1928 results about "Data point" patented technology

The invention discloses an intelligent definition method and system for industrial edge data acquisition, a medium and equipment. The method comprises the following steps: acquiring operation data of field equipment in real time; performing protocol analysis and cleaning on the data to generate standardized data points, dynamically detecting the communication state and adaptively adjusting the acquisition frequency; classifying and aggregating the standardized data points into a running state feature set and calculating feature indexes; edge side anomaly detection is carried out based on the feature set, and a protocol container library is synchronously called to match a device communication protocol to generate matching information; an acquisition strategy optimization instruction is generated in combination with the abnormal result and the matching information, and transmission characteristic indexes and instructions are packaged; and structuring storage feature indexes according to equipment types and time dimensions to form a historical operation database, and managing a storage period by adopting a sliding window mechanism. According to the invention, protocol adaptive analysis, dynamic acquisition adjustment and edge intelligent analysis are realized through software definition, the hardware dependence and field debugging risk are reduced, and the data acquisition efficiency and the intelligent level are improved.

The invention discloses a multi-modal fused intelligent question answering and knowledge retrieval method and system, and the method comprises the steps: building a multi-modal data index model oriented to a heterogeneous knowledge source, carrying out the feature mapping of text, image, table, chart, audio and video contents through a unified semantic embedding space, and generating a cross-modal index set; after a query request is received, performing semantic matching and structure matching on the cross-modal index set by using a multi-channel retriever to obtain candidate evidence fragments; and based on an evidence granularity decomposition strategy, performing minimum evidence unit division on text statements, table units, chart data points and multimedia frame contents in the candidate evidence fragments, and establishing a semantic consistency graph among the units. According to the method, high-credibility traceable generation of question and answer results is realized through multi-modal fusion and space-time consistency constraint, and the retrieval precision and interpretation transparency in a complex knowledge scene are remarkably improved.

The invention discloses a welding seam track extraction method and system based on RANSAC parameter fitting, and the method comprises the steps: S1, obtaining three-dimensional point cloud data, and obtaining the normal vector and local curvature of each point cloud data point; selecting an initial seed point from the three-dimensional point cloud data according to the local curvature of each point cloud data point; s2, establishing a feature similarity evaluation system, and adding the initial seed points and the corresponding similar adjacent points into the same plane; according to different planes where the point cloud data points are located, plane area coarse segmentation is carried out; and S3, a plurality of geometric fitting models are constructed for the welding track based on the RANSAC algorithm, the geometric fitting model with the highest matching degree is selected from the geometric fitting models, and the welding track in the welding area is extracted. According to the technical scheme, the welding seam track can be rapidly and effectively extracted, the universality of welding seam track recognition is improved, and the extraction error of the welding seam track is remarkably reduced.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention relates to the technical field of industrial internet, and discloses a multi-dimensional data integration and dynamic risk assessment method for enterprise purchase anomaly detection, which comprises the following steps: establishing a reliability verification matrix of multi-dimensional purchase data, and extracting abnormal data points; identifying a cross correlation mode of the abnormal data points, and constructing a purchase behavior interaction graph of the target enterprise; analyzing a risk evolution path of the purchase risk factor, and calculating a risk conduction entropy value; and according to the risk conduction entropy value, generating a risk grading label of the purchase risk factor so as to output a purchase anomaly detection report of the target enterprise. According to the invention, the accuracy of enterprise purchase anomaly detection can be improved.

The invention discloses a hydraulic engineering supervision and analysis system and method based on digital twinning, and belongs to the technical field of hydraulic engineering operation monitoring and management, and the method comprises the steps: recognizing data points with periodic fluctuation characteristics and sparse spatial distribution, extracting a weak disturbance data subset, and building a response evolution curve; identifying a potential abnormal region through neighborhood statistics and clustering; for the abnormal region, inverting possibly existing micro-damage types and distribution directions based on historical time sequences and environmental boundary conditions to obtain an inversion defect feature set, and inputting the inversion defect feature set into a twinborn model to execute local heat-seepage-force coupling calculation to obtain a stress response evolution curve in a future preset period; calculating a risk score value according to the growth rate, step characteristics and frequency abrupt change of the curve, and outputting risk level and evolution trend information when the score exceeds a set threshold value; according to the invention, early recognition and dynamic evolution analysis of tiny hidden dangers can be realized, and the intelligent and refined level of hydraulic engineering safety supervision is improved.

