38 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 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 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 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.

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 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.

The application relates to a servo hydraulic multi-working-condition parameter adaptive switching and correction method, which comprises the following steps: monitoring and collecting system real-time data streams containing real-time hydraulic pressure, actuator speed and control response deviation; identifying the current working condition and indexing the reference control parameter based on the real-time data; running a parallel response evaluation program to generate a dynamic stability index and determine an expected performance threshold; comparing the dynamic stability index with the expected performance threshold, generating a temporary compensation coefficient to real-time correct the reference control parameter when it is confirmed that parameter mismatch occurs; monitoring the correction offset, and updating the working condition-parameter mapping database when the aging judgment condition is met. The application has the advantages of replacing manual parameter adjustment and realizing millisecond-level automatic switching of working conditions, effectively solves the parameter mismatch problem caused by equipment aging through a model self-evolution mechanism, and ensures the control precision and stability of the servo hydraulic system in the whole life cycle.

The invention discloses a production equipment scheduling optimization method and system, and relates to the field of industrial program control, and the method comprises the steps: extracting task cycle execution data from a historical task execution data table, obtaining the planned completion time and the actual completion time, calculating the phase deviation of each task cycle, carrying out the statistics, obtaining a phase deviation sequence, and carrying out the calculation of the phase deviation sequence; calculating the standard deviation; a deviation trend coefficient is determined, the aging stability index of each device is calculated in combination with the standard deviation, aging grading is carried out on the devices, and an effective time window is set; in the effective time window, constructing a task dependency graph and carrying out topological sorting on the task dependency graph to obtain a task priority sequence so as to calculate a scheduling anchor point moment for each task; determining a time buffer band width of each task on the corresponding candidate device to determine an expected completion time; and for each task, determining equipment which enables the expected completion time to be minimum and meets the scheduling anchor point moment constraint from the candidate equipment for allocation, and generating a scheduling scheme.

The invention provides a portal frame quality detection method and system, and relates to the technical field of portal frame production, and the method comprises the steps: obtaining the process link data of each key part in the production process of a forklift portal frame, and the process link data comprises the welding link data and the assembly link data; performing anomaly detection on the link data of each process to obtain a link anomaly degree value; according to the link abnormal degree value and the link weight corresponding to the link abnormal degree value, stability analysis of different use modes is carried out to obtain a stability index; and according to the stability index, determining the qualification of the door frame quality so as to complete door frame quality detection. According to the detection method and system provided by the invention, the quality evaluation efficiency of the door frame after production can be effectively improved, and the reliability of a door frame quality qualification detection result is ensured.

The application relates to the technical field of intelligent control, and discloses an intelligent control method and system for a mold production process. The method comprises the following steps: collecting material characteristic distribution data of a plate material, and constructing an initial material characteristic map; simulating the deformation behavior of the plate material under a forming load, and establishing a deformation behavior model; if it is detected that the stress exceeds a preset threshold value in the deformation behavior model, adjusting simulation parameters, obtaining a preliminary mold pose correction value, integrating a real-time coupling algorithm to calculate a dynamic pose parameter, generating an adaptive optimization path of the mold, and evaluating feasibility; processing the adaptive optimization path through a neural network to generate a refined mold pose sequence, and driving an actuator to adjust the position and posture of the mold in real time, monitoring the plate material forming process to determine a quality stability index; if the index shows that defects are reduced, recording the current real-time coupling configuration, and obtaining a control parameter configuration set. The application improves the control accuracy of the mold in the plate material forming process.

The invention discloses a human-post dynamic matching system and method based on a digital trajectory, and relates to the technical field of human resources. According to the method, a behavior stability index Ks of an operation behavior is established based on a behavior trajectory data sequence, and the behavior stability index Ks is preliminarily compared and evaluated; and the system can automatically identify the rhythm fluctuation trend of the personnel in the post process execution process. When the behavior stability index Ks exceeds a preset threshold value, it can be judged that the behavior process is abnormal in real time, and a process structure depth detection module is triggered. The mechanism breaks through the limitation of judging the flow abnormality by depending on single residence time or the number of events in the prior art, realizes statistical stability monitoring of the time interval of the operation event, and enables the system to recognize the problems of operation rhythm abnormality, behavior jitter, node delay and the like in advance, thereby improving the sensitivity and accuracy of flow behavior monitoring.

The invention relates to the technical field of intelligent control, and discloses an intelligent control method and system for a mold production process. The method comprises the following steps: collecting material characteristic distribution data of a plate material, and constructing an initial material characteristic map; the deformation behavior of the plate under the forming load is simulated, and a deformation behavior model is established; if it is detected that stress exceeds a preset threshold value in the deformation behavior model, simulation parameters are adjusted, a preliminary mold pose correction value is obtained, a real-time coupling algorithm is integrated to calculate dynamic pose parameters, an adaptive optimization path of the mold is generated, and feasibility is evaluated; the adaptive optimization path is processed through a neural network, a refining mold pose sequence is generated, an actuator is driven to adjust the position and the pose of the mold in real time, and the plate forming process is monitored to determine a quality stability index; if the index display defects are reduced, current real-time coupling configuration is recorded, and a control parameter configuration set is obtained. The control accuracy of the die in the plate forming process is improved.

The application discloses a kind of digital construction management systems, belong to marine engineering construction technical field.The system includes data acquisition module, collaborative evaluation module, exclusive adaptation evaluation module and decision execution module.Data acquisition module real-time collection marine engineering under construction project resource state data, process execution data and exclusive process parameters;Collaborative evaluation module is based on resource conflict and waiting time calculation multiple project collaborative stability index;Exclusive adaptation evaluation module calculates exclusive process adaptation load according to welding deformation, process lag and load deviation and other parameters;Decision execution module generates resource rescheduling scheme or process optimization scheme automatically when the index is lower than the first threshold or the load is higher than the second threshold.The application is aimed at the scene of marine engineering multiple project parallel, exclusive process complex, realizes the quantitative early warning and automation decoupling of resource conflict, and the accurate control of process deviation, effectively improves construction efficiency and quality safety level.

This invention relates to the field of flood forecasting technology, specifically a method and system for real-time correction of flood forecasting errors. The method includes acquiring data under a unified time reference, calculating net head and instantaneous flow rate; querying theoretical efficiency, calculating measured efficiency, and performing difference calculations to obtain the electro-hydraulic efficiency residual; evaluating the electro-hydraulic efficiency residual to obtain a stability index; determining the operating phase and calculating the confidence level of the operating phase based on the stability index; generating a correction vector and correcting the uncorrected process line to obtain a corrected process line; projecting the corrected process line according to a preset engineering feasible region to obtain a first corrected output; performing online verification and incremental correction on the first corrected output to obtain a final corrected output; and updating the model's initial state and boundary conditions for the next time window based on the final corrected output. This invention overcomes the error accumulation problem caused by phase misjudgment under hysteresis effects and field disturbances in traditional methods, significantly improving the real-time accuracy of flood forecasting.