Motion timing and process statistical algorithm services
For continuous images, sensing sequences and equipment process data, it provides sample evaluation and project adaptation for motion estimation, target correlation, signal smoothing, period and spectrum analysis, change detection, online statistics and anomaly scoring. The capability scope covers 20 timing and process analysis modules, which can be used in combination according to data, working conditions and business decisions.
Applicable objects and task boundaries
Suitable for enterprise projects where cameras or sensors continuously collect data and need to extract status from time changes, such as target position smoothing, workpiece motion detection, equipment status trends, repetitive process cycles, peak events, waveform similarity and multivariate deviation analysis.
- Estimating 2D displacements, foreground changes, or areas of motion on consecutive images.
- Perform inter-frame one-to-one correlation and position state estimation on the detection results.
- Perform smoothing, derivation, spectral, time-frequency and periodic analysis of equally spaced numerical sequences.
- Online updates of process means, covariances, trends, and change points.
- Output distance, residual, or cumulative change scores based on confirmed normal samples.
- Outputs index, time and quality status for peak, envelope and event positions.
The conversion of two-dimensional image displacement into space velocity requires geometric and time calibration; the identity, fault category, quality defect and hazard level require corresponding labels, mechanisms or process rules, and are verified by project data.
Input, output and status definitions
| Project | Main content | Must be fixed in the project |
|---|---|---|
| image sequence | Continuous frames, detection points, candidate boxes or regions | Frame order, exposure, field of view, scale, timestamps and frame drop rules |
| Numeric sequence | Univariate or multivariate observations, process quantities, characteristics | Units, sampling rates, missing values, resampling, and valid intervals |
| Baseline data | Normal samples, training periods, control limits | Working condition coverage, pollution elimination, version and update conditions |
| Movement output | Point displacement, displacement field, foreground mask, trajectory status | Pixel or physical units, coordinate systems, confidence and failure states |
| Timing output | Smooth value, derivative, spectrum, period, similarity, peak position | Windowing, frequency resolution, boundary handling and time base |
| Statistical output | Mean, covariance, variance, and anomaly scores | Threshold sources, statistical denominators, alarm hysteresis and reset rules |
The interface should return calculation results, timestamps, parameter versions, data quality and status codes at the same time. When the input is interrupted, the sample is insufficient, the time sequence is reversed, the parameter is out of bounds or the baseline is invalid, a clear status should be output and the current measurement should not be replaced by the last result.
Technical route and key points for selection
| mission route | Common methods | suitable conditions | Main limitations |
|---|---|---|---|
| Location estimation and correlation | Kalman filtering and one-to-one matching | Already have position measurement, cost definition and time sequence | Invalid measurements need to be processed separately, and the correlation results need to be combined with business objects. |
| Image movement and foreground | Sparse or dense optical flow, background separation, frame difference | Consecutive frames with definable texture, camera and background conditions | Lighting, occlusion, camera shake and initialization can affect output |
| Smoothing and outlier handling | Savitzky-Golay, EWMA and Hampel | Sampling rules are clear, noise and mutation caliber can be distinguished | Smoothing introduces latency and may also weaken real mutations |
| Spectrum, period and time frequency | Welch spectrum, short-time Fourier, autocorrelation, RMS and peak | The sampling rate and observation duration meet the target frequency analysis | Equipment status needs to be explained based on long-term data and operating conditions |
| Changes and Waveform Comparison | CUSUM, dynamic time warping and recursive regression | Have a reference process, baseline, or interpretable independent variable | Parameters and thresholds need to be re-verified when the working conditions change. |
| Online statistics and anomaly scoring | Online mean covariance, Mahalanobis distance and PCA residuals | There are independent normal data and stable characteristic definitions | The score is used to identify deviations from the relative baseline, and the fault category needs to be established separately. |
Typical links are: clock and data quality check → image or signal preprocessing → feature or motion calculation → smoothing and status update → scoring or event rules → status, log and result output. The algorithm combination needs to avoid repeated filtering, information leakage and the use of acceptance data for parameter adjustment.
Data and collection requirements
Sequence data needs to retain the original sampling order, monotonic timestamp, sampling rate or actual time interval, and record downtime, model change, parameter adjustment, frame loss and manual intervention. When there is only a frame number without a reliable clock, it is impossible to output traceable true frequency, speed or event delay.