Systems, methods, and devices that relate to assessing uncertainty associated with entities are disclosed. In one example aspect, the method receives artifacts relating to an entity and categories for assessing uncertainty. For each category, a generative model retrieves and standardizes data points from the artifacts. A rule-based model inputs the standardized data points to output a rating. The generative model then generates an assessment of the rating and data points according to a predefined structure. The method outputs a summary, rating, and standardized data points for each category. These outputs can be used by other systems for assessing the uncertainty of the entity and taking action based on the assessment.

The invention relates to the technical field of power grid state monitoring, in particular to an intelligent monitoring and early warning method and system for a high-voltage power grid, and the method comprises the steps: collecting the multi-dimensional parameter data of a power grid node, and obtaining the multi-dimensional parameter data of the power grid node based on the relative deviation of the data of each dimension in a local window and the mean value of the data of each dimension in combination with the correlation coefficient of the data of each dimension; calculating parameter fluctuation attention at a target moment so as to correct parameter data of each dimension; processing the data points through a clustering algorithm to obtain a plurality of clusters, and selecting the cluster center of the cluster with the most data points as a power stability index; and calculating the relative deviation between the data point and the index, and generating a state early warning coefficient so as to estimate and evaluate the abnormality of the power grid node region and generate an early warning signal. According to the method, parameter fluctuation is accurately quantified by fusing the deviation degree and correlation of the multi-dimensional data of the local window, and a foundation is built for monitoring and early warning.

The invention discloses a power supply equipment fault prediction method and device based on deep learning, and relates to the technical field of power system equipment fault prediction and deep learning application. The method comprises the following steps: acquiring a power grid topological structure, an equipment operation state, a historical fault record, a real-time equipment load and environmental condition data; forming a space-time correlation basic diagram according to the power grid topology and the equipment operation state, and calculating the correlation strength by using a diagram neural network; calculating fault time delay and determining a transmission path set by using a long short-term memory network in combination with association strength and historical fault records; fusing multiple data to calculate a cross-regional fault propagation probability, and generating a predicted fault path list; and the fault prediction output of the long-short-term memory network input is updated, and the real-time operation data verification optimization of the power grid is combined, so that accurate cross-regional cascade fault prediction is realized, and safe and stable operation of the power grid is ensured.

The invention relates to the technical field of real-time data analysis, in particular to a real-time data analysis system based on big data, which comprises an event capture module, a trend identification module, a priority processing module and a data isolation module. According to the method, the data stream is monitored in real time, the timestamps, the change amplitude and the frequency of the data points are extracted, the timeliness and the accuracy of data processing are enhanced, real-time change analysis is carried out on the data, instant understanding of the data stream is enhanced through key change marks, the response speed of market changes is increased, and the market competitiveness is improved. By analyzing the detailed background and development trend of key events, the prediction accuracy and the operation foresight are improved, resources are dynamically reconfigured according to the priority of the events, the key events are ensured to be processed preferentially, the resource utilization efficiency and the operation response speed are greatly improved, data isolation guarantees the concentration and safety of high-priority event processing, and the method is suitable for popularization and application. And the data processing efficiency is obviously improved.

The invention relates to the technical field of mechanical automation control, in particular to a bearing pressing machine control method and system. The method comprises the following steps: acquiring multi-dimensional monitoring data in a press fitting process; based on the difference between the monitoring data at the current moment and the data at each moment in the preset window, constructing a deviation degree sequence of each dimension at the current moment; adjusting the length of a preset window based on the correlation of the deviation degree sequence to obtain a target window at the current moment; determining a truncation distance of clustering analysis according to the data point distribution sparseness of the monitoring data in the target window; taking the truncation distance as a parameter, and performing clustering analysis on the data points in the target window through a density clustering algorithm to identify abnormal data points; and when the monitoring data at the current moment is abnormal, the bearing press-fitting machine is controlled to execute a preset response action. According to the method, the analysis window and the clustering truncation distance are adaptively adjusted, so that the accuracy of abnormal state recognition under the multi-stage working condition is improved.

