernel, containers, and application layers and mapping and associating them; aligning and concatenating the associating data to generate a time-series feature vector; using the time-series feature vector to train and test the constructed model, calculating the residuals based on the predicted values ​​and corresponding actual observed values ​​during the testing period, and analyzing the residual probability distribution characteristics; using the model's prediction results for cloud services to calculate the residuals, and generating anomaly confidence scores based on the residual probability distribution characteristics; under the condition that the anomaly confidence score is greater than a threshold, analyzing the anomaly type and its impact range, then matching a remediation plan in the policy library and automatically executing the remediation plan; monitoring the execution effect of the remediation plan and feeding it back to the model; calculating a stability index based on key stability indicators to form a stability change curve, and then evaluating the execution effect of the remediation plan.

The present disclosure relates to a train operation method and device based on the coupler state, equipment, storage medium and computer program, the method comprising: initializing all gear discrete points in the discrete position domain to obtain a discrete gear sequence set, calculating an expected speed sequence according to the discrete gear sequence set, calculating a train coupler state sequence according to the pre-acquired coupler critical displacement and the discrete position domain sequence, calculating a train coupler state change sequence according to the train coupler state sequence, calculating a coupler state change index sequence according to the train coupler state change sequence, calculating a stability index sequence according to the coupler state change index sequence, calculating a speed following index sequence according to the expected speed sequence and a preset target speed sequence, calculating a comprehensive evaluation index sequence according to the stability index sequence and the speed following index sequence, selecting an optimal gear sequence in the discrete gear sequence set according to the comprehensive evaluation index sequence, and controlling the train to run by using the optimal gear sequence.

The invention discloses a report information acquisition method and system based on project cost management, and relates to the technical field of project cost management. The report information acquisition method based on project cost management comprises the following steps: acquiring a monitoring performance evaluation index; acquiring a transmission stability index; obtaining an early warning execution efficiency score; and obtaining report information. According to the method, whether data acquisition is started or not is determined by monitoring performance evaluation indexes; and then, according to the transmission stability index, whether fault early warning needs to be sent out is judged. And finally, comparing a preset value in a database according to the early warning execution efficiency score to determine whether to generate a report. The effect of improving the data transmission stability of the engineering cost report information in the collection and management process is achieved, and the problem that the data transmission stability is not considered when the engineering cost report information of a material warehouse in a construction site is collected and managed in the prior art is solved.

The invention provides a data management system based on data quality evaluation, and relates to the technical field of data management. According to the system, a data monitoring module intercepts a data packet, segments the data packet according to a time window and then transmits the data packet, an evaluation rule generation module calculates an instantaneous offset percentage and maps the instantaneous offset percentage into a credible mark, a turnover frequency is counted to obtain a stability index, and an executable vector is generated. And the real-time evaluation module compares the field value of the data packet with the vector boundary, and outputs a report if the boundary is exceeded. And the data governance module covers and marks the abnormal field with the boundary value and sends the abnormal field back to a downstream link. The feedback optimization module adjusts the stability index to optimize the evaluation rule. According to the method, the timeliness of abnormal data capture is improved, data qualification judgment is precise through three-level progressive judgment, the fault tolerance rate of quality evaluation is improved, self-optimization iteration of evaluation rules is achieved through a feedback mechanism, and the long-term operation adaptability of the system is enhanced.

A method includes obtaining an actual value of a condition affected by operating the building equipment, operating the building equipment using a setpoint for the condition, calculating a stability index based on a timeseries of errors between the actual value and the setpoint, comparing the stability index to a criterion, in response to the stability index satisfying the criterion, executing an action relating to the building equipment, and in response to the stability index not satisfying the criterion, not executing the action.

The invention provides an intelligent material sorting method and system, and the method comprises the steps: S4, matching a corresponding runner adjustment strategy from a preset dynamic regulation and control rule library according to the occurrence position and type of an abnormal state, optimizing a sorting path and an equipment scheduling scheme through a genetic algorithm, and obtaining an optimized material sorting sequence; s7, extracting processing records of similar scenes from the historical safety accident database according to the isolation state of the dangerous goods, evaluating the level of potential threats by adopting a decision tree algorithm, and determining the stability index of the overall operation of the system; and S9, according to the new regulation and control instruction, the working states of all the sorting devices are adjusted through a distributed calculation framework, the short-term change trend of material flowing is analyzed and predicted through a time sequence, and a stable operation scheme of the system in a high-load scene is obtained. The safety and efficiency of material sorting are greatly improved.

