A data processing method and system based on embedded industrial control screen

Abstract

The present invention relates to a data processing method and system based on an embedded industrial control screen. The method first processes multi-source heterogeneous sensor data in segments, then calculates statistical feature data of the segmented data, determines data processing priority based on the statistical feature data, and processes part of the data first, which helps to fully utilize the limited computing power of the embedded industrial control screen to achieve the effect of real-time data processing. Moreover, by using a virtual processing unit suitable for the application scenario of the embedded industrial control screen, the data to be processed is processed into a multi-dimensional feature vector, and then the component data of the multi-dimensional feature vector is obtained in advance, and then a visual rendering engine is used to process the component data into real-time trend data that can be visually displayed. Data related to the change trend of sensor data can be quickly extracted from the data to be processed and converted into data that can be visually displayed, thereby meeting the real-time visual display requirements of the embedded industrial control screen for the change trend of multi-source heterogeneous data.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to a data processing method and system based on an embedded industrial control screen. Background Art

[0002] With the development of industrial automation, embedded industrial control screen technology has emerged. An embedded industrial control screen is a computer device specifically used for industrial control systems. It is usually embedded in machinery and equipment to monitor and manage production processes.

[0003] Due to software and hardware constraints, traditional embedded industrial control screens have limited computing power, making it difficult to meet the needs of real-time analysis and visualization of multi-source data. However, efficient data processing and analysis are crucial for improving production efficiency and decision-making quality in this field. Therefore, a solution is needed to overcome this dilemma. Summary of the Invention

[0004] Based on this, it is necessary to provide a data processing method and system that can meet the real-time analysis needs of multi-source data of embedded industrial control screens in response to the above technical problems.

[0005] In a first aspect, the present invention provides a data processing method based on an embedded industrial control screen, the method comprising:

[0006] Acquire multi-source heterogeneous sensor data;

[0007] Processing the multi-source heterogeneous sensor data by a sliding window technology to obtain segmented data; calculating statistical feature data of the segmented data, wherein the statistical feature data constitutes first statistical data;

[0008] Establishing a task priority list based on the first statistical data and preset priority rules;

[0009] Converting the segmented data into a standard format data set;

[0010] Performing feature point annotation on the standard format data set to obtain an annotated data set;

[0011] Processing the labeled data set using a virtual processing unit according to a task priority list to obtain a multidimensional feature vector;

[0012] Extracting component data of the multidimensional feature vector, wherein the component data includes a trend component and a seasonal component;

[0013] The component data is processed in real time using a visual rendering engine to obtain real-time trend data.

[0014] In one embodiment, processing the labeled data set using a virtual processing unit according to the task priority list to obtain a multi-dimensional feature vector comprises the following steps:

[0015] Determine whether the amount of data in the labeled data set exceeds a first preset threshold, and obtain a determination result;

[0016] When the judgment result is that the data volume exceeds a first preset threshold, the labeled data set is divided according to the task priority list and the preset partitioning rule to obtain a partitioned data set;

[0017] Allocating multiple virtual processing units to extract feature data of each partitioned data set in parallel;

[0018] Processing the feature data using a principal component analysis algorithm to obtain a multidimensional projection;

[0019] Extracting key dimensional information of the multidimensional projection using a preset projection template to obtain a multidimensional feature set;

[0020] The multidimensional feature set is processed using data integration and vector concatenation to obtain a multidimensional feature vector.

[0021] In one embodiment, extracting component data of the multidimensional feature vector includes:

[0022] According to the preset sharding rules, the multi-dimensional feature vector is processed in real time by using a streaming computing framework to obtain data fragments;

[0023] Calculating each data segment in parallel to obtain second statistical data, wherein the second statistical data includes a data change amplitude;

[0024] When the data change amplitude exceeds a second preset threshold, decomposing the second statistical data using a time series decomposition algorithm to obtain decomposed data;

[0025] Component data are extracted from the decomposed data using a moving average method.

[0026] In one embodiment, the processing of the component data in real time using a visualization rendering engine includes:

[0027] According to a preset visualization template, the component data is converted into visualization feature data through data mapping technology;

[0028] The visual feature data is drawn in real time using a rendering engine, and the rendering results are dynamically refreshed to obtain real-time trend data.

[0029] In one embodiment, the method further comprises:

[0030] Get historical trend data;

[0031] Calculating the cosine similarity between the real-time trend data and the historical trend data to obtain a first cosine similarity;

[0032] When the first cosine similarity is lower than a third preset threshold, the historical trend data set is updated according to the real-time trend data to obtain an updated historical trend data set.

[0033] In one embodiment, the method further comprises:

[0034] Constructing a real-time trend feature vector set based on the real-time trend data;

[0035] Using a K-means algorithm to cluster the historical trend data set to obtain a clustering result, wherein the clustering result includes a dynamic trend model and cluster center point data;

[0036] Constructing a historical trend feature vector set based on the clustering results;

[0037] Calculating the Euclidean distance between the real-time trend feature vector set and the historical trend feature vector set to obtain a first Euclidean distance;

[0038] When the first Euclidean distance is greater than a fourth preset threshold, calculating a difference vector between the real-time trend feature vector and the historical trend feature vector, wherein the difference vector constitutes a trend comparison data set;

[0039] The trend comparison data set is processed using vector graphics acceleration technology, a preset rendering frequency, and preset scaling parameters to obtain a first dynamic trend chart.

[0040] In one embodiment, the method further comprises:

[0041] Get real-time interaction data;

[0042] Extracting feature point data and metadata from the real-time interaction data through event monitoring technology, wherein the feature point data and metadata constitute an interaction event feature set;

[0043] Performing clustering processing on the feature point data using a K-means algorithm to obtain zoom focus data and annotation clarity data, wherein the zoom focus data and the annotation clarity data constitute an interactive response parameter set;

[0044] When the interactive response parameter set does not match the preset rendering parameter threshold, adjusting the zoom focus data and the annotation clarity data using a preset algorithm, the adjusted zoom focus data and the adjusted annotation clarity data forming an interactive configuration data set;

[0045] Parameter mapping is performed on the interaction configuration data set and the first dynamic trend chart to obtain interaction enhancement trend data, where the interaction enhancement trend data includes trend information of the interaction configuration.

[0046] In one embodiment, the method further comprises:

[0047] Obtain preset style configuration data and resolution adaptation requirement data;

[0048] According to the resolution adaptation requirement data, the style configuration data is adjusted by a CSS preprocessor to obtain a multi-resolution adaptation style sheet, wherein the multi-resolution adaptation style sheet constitutes a dynamic style configuration set;

[0049] Rendering the trend comparison dataset at multiple resolutions using a WebGL renderer according to the dynamic style configuration set and the rendering frequency to obtain rendering data containing icon dynamic information; the multi-resolution rendering includes: adjusting a frequency synchronization parameter when the rendering frequency is lower than a fifth preset threshold;

[0050] Using a K-means algorithm to cluster the feature points of the rendered data to obtain feature point annotation data;

[0051] According to the scaling parameter and the feature point annotation data, a second dynamic trend chart including feature point annotations is generated by vector graphics rendering technology, and the second dynamic trend chart constitutes visual analysis report data.

[0052] In one embodiment, the method further comprises:

[0053] Extracting core data from the visual analysis report data;

[0054] Compress the core data using the LZ77 compression algorithm to obtain compressed data;

[0055] storing the compressed data in a designated database;

[0056] determining storage stability of the compressed data in the designated database to obtain a determination result, the determination result including whether the storage stability reaches a sixth preset threshold;

[0057] Generate a storage report, which includes the write success rate, time consumption, the core data and the judgment result.