The invention discloses a submarine topography super-resolution reconstruction method based on window displacement multi-source fusion, and aims to solve the problem of precision attenuation caused by absence of multi-beam truth value evaluation and resolution improvement of an existing neural network submarine topography reconstruction method. The method comprises the following steps of: obtaining gravity anomaly, gravity vertical gradient, vertical line deviation component, submarine topography background and ship survey water depth multi-source data of a target sea area; constructing a residual U-Net attention neural network model, and performing training by taking multi-source data extracted by a 11 * 11 window and position codes as input; moving the trained model sampling window in a staggered manner according to a target resolution step length; extracting data points in the window during movement and injecting position codes; and outputting a high-resolution water depth predicted value based on the position code and the window data. The reconstruction precision is improved by fusing physical quantities, the multi-resolution robustness is guaranteed by a window displacement mechanism, and the medium-frequency feature reconstruction capability is enhanced by a residual U-Net attention model.

The invention relates to the technical field of data acquisition, in particular to a station automation terminal with an efficient electric energy data acquisition function. Obtaining an abnormal degree according to neighborhood fluctuation characteristics of data points in the electric energy monitoring time sequence; obtaining an abnormal point according to the abnormal degree; obtaining a fluctuation stable value of the abnormal point according to the interval feature and the data difference feature of the abnormal point and other adjacent abnormal points; adjusting a weight coefficient of an abnormal point in the ARIMA model according to the fluctuation stable value to obtain a self-adaptive ARIMA model; and predicting the electric energy monitoring time sequence according to the self-adaptive ARIMA model to obtain an electric energy prediction time sequence. According to the invention, the preset acquisition frequency is adjusted according to the difference characteristics of the abnormal points between the electric energy prediction time sequence and the electric energy monitoring time sequence, the adaptive acquisition frequency is obtained, the electric energy data behind the adjustment point is acquired, and the efficiency and accuracy of electric energy data acquisition are improved.

The invention relates to the technical field of power distribution network monitoring, in particular to an intelligent operation and maintenance management platform for a power distribution network. Obtaining suspected abnormal points according to data discrete features and local data fluctuation features of the feature data sequence; according to the number characteristics of the suspected abnormal points in all the operation and maintenance characteristics and the data distribution characteristics of the suspected abnormal points, risk statistical characteristic values of the power distribution network equipment are obtained; clustering suspected abnormal points in the operation and maintenance features, and obtaining risk distribution feature values of the power distribution network equipment according to time interval features between the data point clusters and interval features of the suspected abnormal points in the data point clusters; and obtaining the risk degree of the power distribution network equipment according to the risk statistical characteristic value and the risk distribution characteristic value. According to the invention, the inspection sequence of all power distribution network equipment is sorted according to the risk degree, and inspection is carried out in sequence, so that the inspection efficiency and the power utilization stability are improved.

The invention discloses a water quality monitoring method and system based on big data analysis, and the method comprises the steps: obtaining water quality parameter data collected by multiple types of sensors, carrying out the format unification processing of the water quality parameter data through a standardization algorithm, and carrying out the elimination through a median filtering algorithm if abnormal data points are detected, thereby obtaining a standardized data set; aiming at the standardized data set, performing feature extraction on the chemical parameters, the spectral features and the biological indexes by adopting a principal component analysis algorithm to obtain feature vectors, and if the variance contribution rate of the feature vectors exceeds a preset threshold value, retaining the feature vectors and generating a dimension reduction feature data set; according to the dimension reduction feature data set, a random forest algorithm is adopted to construct a pollution evaluation model, a pollution index is calculated, if the pollution index exceeds a preset threshold value, a high pollution state mark is generated, and a pollution evaluation result is output. According to the invention, real-time monitoring, evaluation, early warning and traceability of water quality pollution are realized, and comprehensive technical support is provided for water environment protection.