The invention discloses a multi-microgrid cooperative active support control method, and relates to the technical field of energy cooperative control, and the method monitors the operation state information in the continuous operation period of a power distribution system, and switches to a cooperative active support mode when the operation state continuously deviates from a preset allowed operation interval. A power withdrawal analysis time window is determined by constructing an operation quantity time sequence and combining stable interval judgment and change direction consistency analysis. Constructing a power retracement variable quantity delta P in a retracement stage, and forming a retracement consistency index Cret through difference integral analysis to perform retracement state evaluation; and constructing a retracement stability index Rsta in a consistent structure state to carry out rhythm stability evaluation. And when the withdrawal rhythm of the micro-grid is stable, constructing a collaborative support constraint quantity Uad, mapping participation depth limitation information into collaborative active support control parameters, and enabling a multi-micro-grid collaborative active support process to form a closed-loop control structure based on withdrawal behavior analysis.

The invention discloses a construction method and system of a non-abandoned inheritance atlas, and relates to the technical field of atlas construction. The construction method of the non-abandoned inheritance atlas comprises the following steps: acquiring work image data, identity information and skill interview voice information of a plurality of embroidery inheritors, inputting the work image data, the identity information and the skill interview voice information into a style clustering model to extract an attribute triple, and constructing an initial non-abandoned inheritance atlas; the method comprises the following steps: acquiring propagation behavior data of each embroidery inheritor, performing feature analysis on the propagation behavior data and an attribute triple to obtain a propagation traction index and a structure process stability index, and performing comprehensive analysis to obtain a skill inheritance index. According to the method, an embroidery non-perpetual inheritance atlas is generated, so that a structural connection relationship between an inheritor and a style category is established, an atlas node system with expression levels and comparability is constructed, and the precision and practicability of the atlas in structural expression are enhanced.

The application provides a kind of data governance system based on data quality evaluation, it is related to data governance technical field.The system data monitoring module intercepts data packet and transmits after cutting according to time window, evaluation rule generation module calculates instantaneous deviation percentage and is mapped as trusted mark, obtains stability index by statistical turnover frequency, generates executable vector.Real-time evaluation module compares data packet field value with vector boundary, and outputs report if it is out of range.Data governance module covers abnormal field with boundary value and marks, and sends back downstream link.Feedback optimization module adjusts stability index to optimize evaluation rule.The application improves the timeliness of capturing abnormal data, accurately determines the eligibility of data through three progressive determinations, improves the fault tolerance rate of quality evaluation, realizes self-optimization iteration of evaluation rule using feedback mechanism, and enhances the long-term operation adaptability of the system.

The present application is applicable to the field of financial services technology, and in particular relates to a method, device, equipment and medium for monitoring the stability of an online model of a financial system. The method extracts data type features from offline tag data to obtain target data characterized as a target floating-point number type, and based on at least one preset field type parameter, sequentially calls the same packaged sharding logic to shard the target data to obtain sharding fields of the corresponding field types, calculates the first data volume corresponding to each field type in the current cycle, and combines the second data volume corresponding to each field type in the cycle before the current cycle to calculate the first stability index between the two cycles. When the first stability index is less than the stability threshold, an abnormal alarm is generated. Based on the same packaged sharding logic, data sharding can be performed for different field types for subsequent data statistics, thereby helping to improve the efficiency of PSI calculation.

The invention relates to the technical field of foundation construction, in particular to a pile driver guiding method and system for foundation construction of various structures, according to the system and method, before a pile driver conducts foundation construction, a virtual model is built for a pile body structure, and a foundation construction target area is constructed based on geological survey data of all stratums in the foundation construction target area; a clamping guiding scheme, meeting the screening rule, of each stratum in the subsequent piling process of the current pile body is analyzed, and smooth transition of the clamping guiding scheme of each stratum in the piling process is ensured through quantitative evaluation of a flow stability index; through full-chain innovation of parametric modeling, pile body structure adaptation, stratum adaptation, process optimization and intelligent decision making, the problem that in traditional piling construction, different pile body structures are difficult to adapt to a clamping guiding scheme of a complex stratum is effectively solved, and the scheme transition risk during stratum switching is remarkably reduced; and the construction precision is improved from the millimeter level of the traditional process to the submillimeter level for control.