[0058] In one embodiment, the method further comprises:

[0059] Receive data query instructions and resolution parameters of the target terminal;

[0060] Extracting core data from the storage report through an interactive query interface according to the data query instruction;

[0061] Using JSON parsing technology to structure the core data to obtain structured query data;

[0062] According to the resolution parameter, adjusting the output format style parameter of the structured query data through CSS media query to obtain first display data adapted to the terminal;

[0063] Interactive zooming technology is used to calculate the zoom focus coordinates;

[0064] Generate a dynamic trend chart including the zoom focus coordinates by using Canvas drawing technology to obtain interactive enhanced chart data;

[0065] If the frame rate of the interactive enhanced chart data is lower than a seventh preset threshold, adjusting a rendering frequency parameter of the interactive enhanced chart data through a WebGL renderer to obtain terminal output data;

[0066] The terminal outputs the data to the target terminal.

[0067] In a second aspect, the present invention provides a data processing system based on an embedded industrial control screen, the system comprising:

[0068] A first data acquisition module is used to acquire multi-source heterogeneous sensor data;

[0069] A segmentation calculation module is used to process the multi-source heterogeneous sensor data by a sliding window technology to obtain segmented data; calculate statistical feature data of the segmented data, wherein the statistical feature data constitutes first statistical data;

[0070] a priority module, configured to establish a task priority list based on the first statistical data and preset priority rules;

[0071] A format conversion module, configured to convert the segmented data into a standard format data set;

[0072] A data annotation module, configured to annotate the standard format data set with characteristic points to obtain an annotated data set;

[0073] a virtual unit module, configured to process the labeled data set using a virtual processing unit according to a task priority list to obtain a multidimensional feature vector;

[0074] A component extraction module, configured to extract component data of the multidimensional feature vector, wherein the component data includes a trend component and a seasonal component;

[0075] The data rendering module is used to use a visual rendering engine to process the component data in real time to obtain real-time trend data.

[0076] In the above-mentioned data processing method based on the embedded industrial control screen, the multi-source heterogeneous sensor data is first segmented and processed, and then the statistical feature data of the segmented data is calculated. Based on the statistical feature data, the data processing priority is determined, and some data is processed first, which helps to fully utilize the limited computing power of the embedded industrial control screen to achieve the effect of real-time data processing. Moreover, by using a virtual processing unit suitable for the application scenario of the embedded industrial control screen, the data to be processed is processed into a multi-dimensional feature vector, and then the component data of the multi-dimensional feature vector is obtained in advance. Then, the component data is processed into real-time trend data that can be visually displayed using a visualization rendering engine. This can quickly extract data related to the changing trend of sensor data from the data to be processed and convert it into data that can be visually displayed, thereby meeting the real-time visualization display requirements of the embedded industrial control screen for the changing trend of multi-source heterogeneous data. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present invention, and together with the description are used to explain the principles of the present invention, and do not constitute an improper limitation of the present invention.

[0078] Figure 1 A schematic flow chart of a method for processing data of an embedded industrial control screen in one embodiment;

[0079] Figure 2 Schematic diagram of the process of step S106 in one embodiment;

[0080] Figure 3 Schematic diagram of the process of step S107 in one embodiment;

[0081] Figure 4 The figure is a schematic diagram of the structure of an embedded industrial control screen data processing system in one embodiment. DETAILED DESCRIPTION

[0082] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0083] It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of systems and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0084] In one embodiment, Figure 1 As shown, a data processing method based on an embedded industrial control screen is provided, and the method includes the following steps:

[0085] S101, acquiring multi-source heterogeneous sensor data.

[0086] Among them, multi-source heterogeneous sensor data can refer to data from different sensors with different structures and formats.

[0087] Specifically, the system can have a data receiver capable of receiving data from a variety of sensors. Sensors come in many different types, such as temperature sensors, humidity sensors, and pressure sensors. Sensor data formats often vary. For example, a temperature sensor may output data in JSON format (JSON stands for JavaScript Object Notation, a lightweight data exchange format), while a pressure sensor may output data in XML format (XML stands for Extensible Markup Language, a plain text file format).

[0088] S102 , processing the multi-source heterogeneous sensor data by a sliding window technology to obtain segmented data; calculating statistical feature data of the segmented data, wherein the statistical feature data constitute first statistical data.

[0089] Specifically, the sliding window technology is an existing flow control technology that moves on an array or string by maintaining a fixed-size window, so that a series of data can be processed in a shorter time, which is particularly efficient. Using the sliding window technology to process the multi-source heterogeneous sensor data can improve the data processing speed and meet the real-time processing requirements of the industrial control screen data as much as possible. The multi-source heterogeneous sensor data is segmented using the sliding window technology to obtain segmented multi-source heterogeneous sensor data. The statistical features of the segmented data are calculated, and the statistical features may include at least one of the data statistical values ​​such as the mean, variance, maximum value, minimum value, and median value. Specifically, the size of the sliding window can be set as needed, for example, it can be 3 seconds, 5 seconds, 8 seconds, 10 seconds, etc., and the step size can be 1 second, 2 seconds, etc.

[0090] It should be noted that before processing the multi-source heterogeneous sensor data using the sliding window technique, the multi-source heterogeneous sensor data can be subjected to some common data preprocessing. For example, when the multi-source heterogeneous sensor data is inconsistent in format, a preset standardized protocol can be used to unify the format of the multi-source heterogeneous sensor data. For example, the MQTT (Message Queuing Telemetry Transport) protocol can be used as a standardized protocol to convert data of different formats into a unified JSON format. For example, when the multi-source heterogeneous sensor data is relatively chaotic, a timestamp synchronization method can be used to align the multi-source heterogeneous sensor data. Specifically, the NTP protocol (Network Time Protocol) can be selected to add a unified timestamp to each data point to ensure data alignment and obtain a synchronized data stream, thereby facilitating the use of the sliding window technique.

[0091] S103: Establish a task priority list according to the first statistical data and preset priority rules.

[0092] The priority rule may refer to a rule that can be used to determine the priority of data processing.

[0093] Specifically, since the first statistical data is segmented, it is obvious that the data content of different segments is different, and the priorities for processing are different, and some segmented data even do not need to be processed. Therefore, certain priority rules can be preset so that the first statistical data of different segments have different processing priorities, forming a priority list of data processing tasks. For example, the processing priority can be determined based on the urgency or importance of data processing. For example, the segmented data with abnormal temperature fluctuations has a higher priority, and a task priority list can be generated based on the abnormal temperature fluctuations. This method can effectively identify critical tasks, improve system response efficiency, and help to call limited computing resources to quickly meet data processing needs.

[0094] S104: Convert the segmented data into a standard format data set.

[0095] Specifically, the segmented data may contain structured data and unstructured data. For example, structured data may be the JSON format output of a temperature sensor, containing fields such as "temperature value" and "timestamp"; unstructured data may be a text description of a log record, containing natural language information about the operating status of the device. Preset parsing rules can be used to map fields to structured data, and text segmentation technology can be used to extract semantic units from unstructured data to obtain a preliminary extracted data set. For the preliminary extracted data set, a unified data standardization protocol is used to convert the format of the heterogeneous data therein. If the format of the heterogeneous data does not conform to the preset specifications, the data structure is adjusted through field completion technology to obtain the standard format data set.