Representative data should cover normal operating conditions, starts and stops, speed changes, load changes, lighting changes, occlusions, camera shake, short interruptions, abnormal events and recovery processes. The abnormal scoring project also requires a normal set aside from the parameter adjustment data, as well as abnormal or risk samples confirmed by the business.
If you need to convert physical displacement or velocity from pixel displacement, calibration, imaging geometry and time base should be provided; if equipment alarm is needed, process status, alarm definition, allowable delay, false alarm handling and reset rules should be provided.
Verification methods and applicable conditions
The project prepares real continuous data according to the task, and verifies displacement, trajectory, frequency, period, event and anomaly scores respectively. The data set covers normal operating conditions, starts and stops, speed and load changes, short interruptions, environmental disturbances and confirmed abnormal events.
Motion errors, event detection, false alarm rates, detection delays, processing beats, resource usage and recovery strategies are tested on the target platform and recorded along with data windows, thresholds, working conditions and software versions.
Project implementation and delivery
- Task and clock confirmation: Clarify the business meaning of events, trends, speeds, cycles or anomalies, and check sampling links and time benchmarks.
- Data baseline establishment: Separate development, validation and independent acceptance data, documenting conditions, labels, missing data and boundary samples.
- Algorithm combination and interface adaptation: Fixed preprocessing, windows, thresholds, state machines, result fields, logs and parameter versions.
- Independent data verification: Check errors, detections, false positives, delays, recovery, time consumption and resource usage, and retain sample-by-sample results.
- Equipment joint debugging and regression: Retest the agreed equipment and collection links, and establish version, parameter and baseline change records.
Delivery may include algorithm programs or call libraries, interface and status code descriptions, parameter configurations, sample and result lists, verification reports, and version notes. Sensor selection, acquisition hardware, PLC logic, equipment safety loops, fault diagnosis knowledge base and third-party platform interfaces are performed within the scope of confirmation.
Acceptance method
| Acceptance tasks | Optional indicators | Conditions that must be recorded |
|---|---|---|
| Motion estimation and tracking | Displacement error, loss of tracking rate, rejection rate, recovery time | Coordinate system, scale, frame rate, occlusion and matching aperture |
| Spectrum and Period | Frequency error, amplitude or energy error, period error | Sampling rate, window, observation duration, detrending and frequency range |
| events and changes | Event-level precision, recall, false positive rate, and detection latency | Event definitions, matching windows, continuous event merging and statistical denominators |
| Exception score | Normal false positives, anomaly detection, threshold sensitivity and drift | Training set, holdout set, working case stratification, feature version and threshold source |
| Run online | Single time consumption, update rate, memory, interruption and restart recovery | Target platform, data dimensions, concurrency, running time and log version |
Acceptance thresholds are determined by a combination of business risk, sample distribution, and target devices. Frame-level, event-level, and device-level metrics cannot be mixed; development data results cannot replace independent acceptance data results.
Authorization, maintenance and changes
Run licenses can be provided based on agreed devices, scenarios, data sources and versions. Continuous services can cover original scenario troubleshooting, parameter review, regression verification and controlled updates; newly added sensors, sampling rates, working conditions, product families, event types or fault tags fall within the scope of change assessment.
Source code, platform adaptation, equipment safety loops, data annotation and new diagnostic models are listed separately in the contract. Baseline updates, threshold changes, and model retraining retain approval, version, and rollback records.
FAQ
Can optical flow output be directly converted to device speed?
Only after imaging geometry, pixel scale, timestamps, and camera motion effects have been confirmed can physical velocities in the agreed direction be converted. 2D optical flow itself is not a 3D velocity measurement.
Can abnormality scores directly determine the fault type?
cannot. Distance, reconstruction residual, or cumulative change are used to describe deviations from a baseline. Failure types require labels, mechanism or process rules, and are verified by independent data.
Can a project be carried out with only normal data?
A normal baseline and preliminary score can be established, but a complete abnormality detection rate cannot be given based on this. During acceptance, it is necessary to clarify the verifiable range of normal false alarms, drift and known disturbances, and formulate a supplementary plan for abnormal samples.
Why do sample rates and timestamps have to be explicit?
Frequency, speed, event latency, and window length all depend on the time base. Uneven sampling, dropped frames, or time reversal will change the calculation meaning and need to be corrected first or invalid status returned in the interface.
Define the Development Scope with Samples and Device Requirements
Motion timing and process statistical algorithm services
Please describe the task object, original picture or video, target indicators, device platform, interface and sample situation.
Phone: 13910119357xuzhiyang0928@gmail.com