A method and system for controlling a heating, ventilation, and air conditioning system (HVAC) in a building based on adaptive model predictive control (MPC) is disclosed, and belongs to the technical field of intelligent building adaptive control. The method may include: acquiring operation data of the HVAC under different working conditions, and performing preprocessing on the acquired data; calculating a marginal fitness measure (MAD) of each data point for each class based on the preprocessed data, and calculating a fitness measure of each data point for each class by combining MADs of all descriptors to obtain a global fitness measure (GAD); setting a GAD threshold, and classifying current operation states of the system according to classification rules; and adopting adaptive control strategies for different operation states since model parameters of each class are fixed after classification.

Hierarchical modelling and advanced feature engineering discover abnormalities in time series data with irregular trends. Data is collected in real time to ensure temporal integrity in the invention. Extraction filters and isolates useful data. Data cleansing removes noise and extraneous data after preliminary analysis identifies patterns and abnormalities. Feature engineering organizes cleansed data for machine learning algorithms. Primary storage stores this data for fast retrieval and extensive trend analysis. Holidays and weekends provide unique patterns in trend analysis. These trends are used to cluster data and create hierarchical predictive models, starting with a first-order model for general trends and increasing in order to refine residuals. Serializing these models improves storage and retrieval. Trend clusters are created from new data points, and algorithms detect pattern deviations. Statistical tests and machine learning classifiers identify anomalies and create alerts and remedial measures. The system monitors and analyzes incoming data to detect anomalies.

The invention discloses a multi-system collaborative data cross validation management system, and relates to the technical field of logistics data management, and the system comprises an initial credibility grading module which is used for obtaining a data stream and a preset static source reliability factor, and calculating an initial credibility score; the evidence weight dynamic quantification module is used for calculating a timeliness attenuation factor, matching a current service scene, distributing a service rule adjustment factor and determining a final evidence weight score of the data point; the gold record automatic judgment module is used for sequencing the data points; obtaining a highest score and a second high score; and the judgment confidence evaluation module is used for calculating a judgment confidence margin between the highest score and the second highest score, and automatically routing the conflict data set to a manual auditing queue or automatically confirming a golden record. Through quantitative evaluation, dynamic weighting and automatic judgment, the credible gold record is efficiently generated, and refined risk control and non-tampering decision traceability are realized.

The invention discloses a method and system for reducing data errors of a dynamic glucometer, and the method comprises the steps: collecting a current signal of fluid between subcutaneous tissues in real time through an implantable sensor, and synchronously recording the working environment parameters of the sensor; carrying out denoising and normalization processing on the current signal, and carrying out temperature compensation and drift correction on the signal according to a working environment parameter; extracting time sequence features and frequency domain features from the signals processed in the step S2; training a machine learning model based on the time sequence feature data and the frequency domain feature data, wherein the machine learning model is used for predicting the blood glucose concentration and identifying abnormal data points; correcting a model prediction result through residual analysis and weighted adjustment, and performing secondary correction on abnormal data points in combination with event data input by a patient; and outputting the optimized blood glucose data to a terminal display device in a time sequence curve form. Through the process, the influence of abnormal state data on blood glucose monitoring is reduced, and the data stability is improved.

The invention discloses a water quality parameter prediction method and system based on multi-sensor data fusion, and the method comprises the steps: collecting and preprocessing original dissolved oxygen data and turbidity data, and obtaining dissolved oxygen data and turbidity data in a plurality of time windows; according to the dissolved oxygen data and the turbidity data in each time window, calculating a water quality feature vector, a dynamic convolution kernel and an abnormal discrimination probability of each sensor in the time window, and performing traversal elimination based on the abnormal discrimination probability to obtain a sensor sequence; according to geographic coordinates and water flow directions of sensors corresponding to data points in the sensor sequence, calculating spatial relationship weights among the sensors, according to the water quality feature vectors, calculating frequency dynamic features, and combining the spatial relationship weights and the frequency dynamic features to generate global feature vectors; and calculating a water quality parameter tensor according to the global feature vector and the long-short term memory network, and generating a water quality parameter thermodynamic diagram. According to the method, the problem of insufficient prediction precision caused by poor spatial collaboration of single-point abnormal data and multi-source data is solved.