[0096] In one possible implementation, parsing rules are defined based on JSONSchema (a declarative language based on JSON), mapping the "temperature value" field to the uniform field name "temp_value" and the timestamp field to "timestamp." If the data is missing key fields, such as humidity data not including "unit," rules are used to complete the fields and add a default value such as "%. This method ensures field consistency and facilitates subsequent processing. For unstructured data, semantic units can be extracted using text segmentation techniques. For example, the log text "Device A has an abnormal temperature for 10 minutes" can be segmented using natural language processing tools to extract semantic units such as "Device A," "Temperature Abnormal," and "10 minutes."

[0097] In one embodiment, a dictionary-based word segmentation method is used, combined with a preset keyword library, to identify key entities and actions and generate a structured set of semantic units. This method effectively converts unstructured data into a processable format. Based on the preliminary extracted data set, a unified data standardization protocol is used for format conversion. For example, Protobuf is selected as the standardization protocol to convert heterogeneous data in JSON and XML formats into Protobuf format. If the data format does not conform to the specification, such as the XML data lacks the required field "sensor ID", the field completion technology is used to infer and add the ID value based on the device configuration file. This process ensures the uniformity of the data format and reduces the complexity of subsequent processing. There are many similar data format conversion methods in the prior art, and technicians can choose according to actual needs, which will not be repeated here.

[0098] S105 , annotating feature points of the standard format dataset to obtain an annotated dataset.

[0099] Specifically, for standard format data sets, there are multiple methods to implement feature point annotation. For example, key point detection algorithms can be used to annotate key dimension features. For example, in temperature data, the key dimensions are "temperature value" and "timestamp". In one possible implementation, a threshold-based detection algorithm is used to annotate data points with temperature values ​​exceeding 50°C as "abnormal", and the annotation information is recorded through a preset template, such as {"point_id":1, "type":"abnormal", "value":52.3, "timestamp":"2025-05-0910:00:00"}. This method facilitates subsequent analysis of key data points. For feature-annotated data sets, data integration technology can also be used for clustering. For example, based on the K-means clustering algorithm, the annotated data is divided into three categories according to the degree of temperature anomaly: "mild", "moderate" and "severe". In one embodiment, the preset clustering rules are: temperature values ​​between 50 and 60°C are mild, 60 to 70°C are moderate, and above 70°C are severe. The resulting annotated dataset contains clustered annotations, such as {"cluster_id":1, "type":"severe", "points":[point_id1,point_id2]}. This method effectively integrates data, facilitating the system's identification and processing of critical events. The above method, from data extraction to clustering, forms a complete processing chain. Each link supports the other, ensuring efficient and accurate conversion from raw data streams to the final annotated dataset. This approach is suitable for real-time data processing in embedded systems.

[0100] S106 , using a virtual processing unit to process the labeled data set according to the task priority list to obtain a multi-dimensional feature vector.

[0101] The virtual processing unit may refer to a logical unit representing processing capabilities in a virtual environment.

[0102] Specifically, one or more virtual processing units can be used to process the corresponding labeled data sets in order of priority based on the task priority list. Based on the task priority list, a heuristic algorithm can be used to allocate tasks. In one embodiment, a genetic algorithm is used to optimize the allocation, and high-priority tasks are preferentially allocated to idle virtual processing units. For example, the temperature anomaly processing task is allocated to unit 1. If the resource occupancy of a unit exceeds a threshold, such as 80%, the low-priority task is migrated to unit 2, and a task allocation table is generated. It can be understood that this dynamic adjustment mechanism avoids resource bottlenecks and improves system stability. For example, in industrial monitoring scenarios, embedded systems need to process sensor data in real time. Through the above method, the system can efficiently allocate resources, prioritize key anomalies, and ensure monitoring reliability. The implementation of each technical theme revolves around efficient resource utilization and real-time data processing, supporting each other to form a complete solution and significantly improve the performance of embedded systems.

[0103] Furthermore, regarding the construction of virtual processing units (VPUs), a pre-defined lightweight virtualization framework can be used to deploy a virtualized environment within the embedded industrial control panel system, generating multiple VPUs for efficient resource management. For example, LXC (Linux Container, a kernel virtualization technology) container technology can be used in conjunction with a pre-defined configuration file containing the number of VPUs, such as four, and resource constraints, such as allocating 2GB of memory and one CPU core per unit. LXC can be used to quickly create four VPUs, forming a VPU cluster. This approach ensures resource isolation, reduces system overhead, and improves embedded device resource utilization. This approach is widely used in existing technologies and will not be discussed further.

[0104] S107: Extract component data of the multidimensional feature vector, where the component data includes a trend component and a seasonal component.

[0105] The trend component may refer to a component representing the long-term increase or decrease of data, which may be linear or nonlinear. The seasonal component may refer to a component representing the periodic fluctuation of data.

[0106] Specifically, depending on the type and variation characteristics of sensor data, there can be multiple types of trend components, such as rising temperature trends, falling temperature trends, rising humidity trends, falling humidity trends, rising pressure trends, falling pressure trends, and so on. Similarly, there can be multiple seasonal components, such as daily temperature cycles, daily humidity cycles, and annual pressure cycles. Various methods can be used to extract component data from multidimensional feature vectors, such as first performing data decomposition on the multidimensional feature vectors and then performing data extraction to obtain the component data.

[0107] S108: Using a visual rendering engine to process the component data in real time to obtain real-time trend data.

[0108] The visualization rendering engine may refer to a software tool for creating and presenting three-dimensional graphics and scenes in a computer. The real-time trend data may refer to data that can be visualized and contains information about data change trends.

[0109] Specifically, the component data may be first converted into a visual data structure, and then a visual rendering engine applicable to industrial control screen application scenarios may be used to render the visual data structure in real time, and the rendering result may be dynamically refreshed to obtain real-time trend data.

[0110] In the above-mentioned data processing method based on the embedded industrial control screen, the multi-source heterogeneous sensor data is first segmented and processed, and then the statistical feature data of the segmented data is calculated. Based on the statistical feature data, the data processing priority is determined, and some data is processed first, which helps to fully utilize the limited computing power of the embedded industrial control screen to achieve the effect of real-time data processing. Moreover, by using a virtual processing unit suitable for the application scenario of the embedded industrial control screen, the data to be processed is processed into a multi-dimensional feature vector, and then the component data of the multi-dimensional feature vector is obtained in advance. Then, the component data is processed into real-time trend data that can be visually displayed using a visualization rendering engine. This can quickly extract data related to the changing trend of sensor data from the data to be processed and convert it into data that can be visually displayed, thereby meeting the real-time visualization display requirements of the embedded industrial control screen for the changing trend of multi-source heterogeneous data.

[0111] In one embodiment, Figure 2 As shown, step S106 includes the following steps:

[0112] S201, determining whether the data volume of the labeled data set exceeds a first preset threshold, and obtaining a determination result.

[0113] S202: When the judgment result is that the data volume exceeds a first preset threshold, the labeled data set is divided according to the task priority list and preset partitioning rules to obtain partitioned data sets.

[0114] S203 , allocating multiple virtual processing units to extract feature data of each partitioned data set in parallel.

[0115] S204: Process the feature data using a principal component analysis algorithm to obtain a multi-dimensional projection.

[0116] S205: Using a preset projection template, extract key dimension information of the multi-dimensional projection to obtain a multi-dimensional feature set.

[0117] S206 , processing the multidimensional feature set using data integration and vector concatenation to obtain a multidimensional feature vector.

[0118] The first preset threshold may refer to a preset threshold representing the size of the data. The principal component analysis algorithm is a commonly used dimensionality reduction algorithm that maps high-dimensional data to a low-dimensional space through multi-dimensional projection, thereby retaining the main information.