The embodiment of the invention discloses a task scheduling method and device, equipment and a medium. The scheme can comprise the steps of obtaining task information of a plurality of to-be-processed tasks; the task information at least comprises publisher information of each to-be-processed task; based on the publisher information of each to-be-processed task, adding each to-be-processed task to a corresponding queue of a preset level; the preset grade at least comprises a first grade and a second grade, and the priority of the first grade is higher than that of the second grade; acquiring system information of the task scheduling system; the system information at least comprises a stability index and a resource utilization rate index; based on the stability index and the resource utilization rate index, determining a scheduling rate for the queue of the preset level; and scheduling the to-be-processed tasks in the queue of the preset level according to the scheduling rate.

The application discloses a kind of leading instability machine group automatic marking method, system, device and medium, belong to electric power technical field.The method includes: obtaining input sample, judge whether input sample is transient instability sample, if yes, leading instability machine group is marked;For transient instability sample, obtain the power angle curve set of all generators {δ i (t)}, according to power angle curve set {δ i (t)} Similarity matrix ρ is obtained;Based on similarity matrix ρ, after AP clustering, get the homology machine group clustering result set G AP ={G1,G2,…,G K};According to homology machine group clustering result set G AP Marking result is obtained.The application is dynamically obtained after each generator fault, and clustering mode is automatically carried out generator homology grouping, and the most leading machine group is regarded as leading instability machine group, and leading instability machine group of mass sample can be marked spontaneously and in batches, to provide fine-grained generator level stability index for data-driven stability evaluation model.

The invention belongs to the technical field of blast furnace ironmaking data processing, and discloses an ironmaking data sampling method fusing time decay and multi-parameter stability, and the method comprises the following steps: firstly, carrying out dual quality filtering on ironmaking historical data; partitioning according to time, and retaining the latest block data; and calculating a furnace condition stability index based on the average value of variable coefficients of multiple process parameters, obtaining a comprehensive weight in combination with a time attenuation coefficient, and periodically and dynamically updating a sampling strategy. The method overcomes the defect that the existing method is not combined with the characteristics of the blast furnace ironmaking process, improves the representativeness of the sampled sample, adapts to data mining and modeling requirements, is simple and convenient to implement, and can be used for data preprocessing of the full life cycle of the blast furnace.

The present application relates to the technical field of gas leakage detection, and particularly relates to a gas leakage intelligent detection and early warning method and system, comprising: according to the pipeline layout and use scene of a target gas pipeline, completing the division of monitoring sub-areas of a target area, and uniquely identifying each monitoring sub-area. The present application divides monitoring sub-areas by combining the layout and use scene of the target gas pipeline, extracts the working condition characteristics of each area and defines the working condition type, adjusts the data collection parameters according to the working condition characteristics, makes the data collection more targeted, effectively reduces the collection of invalid data, and improves the effectiveness of the monitoring data; the initial stability index is corrected for deviation by a working condition characteristic correction model, which eliminates the monitoring error caused by working condition fluctuations, and makes the stability index better reflect the actual operation state of each area.

The invention discloses an AVC adjustment effect evaluation method, device and equipment, a storage medium and a product thereof, and the method comprises the steps: obtaining real-time multi-source operation data after AVC adjustment of a power grid, and a preset evaluation rule which is a standardized parameter configuration scheme formed based on a multi-index detection rule and a weight coefficient; based on the multi-source operation data and evaluation rules, multi-dimensional index detection is carried out on the power grid after AVC adjustment, an index result is obtained, and multi-dimensional indexes at least comprise one of safety indexes, stability indexes and economic indexes; and based on the index result, evaluating the AVC adjustment effect to obtain an evaluation result. Namely, the whole evaluation process takes standardized rule driving, real-time operation data as input and multi-dimensional indexes as criteria, and the whole process from data acquisition to overall effect judgment can be automatically completed on the premise that manual intervention is not needed, so that the accuracy of the AVC on evaluation after power grid regulation is improved.

The present application relates to the technical field of virtual power plant operation control and resource aggregation analysis in new power systems, in particular to a resource aggregation correction updating and effect evaluation method for virtual power plants, which comprises obtaining real-time operation state information, prediction information and safety risk information of various resources in the virtual power plant, and establishing resource operation boundaries; according to resource state quantity, price signal, prediction deviation and disturbance risk, the operation boundaries are online corrected; according to the corrected boundaries, the feasible output set of various resources is constructed, the feasible region is aggregated, the available space is calculated by combining the current reference power to adjust the available space up and down, and the global aggregated feasible region is updated; according to the two-way adjustment space and the aggregated output dynamic response process, the equivalent adjustment capacity, response speed and stability index are calculated to complete the aggregation effect evaluation. The present application can realize dynamic characterization of virtual power plant aggregation adjustment capacity, real-time updating of feasible region and quantitative evaluation of aggregation effect.