Apparatus and method for generating an output using an AI-PII model. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor receive personally identifiable information (PII) data, receive one or more model constraints, map, using an AI-PII model, the PII data to at least a data schema as a function of the one or more model constraints by identifying at least a PII datum of the PII data, categorizing the at least a PII datum to one or more categories of a plurality of categories, and mapping the PII data to the at least a data schema, modify the data schema based on a refinement datum, wherein the refinement datum is generated based on a temporal datum of the one or more model constraints, and generate an output as a function of the refinement datum and data schema.

The invention provides a geological environment monitoring method and system, and the method comprises the steps: constructing an air-space-earth-depth four-dimensional cooperative monitoring network, collecting multi-modal geological environment data, carrying out the preprocessing of the data, and obtaining multi-modal feature data and abnormal data points after dynamic load balance distribution of calculation resources; fusing the data to generate a comprehensive geological environment feature map containing space-time correlation features, abnormal hot spot distribution and a geologic body three-dimensional reconstruction model; and based on the map, performing geological risk prediction by using a geological risk prediction model to obtain a prediction probability. A monitoring network is constructed to integrate multi-modal geological data, resource allocation is optimized by combining edge calculation dynamic load balancing, geological risk prediction is realized by using a prediction model, the problems of single data, poor dynamic adaptability and weak model generalization ability of a traditional method are solved, the prediction precision is improved, the response time is shortened, and the prediction efficiency is improved. And a high-risk area is visually displayed through a three-dimensional risk thermodynamic diagram.

The invention relates to the technical field of three-dimensional modeling, in particular to a dynamic modeling method for a geological structure three-dimensional model. The method comprises the steps that multi-source data are acquired and preprocessed, a voxel semantic fusion algorithm based on variational optimization is introduced, after semantic information is extracted from the preprocessed multi-source data, the preprocessed multi-source data are mapped to a target three-dimensional space grid, and an optimal semantic fusion vector is obtained; based on the optimal semantic fusion vector, generating a standard voxel data pool, and constructing a geological structure three-dimensional model; monitoring data change, calculating the position of a newly added data point, combining the standard voxel data pool to obtain a space updating area, and modeling the space updating area to realize model updating; and after the modeling of the space updating region is completed, optimizing the boundary continuity. The problems that multi-source geological data cannot be directly used for structure construction and semantic fusion of a three-dimensional model, dynamic response to newly-added data is lacked, and geometric discontinuity and structural logic discontinuity exist at the boundary are solved.

The invention discloses a data dynamic partition storage method and system based on adaptive clustering, and relates to the field of data processing. The method comprises the following steps: S1, extracting multi-scale geometric features of a high-dimensional data set, calculating a local curvature and generating a curvature feature matrix; s2, constructing a feature distance matrix and a similar matrix based on the curvature feature matrix, and generating a low-dimensional embedding matrix; s3, executing self-optimization clustering according to the low-dimensional embedded matrix, determining a cluster number through singular value distribution, and generating an initial cluster; s4, calculating a stability factor of each cluster, and triggering cluster splitting or merging operation according to a threshold value to form updated cluster division; and S5, performing incremental processing on newly added data points, obtaining a new point local curvature through local curvature gradient correction, mapping the new point local curvature to a low-dimensional space, dynamically deciding affiliation based on a cluster radius, and updating partitions. Through multi-scale feature extraction, self-optimization clustering and incremental updating mechanisms, high-dimensional data clustering precision, robustness and calculation efficiency are improved.

The invention relates to the technical field of electric digital data processing, in particular to a radiator working state monitoring method and system based on data analysis, and the method comprises the steps: collecting multi-dimensional parameter data of a radiator at different moments, and taking the multi-dimensional parameter data of the radiator at the same moment as a data point; clustering all the data points to obtain a plurality of clusters; the abnormal degree of any cluster is calculated, the abnormal degree is in inverse correlation with the number of the data points in each cluster and is in positive correlation with the standard deviation of the data points in each cluster, the data points in the cluster with the abnormal degree larger than a set threshold value are responded to be abnormal data points, and it is judged that the radiator is abnormal at the moment corresponding to the abnormal data points. The problem that the monitoring efficiency and accuracy are not high in the process of monitoring the abnormality of the radiator is solved.