[0119] Specifically, the first preset threshold can be set according to actual needs, and the present invention does not make specific restrictions. For example, the first preset threshold can be 10 4 ~10 5A numerical value in a piece of data. If the data volume of the annotated dataset exceeds a preset threshold, the annotated dataset can be partitioned into multiple data partitions according to the task allocation table. Preset partitioning rules can be used to generate partitioned datasets, i.e., partitioned datasets. The preset partitioning rules can be set based on the specific data situation; for example, one partition can be generated for each hour of data. For each partitioned dataset, virtualization technology is used to assign each data partition to a corresponding virtual processing unit. These virtual processing units can then be synchronously processed using a parallel computing framework to extract feature data. Based on the feature data, the principal components of a principal component analysis algorithm are determined. Multidimensional projection processing is then performed on the data of the virtual processing units using the principal component analysis algorithm. Key dimensional information is extracted using a preset projection template to obtain a multidimensional feature set. For this multidimensional feature set, data integration technology is used to merge the multidimensional feature sets, and vector concatenation technology is used to generate the final feature vectors, resulting in a feature vector dataset. For example, in embedded system data processing scenarios, the processing of annotated datasets must cope with large-scale data and high concurrency requirements. If the data volume of an annotated dataset exceeds a preset threshold, such as 1 million records, it must be split using a partitioning strategy. In one possible implementation, based on a task priority list, the dataset is partitioned by sensor type, such as forming separate sub-datasets for temperature and humidity sensor data. Partitioning rules can be based on timestamp ranges, for example, generating a partition for each hour of data, resulting in a partitioned dataset, such as the "temperature_2025-05-09_10:00" subset. This approach facilitates subsequent parallel processing. Virtualization technology is used to allocate resources for partitioned datasets. Specifically, each data partition is mapped to an independent virtual processing unit, such as a virtual machine or container. In one embodiment, Docker containers (Docker containers represent an open-source application container engine) are used to allocate computing resources to each partition, and Spark (Spark represents a memory-based parallel computing framework) is used for synchronous processing. For example, the temperature sub-dataset is processed in container A, and the humidity sub-dataset is processed in container B. Feature data, such as the average temperature value, is extracted in parallel from each container. Based on the feature data, principal component analysis (PCA) is used to reduce dimensionality and extract key information. In another possible implementation, the temperature and humidity data are projected into a two-dimensional space, and key dimensions such as "temperature mean" and "humidity variance" are extracted. The projection template can be preset to retain 90% of the variance in the dimension, generating a multidimensional feature set. This method effectively simplifies data complexity and helps improve processing efficiency.

[0120] For multidimensional feature sets, data integration technology is used for merging processing. Preferably, vector splicing technology can be used to integrate each multidimensional feature set into a multidimensional feature vector. For example, the temperature feature vector [25.5, 0.8] and the humidity feature vector [60, 1.2] are spliced ​​into [25.5, 0.8, 60, 1.2]. In one embodiment, the features are normalized before splicing to ensure that the numerical range is consistent, such as 0 to 1. This process helps facilitate subsequent analysis. An efficient processing chain is formed in the process, and each link supports each other to ensure the rapid processing of large-scale data in embedded systems. Partitioning reduces the pressure on a single node, virtualization and parallel computing improve efficiency, principal component analysis simplifies data, and feature splicing ensures information integrity. This process is suitable for real-time data analysis scenarios.

[0121] In one embodiment, Figure 3 As shown, step S107 includes:

[0122] S301, according to the preset sharding rules, use the streaming computing framework to process the multi-dimensional feature vector in real time sharding to obtain data segments.

[0123] S302 , calculating each data segment in parallel to obtain second statistical data, where the second statistical data includes a data change range.

[0124] S303: When the data variation exceeds a second preset threshold, a time series decomposition algorithm is used to decompose the second statistical data to obtain decomposed data.

[0125] S304: extract component data from the decomposed data using a moving average method.

[0126] The stream computing framework may refer to a computing architecture capable of processing real-time data streams. The time series decomposition algorithm may refer to an algorithm capable of analyzing and predicting time series data. The moving average method is a common statistical method. The second preset threshold may refer to a preset threshold representing the magnitude of data change.

[0127] Specifically, for multi-dimensional feature vectors, the real-time data stream is segmented through the streaming computing framework and divided into multiple data segments according to the preset segmentation rules. The data segments are synchronously calculated using parallel processing technology to calculate the data change amplitude and obtain second statistical data. The second statistical data may also include mean data. If the data change amplitude exceeds the second preset threshold, the time series decomposition algorithm is used to decompose the second statistical data to obtain decomposed data. The second preset threshold can be a percentage or a specific change value, which can be set according to actual needs. The present invention does not impose specific restrictions. For example, for changes in temperature data, the second preset threshold can be a 20% increase in temperature or a 2°C increase in temperature. Component data, such as trend components and seasonal components, are extracted by the moving average method, and the component data can constitute a component data set.

[0128] For example, in real-time data processing scenarios in embedded systems, processing multidimensional feature vectors requires high concurrency and dynamic changes. Segmenting real-time data streams using a streaming computing framework can effectively address the high speed and continuity of data streams. Streaming computing frameworks, such as Apache Flink (an open-source stream processing framework), can be used to segment sensor data streams into time windows, for example, generating a data segment every 5 seconds. Specifically, a temperature sensor data stream is segmented by timestamp, such as "temperature_2025-05-09_10:00:00-10:00:05." This segmentation facilitates subsequent parallel processing. It should be noted that parallel processing technology can improve processing efficiency by synchronously computing data segments. In one possible implementation, multithreading technology is used to assign independent computing threads to each data segment. For example, segment A is processed by thread 1, and segment B is processed by thread 2. Each thread simultaneously computes statistical features of the segments, such as the mean temperature. Preferably, thread pool management technology can dynamically adjust the number of threads to ensure resource utilization. Regarding the dynamic changes in the second statistical data, if the magnitude of the change exceeds a preset dynamic threshold, such as if the mean temperature fluctuates by more than 2 degrees Celsius, further processing is required. It is understood that time series decomposition algorithms can effectively extract trends and patterns in data. In one embodiment, a moving average method is used to decompose the data with a window size of 10 seconds to extract trend components, such as rising temperature trends, and seasonal components, such as daily temperature cycle changes. This decomposition method facilitates analysis of long-term patterns of data change.

[0129] In one embodiment, step S108 includes:

[0130] S401 : According to a preset visualization template, the component data is converted into visualization feature data through data mapping technology.

[0131] S402: Use a rendering engine to draw the visual feature data in real time, dynamically refresh the rendering result, and obtain real-time trend data.

[0132] Specifically, based on the component data set, the component data is projected onto a preset visualization template using data mapping technology, and a vector conversion algorithm is used to generate a visualization data structure to obtain visualization feature data. The visualization feature data is then drawn in real time by a rendering engine, and the rendering results are updated using dynamic refresh technology to obtain real-time trend data. For example, the trend component is mapped to the Y-axis of a line graph, and the seasonal component is mapped to color intensity. The vector conversion algorithm can convert the data into a visualization data structure, such as a set of coordinate points in JSON format, for easy rendering. For example, the temperature trend data is converted to [{time: 10:00:05, value: 25.5}, ​​{time: 10:00:10, value: 25.7}]. Specifically, the rendering engine draws the visualization data structure in real time. The rendering engine can use WebGL technology to efficiently render a dynamic line graph. The dynamic refresh technology can include updating the image at a frequency of once per second. The temperature trend line graph extends in real time as new data flows in, and the color changes with the seasonal component. This method intuitively presents data changes, making it easier for users to monitor device status. It's easy to understand that the above process, from sharding to visualization, forms a complete chain. Sharding and parallel processing address high concurrency, time series decomposition extracts key patterns, and mapping and rendering enable intuitive presentation. Each step is logically connected to support real-time data analysis.

[0133] In one embodiment, the method further comprises:

[0134] S501, obtaining historical trend data.

[0135] S502 : Calculate the cosine similarity between the real-time trend data and the historical trend data to obtain a first cosine similarity.

[0136] S503: When the first cosine similarity is lower than a third preset threshold, updating the historical trend data set according to the real-time trend data to obtain an updated historical trend data set.

[0137] The third preset threshold may refer to a preset cosine familiarity threshold.

[0138] Specifically, the third preset threshold can be set based on actual needs and is not specifically limited in the present invention. For example, it can be ≥ 0.8 (normalized data). Historical trend data can be obtained from a historical trend dataset by locating the target dataset using a data index and searching using a query statement based on preset index fields to obtain the historical trend data. The query statement can be an SQL (Structured Query Language) query statement, which facilitates efficient screening and reduces query time. The historical trend data and real-time trend data are converted into multidimensional feature vectors using vectorization technology. Cosine similarity is then calculated between the vectors to obtain a first cosine similarity. If the first cosine similarity is lower than the third preset threshold, the historical trend data is partially replaced, and the real-time trend data is written to the historical trend dataset through transaction processing to obtain an updated historical trend dataset. Cosine similarity is calculated based on the vector angle and can intuitively reflect the degree of similarity between data trends. The updated historical trend dataset can serve as the basis for subsequent analysis, such as predicting device operating status. The third preset threshold needs to be adjusted based on business needs. For example, a higher threshold can be set for scenarios sensitive to temperature fluctuations. The process in this embodiment is clear and specific, which helps to improve the accuracy and real-time performance of data processing.

[0139] In one embodiment, the method further comprises:

[0140] S601: Construct a real-time trend feature vector set according to the real-time trend data.

[0141] S602: Perform clustering processing on the historical trend data set using a K-means algorithm to obtain a clustering result, wherein the clustering result includes a dynamic trend model and cluster center point data.

[0142] S603: Construct a historical trend feature vector set according to the clustering result.

[0143] S604 : Calculate the Euclidean distance between the real-time trend feature vector set and the historical trend feature vector set to obtain a first Euclidean distance.

[0144] S605 : When the first Euclidean distance is greater than a fourth preset threshold, a difference vector between the real-time trend feature vector and the historical trend feature vector is calculated, and the difference vector constitutes a trend comparison data set.

[0145] S606 , using vector graphics acceleration technology, a preset rendering frequency, and preset scaling parameters to process the trend comparison data set to obtain a first dynamic trend chart.

[0146] The K-means algorithm is an existing distance-based unsupervised clustering algorithm. The fourth preset threshold may refer to a preset Euclidean distance value. Vector graphics acceleration technology refers to technology that improves the speed and efficiency of vector graphics processing through hardware or software means. Rendering frequency may refer to the number of frames rendered per second during graphics rendering.

[0147] Specifically, the fourth preset threshold can be set according to actual needs, and the present invention does not impose specific restrictions. For example, it can be 2, 3, 5 or 10. The real-time trend data is extracted and represented in vector form to obtain a real-time trend feature vector, and the real-time trend feature vector constitutes a real-time trend feature vector set. For the updated historical trend data set, the K-means algorithm is used to cluster the historical trend data to generate a dynamic trend model. Each dynamic trend model will have a corresponding cluster center point. The cluster center point data is converted into a vector form to obtain a historical trend feature vector. Multiple historical trend feature vectors constitute a historical trend feature vector set. If the Euclidean distance between the real-time trend feature vector set and the historical trend feature vector set is greater than the fourth preset threshold, the difference vector between the two is calculated through matrix operation, and the difference vector constitutes a trend comparison data set. According to the trend comparison data set, vector graphics acceleration technology is used to generate a first dynamic trend chart through a preset rendering frequency and a preset scaling parameter. The first dynamic trend chart is a visual trend analysis result.

[0148] Regarding clustering using the K-means algorithm, for example, if historical data contains temperature series from the past 30 days, K-means can classify the data into two categories, such as stable trends and abnormal fluctuations, based on features such as the daily average temperature and daily fluctuations. Dynamic trend models can be represented using cluster center data, such as using the cluster center [24.5, 0.5] to represent the mean and fluctuation of a stable trend. This approach facilitates the analysis of long-term patterns.

[0149] When comparing the real-time and historical trend feature vector sets, the Euclidean distance is used to measure the difference. For example, the distance between the real-time vector [25.3, 25.8] and the historical vector [24.5, 0.5] is greater than the threshold value 2.0, indicating a trend offset. In one possible implementation, the difference vector is calculated by matrix operation, such as [0.8, 25.3], and the difference vector data constitutes a trend comparison data set. This method intuitively reflects the direction and magnitude of the deviation, which is convenient for subsequent analysis. Based on the trend comparison result data set, vector graphics acceleration technology is used for visualization. The preset rendering frequency can be set according to actual needs, for example, it can be 1 or 2 times per second. The preset zoom ratio can be set according to actual needs, which can be 1:100 or 1:150. The difference vector can be displayed by a line graph, for example, the horizontal axis is time and the vertical axis is temperature deviation, and the graph is dynamically updated. This visualization method intuitively presents trend changes and assists in equipment status assessment. The various links in the process in this embodiment support each other and help improve real-time analysis capabilities.

[0150] In one embodiment, the method further comprises:

[0151] S701, obtaining real-time interaction data.

[0152] S702: Extract feature point data and metadata from the real-time interaction data through event monitoring technology, where the feature point data and metadata constitute an interaction event feature set.

[0153] S703 , clustering the feature point data using a K-means algorithm to obtain zoom focus data and annotation clarity data, where the zoom focus data and the annotation clarity data constitute an interactive response parameter set.

[0154] S704: When the interaction response parameter set does not match the preset rendering parameter threshold, the zoom focus data and the annotation clarity data are adjusted using a preset algorithm, and the adjusted zoom focus data and the adjusted annotation clarity data constitute an interaction configuration data set.

[0155] S705 : Perform parameter mapping on the interaction configuration data set and the first dynamic trend chart to obtain interaction enhancement trend data, where the interaction enhancement trend data includes trend information of the interaction configuration.