The invention discloses a satellite orbit forecasting method based on deep learning physical constraint loss, and the method comprises the following steps: 1, carrying out the normalization preprocessing of input data, forming a training data set and a test data set, and constructing batch processing training data; and 2, performing dimension expansion on sample data points in each window in the batch processing data formed in the step 1, constructing a multi-dimensional feature space of the sample points, and forming a batch processing input data format capable of being introduced into the model. And 3, performing forward reasoning on the batch data formed in the step 2 by using a model, and obtaining a batch processing orbit prediction value output by the model at the next moment through a CNN lightweight spatial-temporal feature extraction module and a BiLSTM bidirectional time sequence neural network module. And 4, taking the track prediction value obtained in the step 3 and the truth value label in the training set obtained in the step 1 as input, calculating to obtain a loss value of a current training iteration batch through a multi-random learning loss module fusing physical constraints, and performing reverse updating of model parameters to complete model training. And step five, through the steps two to four, performing reasoning verification on the model by using the test set formed in the step one, and comparing with a truth value in the test set to obtain a model test result.

Techniques are described herein that are capable of performing AI-based entity maliciousness analysis using embedding and sampling. A representative sample of data associated with an entity is selected by comparing embeddings that represent the data. A potentially anomalous data point is identified in at least a portion of the data based on a proximity of a node, which corresponds to the potentially anomalous data point, in a tree to a root node of the tree. A statistically anomalous data point is identified in representative sample data points, which define the representative sample, as a result of the statistically anomalous data point indicating an unexpected occurrence of an event. An AI model is triggered to determine whether the entity exhibits malicious behavior by providing an AI prompt, including the representative sample and a description of the potentially anomalous data point and the statistically anomalous data point, to the AI model.

A system identifying outlier data and generating corrective action, wherein the system includes at least a processor and a memory communicatively connected to the at least a processor and containing instructions. The instructions may configure the at least a processor to obtain a dataset, apply a clustering model to the dataset to generate a set of clusters based on the inherent relationships between the data points, determine the distance metric for each data point relative to its corresponding cluster centroid, define an outlier threshold based on the distance metrics of the data points, identify the data points that exceed the outlier threshold as one or more outliers, classify the one or more outliers across one or more axes, and output a report of the one or more identified outliers, wherein the report suggests corrective actions and insights for each of the one or more identified outliers.

The invention discloses a computing power resource dynamic scheduling method and system based on artificial intelligence, relates to the technical field of artificial intelligence, and solves the technical problems that the resource utilization rate of resource data is relatively low and the accuracy and efficiency of a resource dynamic scheduling method are relatively low due to the fact that the change condition of resource use is not considered and predicted in the prior art. Predicted resource data is generated based on task data; according to the predicted resource data and the task queue, generating a capacity expansion and shrinkage resource quantity; generating task priorities based on the task parameters; dynamically adjusting a task queue based on the task waiting duration; and generating a task scheduling scheme according to the capacity expansion and contraction resource quantity and the adjusted task queue, predicting resource data required by tasks in a future period of time in advance, and dynamically adjusting the capacity expansion and contraction opportunity and the expansion and contraction capacity of each piece of resource data in combination with real-time data in the current task queue, so that the utilization rate of the resource data can be maximized, and the task scheduling efficiency is improved. And the accuracy and efficiency of the resource dynamic scheduling method are improved.

The invention relates to the technical field of sewage treatment, and discloses a risk early warning method and system for a sewage treatment trusteeship operation project. The method comprises the following steps: acquiring multi-source operation data of sewage treatment equipment, and generating a time sequence data set after time synchronization processing; identifying abnormal data points based on the data set and generating an abnormal mark sequence; performing time sequence fluctuation and persistence analysis on the sequence, and extracting features to construct an abnormal trend feature vector; performing correlation analysis in combination with medicament dosage records, and determining a cost risk assessment result; generating a layered early warning signal according to the risk level, and matching a response strategy; and integrating related data to form a push data set, sending a notification to an operator and updating a processing record, and finally generating a traceable exception processing file. According to the invention, timely early warning and accurate response in the sewage treatment process can be realized, and the efficiency and safety of operation management and the accuracy of risk prevention and control are improved.