[0156] Specifically, in real-time interactive data processing scenarios, capturing interaction event data from data streams is a critical step. Event monitoring technology is used to capture user actions on embedded system interfaces, such as touchscreen clicks or swipes. In one possible implementation, the monitor records 100 events per second, extracting feature points such as click coordinates and swipe speed, as well as metadata such as event timestamp and operation type, to form an interaction event feature set. For example, a click event might generate a feature vector [120, 150, 0.5, click], representing coordinates (120, 150), a speed of 0.5, and a click type. This approach facilitates capturing user behavior patterns. It should be noted that based on the interaction event feature set, the K-means algorithm clusters the feature points to determine zoom focus and annotation clarity. Specifically, cluster analysis identifies areas with high user activity concentration, for example, classifying coordinate data into three categories: high-frequency interaction areas, low-frequency interaction areas, and abnormal interaction areas. In one embodiment, the center point of the high-frequency interaction area is [130, 160], indicating that users frequently click this area. The zoom focus is set to the center point, and the annotation clarity is set to high to highlight it. This clustering method effectively identifies interactive hotspots. When the interactive response parameter set does not match the preset rendering parameter threshold, the zoom focus data and the annotation clarity data are adjusted so that the adjusted data is as close as possible to the data before adjustment, while at the same time making the adjusted data within the preset rendering parameter threshold. There can be multiple specific preset algorithms, which are technically easy to implement. The present invention does not limit this. Technicians can select existing algorithms as needed or summarize based on experience. For example, the following method can be used: the preset focus closest to the zoom focus A is B, the preset deviation distance is d, and the distance between A and B is greater than d. Then, among multiple pixels whose distance to B is less than d, find the pixel closest to A, for example, pixel C. The coordinate data corresponding to C is the adjusted zoom focus data, that is, the new zoom center point is obtained, and then the annotation clarity corresponding to the new zoom center point is set to high. Doing so ensures that the display effect meets expectations, while helping to save computing resources and improve rendering efficiency while meeting user needs as much as possible. It should be noted that the aforementioned high and low clarity of the annotations are used to express relative differences in clarity, and are intended to emphasize the relative amount of corresponding resource calls. Their specific meanings are related to specific devices. Specific embedded industrial control screens can specify the specific meanings of high, medium, and low clarity based on their own configurations and display needs. Regarding the preset rendering parameter thresholds, the present invention does not impose specific restrictions and can be set according to specific application scenarios. For example, the rendering parameter threshold can be specified as a zoom focus deviation less than a certain pixel value, such as 5 pixels, 10 pixels, 20 pixels, etc., or the clarity level difference can be specified as less than a certain value, such as 2, 3, or 5. Dynamic linkage technology maps interactive configuration data sets to trend charts to generate interactive enhanced trend data.For example, a trend chart displays temperature changes. Interactively configuring the data set adjusts the chart's scale, magnifying high-frequency interactive areas and clearly marking key points, such as temperature peaks. In one embodiment, a user clicks [130, 160] to trigger a zoomed-in chart, displaying detailed temperature fluctuations over a 10-second period. This approach enhances the user's interactive experience with the chart and provides a more intuitive view of data changes. The various steps in this embodiment support each other, facilitating a seamless integration of interactive data and trend analysis, providing a smooth user experience.

[0157] In one embodiment, the method further comprises:

[0158] S801: Acquire preset style configuration data and resolution adaptation requirement data.

[0159] S802 : According to the resolution adaptation requirement data, the style configuration data is adjusted by a CSS preprocessor to obtain a multi-resolution adaptation style sheet, where the multi-resolution adaptation style sheet constitutes a dynamic style configuration set.

[0160] S803, according to the dynamic style configuration set and the rendering frequency, the trend comparison data set is rendered at multi-resolution by a WebGL renderer to obtain rendering data containing icon dynamic information; the multi-resolution rendering includes: when the rendering frequency is lower than a fifth preset threshold, adjusting the frequency synchronization parameter.

[0161] S804: Use a K-means algorithm to cluster the feature points of the rendering data to obtain feature point annotation data.

[0162] S805 , generating a second dynamic trend chart including feature point annotations using a vector graphics rendering technology according to the scaling parameter and the feature point annotation data, wherein the second dynamic trend chart constitutes visual analysis report data.

[0163] Among them, a CSS preprocessor is a tool in the existing technology that extends the functionality of native CSS. WebGL refers to a 3D graphics protocol, and its full English name is Web Graphics Library. A WebGL renderer is a renderer based on WebGL technology. A style sheet, also known as a CSS style sheet (CSS is the abbreviation of Cascading Style Sheet, and its Chinese name is Cascading Style Sheet), can refer to a formatting technology for web page content, used to apply effects to web page display content. The fifth preset threshold may refer to a preset rendering frequency threshold.

[0164] Specifically, the fifth preset threshold can be set according to actual needs, and the present invention does not impose specific restrictions. For example, it can be 30fps, 50fps, 80fps, or 100fps. Receive style configuration data obtained from a preset templated style library. The preset templated style library can contain a variety of predefined CSS style templates, such as font size, color theme and margin configuration, suitable for different device resolutions. In response to the resolution adaptation requirements of the industrial control screen, the style parameters of the style configuration data are adjusted through variable calculation by the CSS preprocessor, and a style sheet adapted to multiple resolutions is generated using the adjusted configuration data. Multiple style sheets constitute a dynamic style configuration set. According to the dynamic style configuration set, combined with the shared rendering frequency parameters, the WebGL renderer performs a multi-resolution rendering task on the trend comparison data set, specifically including: if the rendering frame rate is lower than the fifth preset threshold, the frequency synchronization parameters are adjusted to obtain rendering data containing the dynamics of the chart. For the rendering data, the K-means algorithm is used to cluster the feature points of the rendering data to obtain feature point annotation data. Combining the scaling ratio and the feature point annotation data, a second dynamic trend chart including the feature point annotations is generated through vector graphics rendering technology, thereby obtaining visual analysis report data.

[0165] One possible implementation involves using various CSS preprocessors, such as Sass (Syntactically Awesome Style Sheets). Sass can dynamically adjust parameters for resolution adaptation. For example, for 1080p resolution, the font size might be set to 16px, while for 4K resolution, it might be adjusted to 20px to ensure visual consistency.

[0166] Regarding the WebGL renderer performing multi-resolution rendering tasks, WebGL uses the GPU (GPU stands for graphics processing unit) to accelerate the drawing of complex charts, such as line charts or heat maps. In one embodiment, the rendering frequency is set to 60fps. If it is detected that the rendering frequency drops below 50fps, the frequency synchronization parameters are adjusted, such as reducing the anti-aliasing level, to ensure smoothness. The adjusted rendering data contains dynamic information of the chart, such as animation transition effects, to enhance the user experience. For the rendering data, the K-means algorithm clusters the feature points to optimize the annotation position. For example, if a chart contains 1,000 data points, a clustering algorithm can be used to divide them into three categories: high-density areas, low-density areas, and isolated points. Combined with the scaling accuracy parameter, the annotation position is adjusted to avoid overlap. For example, the font of the high-density area annotation is bold and offset by 5px to ensure clarity and readability.

[0167] There are many vector graphics drawing technologies, such as SVG technology (SVG is the abbreviation of Scalable Vector Graphics, which means scalable vector graphics).

[0168] In one embodiment, the method further comprises:

[0169] S901: Extract core data from the visual analysis report data.

[0170] S902: Compress the core data using the LZ77 compression algorithm to obtain compressed data.

[0171] S903: Store the compressed data in a designated database.

[0172] S904 , determining the storage stability of the compressed data in the designated database to obtain a determination result, the determination result including whether the storage stability reaches a sixth preset threshold.

[0173] S905: Generate a storage report, which includes a write success rate, a time consumption, the core data, and the judgment result.

[0174] The LZ77 compression algorithm is an existing sliding window-based dictionary compression algorithm that achieves compression by replacing repeated character strings with triplets of distance, length, and subsequent characters. The sixth preset threshold may refer to a preset storage stability threshold.

[0175] Specifically, the sixth preset threshold can be set according to actual needs and is not specifically limited in the present invention. For example, it can be 90%, 99%, or 99.9%. The generated visualization analysis report data generally requires efficient storage to support subsequent queries. Core data is extracted from the visualization analysis report data and compressed using the LZ77 compression algorithm. The compressed data is stored in a designated database via a database interface. The storage stability is determined to meet the sixth preset threshold, and a storage report is generated. Using the LZ77 compression algorithm to process the core data can reduce storage space. For example, 1MB of raw data can be compressed to 300KB, improving transmission efficiency. The compressed data can be stored in a MySQL database (MySQL is a relational database management system) via a database interface. Storage stability must meet a preset threshold of 99.9%. Database interfaces generally support batch writes to reduce latency. For example, 1000 records can be written per second to ensure data integrity. The storage report records the write success rate and time consumption, providing a basis for system optimization. In the process of this embodiment, in a real-time monitoring scenario, style adaptation ensures consistent display across different devices, WebGL rendering ensures smooth charting, clustering optimizes annotation clarity, and compression and storage improve data management efficiency. Each link supports each other to form an efficient closed loop of trend chart generation and data processing.

[0176] In one embodiment, the method further comprises:

[0177] S1001, receiving a data query instruction and a resolution parameter of a target terminal.

[0178] S1002: Extract core data from the storage report through an interactive query interface according to the data query instruction.

[0179] S1003, using JSON parsing technology to perform structured processing on the core data to obtain structured query data.

[0180] S1004 : According to the resolution parameter, adjust the output format style parameter of the structured query data through CSS media query to obtain first display data adapted to the terminal.

[0181] S1005, using interactive zooming technology to calculate zoom focus coordinates.

[0182] S1006: Generate a dynamic trend chart including the zoom focus coordinates using Canvas drawing technology to obtain interactive enhanced chart data.

[0183] S1007: If the frame rate of the interactive enhanced chart data is lower than a seventh preset threshold, adjusting a rendering frequency parameter of the interactive enhanced chart data through a WebGL renderer to obtain terminal output data.

[0184] S1008: Output the terminal output data to the target terminal.

[0185] JSON parsing technology may refer to technology that converts JSON-formatted data into a data structure that can be processed by a program. CSS media queries are a query tool in CSS. Canvas is the name of an existing drawing technology. The seventh preset threshold may refer to a preset frame rate threshold.

[0186] Specifically, the seventh preset threshold can be set according to actual needs, and the present invention does not impose any specific restrictions. For example, it can be 30fps, 50fps, 80fps or 100fps. Query parameters are obtained from external query instructions, and the core data in the storage report is extracted through an interactive query interface. The core data is structured using JSON parsing technology to obtain structured query data. The query parameters may include a time range, a data type or a specific indicator, such as traffic data for the past 24 hours. The query instruction is transmitted in JSON format through a RESTful API (RESTful API refers to an application program interface design specification based on the HTTP protocol and following the REST architectural style. The HTTP protocol refers to the Hypertext Transfer Protocol, and the REST architecture is the name of an existing software architecture), including fields such as "start_time": "2025-05-0800:00:00" and "metric": "traffic". The interface needs to support high concurrent access, for example, processing 1,000 queries per second, and ensuring that the response time is less than 200 milliseconds. According to the structured query data, the resolution parameters of the target terminal are detected in combination with the terminal adaptation protocol, and the style parameters of the output format are adjusted through CSS media queries to obtain the first display data of the adapted terminal. For the display data of the adapted terminal, the interactive zooming technology is used to calculate the zoom focus coordinates, and then the Canvas drawing technology is used to generate a dynamic trend chart containing the focus annotation to obtain interactive enhanced chart data. If the frame rate of the interactive enhanced chart data is lower than the seventh preset threshold, the rendering frequency parameters are adjusted through the WebGL renderer to achieve optimized processing of the chart data. The obtained data is the terminal output data, and the terminal output data is output to the target terminal, and the target terminal realizes visual display. In one possible implementation method, the interactive query interface is designed based on GraphQL (GraphQL is the name of a query language) and supports dynamic field selection. After the interface parses the query parameters, the core data of the storage report, such as the original data set of the trend chart, is extracted from the MySQL database. Using JSON parsing technology to structure the core data is an important step in data normalization. The core data may include a timestamp and a numerical value, such as {"timestamp":"2025-05-0812:00:00","value":500}. In one embodiment, the JSON parser converts the data into a key-value pair structure to generate structured query data for subsequent processing. Preferably, the parsing process supports error checking, such as detecting missing fields to ensure data integrity. For example, when parsing 1,000 records, if 5 records are found to lack timestamps, a log is recorded and the invalid data is skipped. Detecting the resolution parameters of the target terminal based on the structured query data in combination with the terminal adaptation protocol is the core of achieving display optimization.For example, the terminal adaptation protocol obtains device information, such as a resolution of 1920x1080, through HTTP request headers. CSS media queries adjust style parameters based on resolution, such as setting a chart font size of 14px at 1080p and 18px at 2560x1440. As you can see, media queries dynamically load style sheets using min-width conditions to ensure consistent display across different devices.

[0187] In one embodiment, the style parameters also include chart margins and color themes, such as the background color of #333 in dark mode. The user zooms the chart using the mouse wheel, and the system calculates the focus coordinates based on the mouse position, such as (500, 300). Canvas drawing technology draws the magnified area based on the coordinates, for example, zooming in 2 times to display the details of the data point. Canvas supports drawing dynamic line charts, marking the data value at the focus, such as "traffic: 600". For example, a chart contains 500 data points, and after zooming, only 50 points near the focus are displayed to improve readability. If the frame rate of the interactive enhanced chart data is lower than the seventh preset threshold, such as 60fps, the rendering frequency parameters need to be adjusted to optimize performance. For example, when it is detected that the frame rate drops to 45fps, the WebGL renderer reduces the rendering accuracy, such as turning off some anti-aliasing effects. Exemplarily, the optimized chart still maintains smooth animation transitions, such as smooth movement of data points. WebGL uses the GPU to accelerate the drawing of complex graphics, such as heat maps, to reduce CPU load.

[0188] The process in this embodiment forms a closed loop from query to display. Query parameters drive data extraction, JSON parsing ensures data structuring, terminal adaptation optimizes display, and interactive zooming and WebGL rendering enhance the user experience. Each step supports each other to ensure efficient data visualization.

[0189] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0190] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties.

[0191] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, and the like.

[0192] Based on the same inventive concept, an embodiment of the present invention further provides a data processing system based on an embedded industrial control screen for implementing the aforementioned data processing method based on an embedded industrial control screen. The implementation solution provided by this system is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the data processing system based on an embedded industrial control screen provided below can be found in the above-mentioned limitations of the data processing method based on an embedded industrial control screen, and will not be repeated here.

[0193] In one embodiment, Figure 4 As shown, a data processing system based on an embedded industrial control screen is provided, comprising: a first data acquisition module, a segment calculation module, a priority module, a format conversion module, a data annotation module, a virtual unit module, a component extraction module and a data rendering module, wherein:

[0194] A first data acquisition module 11 is used to acquire multi-source heterogeneous sensor data;

[0195] A segmentation calculation module 12 is configured to process the multi-source heterogeneous sensor data using a sliding window technique to obtain segmented data; and calculate statistical feature data of the segmented data, wherein the statistical feature data constitute first statistical data.

[0196] A priority module 13 is configured to establish a task priority list based on the first statistical data and preset priority rules;

[0197] a format conversion module 14, configured to convert the segmented data into a standard format data set;

[0198] A data annotation module 15 is used to annotate the standard format data set with characteristic points to obtain an annotated data set;

[0199] A virtual unit module 16 is configured to process the labeled data set using a virtual processing unit according to the task priority list to obtain a multi-dimensional feature vector;

[0200] A component extraction module 17 is used to extract component data of the multidimensional feature vector, wherein the component data includes a trend component and a seasonal component;

[0201] The first data rendering module 18 is configured to use a visual rendering engine to process the component data in real time to obtain real-time trend data.

[0202] In one embodiment, the virtual unit module is used to implement the following steps:

[0203] Determine whether the amount of data in the labeled data set exceeds a first preset threshold, and obtain a determination result;

[0204] When the judgment result is that the data volume exceeds a first preset threshold, the labeled data set is divided according to the task priority list and the preset partitioning rule to obtain a partitioned data set;

[0205] Allocating multiple virtual processing units to extract feature data of each partitioned data set in parallel;

[0206] Processing the feature data using a principal component analysis algorithm to obtain a multidimensional projection;

[0207] Extracting key dimensional information of the multidimensional projection using a preset projection template to obtain a multidimensional feature set;

[0208] The multidimensional feature set is processed using data integration and vector concatenation to obtain a multidimensional feature vector.

[0209] In one embodiment, the first data rendering module is configured to implement the following steps:

[0210] According to a preset visualization template, the component data is converted into visualization feature data through data mapping technology;

[0211] The visual feature data is drawn in real time using a rendering engine, and the rendering results are dynamically refreshed to obtain real-time trend data.

[0212] In one embodiment, the system further comprises:

[0213] A second data acquisition module is used to acquire historical trend data;

[0214] a cosine calculation module, configured to calculate the cosine similarity between the real-time trend data and the historical trend data to obtain a first cosine similarity;

[0215] The data updating module is configured to update the historical trend data set according to the real-time trend data to obtain an updated historical trend data set when the first cosine similarity is lower than a third preset threshold.

[0216] In one embodiment, the system further comprises:

[0217] A first vector module, configured to construct a real-time trend feature vector set based on the real-time trend data;

[0218] A first clustering module is used to perform clustering processing on the historical trend data set using a K-means algorithm to obtain a clustering result, wherein the clustering result includes a dynamic trend model and cluster center point data;

[0219] A second vector module is used to construct a historical trend feature vector set based on the clustering results;

[0220] A Euclidean distance module, configured to calculate the Euclidean distance between the real-time trend feature vector set and the historical trend feature vector set to obtain a first Euclidean distance;

[0221] a third vector module, configured to calculate a difference vector between the real-time trend feature vector and the historical trend feature vector when the first Euclidean distance is greater than a fourth preset threshold, wherein the difference vector constitutes a trend comparison data set;

[0222] The trend comparison module is used to process the trend comparison data set by using vector graphics acceleration technology, a preset rendering frequency and a preset scaling parameter to obtain a first dynamic trend chart.

[0223] In one embodiment, the system further comprises:

[0224] A third data acquisition module is used to acquire real-time interaction data;

[0225] A feature extraction module is used to extract feature point data and metadata from the real-time interaction data through event monitoring technology, wherein the feature point data and the metadata constitute an interaction event feature set;

[0226] a second clustering module, configured to perform clustering processing on the feature point data using a K-means algorithm to obtain zoom focus data and annotation clarity data, wherein the zoom focus data and the annotation clarity data constitute an interactive response parameter set;

[0227] a data adjustment module, configured to adjust the zoom focus data and the annotation clarity data using a preset algorithm when the interaction response parameter set does not match a preset rendering parameter threshold, wherein the adjusted zoom focus data and the adjusted annotation clarity data constitute an interaction configuration data set;

[0228] The first mapping module is configured to perform parameter mapping between the interaction configuration data set and the first dynamic trend chart to obtain interaction enhancement trend data, wherein the interaction enhancement trend data includes trend information of the interaction configuration.

[0229] In one embodiment, the system further comprises:

[0230] A fourth data acquisition module, configured to acquire preset style configuration data and resolution adaptation requirement data;

[0231] A resolution adaptation module, configured to adjust the style configuration data through a CSS preprocessor according to the resolution adaptation requirement data to obtain a multi-resolution adaptation style sheet, wherein the multi-resolution adaptation style sheet constitutes a dynamic style configuration set;

[0232] a second data rendering module, configured to render the trend comparison dataset at multiple resolutions using a WebGL renderer based on the dynamic style configuration set and the rendering frequency, to obtain rendering data containing icon dynamic information; wherein the multi-resolution rendering includes adjusting a frequency synchronization parameter when the rendering frequency is lower than a fifth preset threshold;

[0233] A third clustering module is used to cluster the feature points of the rendering data using a K-means algorithm to obtain feature point annotation data;

[0234] The report data module is used to generate a second dynamic trend chart containing feature point annotations based on the scaling parameters and the feature point annotation data through vector graphics rendering technology, wherein the second dynamic trend chart constitutes visual analysis report data.

[0235] In one embodiment, the system further comprises:

[0236] A first data extraction module is used to extract core data from the visual analysis report data;

[0237] A data compression module, configured to compress the core data using an LZ77 compression algorithm to obtain compressed data;

[0238] A data storage module, used for storing the compressed data in a designated database;

[0239] a data determination module, configured to determine the storage stability of the compressed data in the designated database and obtain a determination result, wherein the determination result includes whether the storage stability reaches a sixth preset threshold;

[0240] The storage report module is used to generate a storage report, wherein the storage report includes the write success rate, time consumption, the core data and the judgment result.

[0241] In one embodiment, the system further comprises:

[0242] A fifth data acquisition module, configured to receive a data query instruction and a resolution parameter of a target terminal;

[0243] A second data extraction module is configured to extract core data from the storage report through an interactive query interface according to the data query instruction;

[0244] A structure processing module is used to perform structured processing on the core data using JSON parsing technology to obtain structured query data;

[0245] a format adjustment module, configured to adjust output format style parameters of the structured query data through CSS media query according to the resolution parameter to obtain first display data adapted to the terminal;

[0246] A focus calculation module, used for calculating the zoom focus coordinates using an interactive zooming technique;

[0247] A chart generation module, configured to generate a dynamic trend chart including the zoom focus coordinates by using a Canvas drawing technology to obtain interactively enhanced chart data;

[0248] a parameter adjustment module, configured to adjust a rendering frequency parameter of the interactive enhanced chart data through a WebGL renderer to obtain terminal output data when a frame rate of the interactive enhanced chart data is lower than a seventh preset threshold;

[0249] The data output module is used to output the terminal output data to the target terminal.

[0250] Each module in the aforementioned embedded industrial control screen-based data processing system can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of the embedded industrial control screen's processor in hardware form, or stored in the embedded industrial control screen's memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0251] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0252] The above-described embodiments merely represent several implementation methods of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A data processing method based on an embedded industrial control screen, characterized in that: The method comprises: Acquire multi-source heterogeneous sensor data; Processing the multi-source heterogeneous sensor data by a sliding window technology to obtain segmented data; calculating statistical feature data of the segmented data, wherein the statistical feature data constitutes first statistical data; Establishing a task priority list based on the first statistical data and preset priority rules; Converting the segmented data into a standard format data set; Performing feature point annotation on the standard format data set to obtain an annotated data set; Processing the labeled data set using a virtual processing unit according to a task priority list to obtain a multidimensional feature vector; Extracting component data of the multidimensional feature vector, wherein the component data includes a trend component and a seasonal component; Using a visual rendering engine to process the component data in real time to obtain real-time trend data; The step of processing the labeled data set using a virtual processing unit according to the task priority list to obtain a multi-dimensional feature vector includes the following steps: Determine whether the amount of data in the labeled data set exceeds a first preset threshold, and obtain a determination result; When the judgment result is that the data volume exceeds a first preset threshold, the labeled data set is divided according to the task priority list and the preset partitioning rule to obtain a partitioned data set; Allocating multiple virtual processing units to extract feature data of each partitioned data set in parallel; Processing the feature data using a principal component analysis algorithm to obtain a multidimensional projection; Extracting key dimensional information of the multidimensional projection using a preset projection template to obtain a multidimensional feature set; The multidimensional feature set is processed using data integration and vector concatenation to obtain a multidimensional feature vector.

2. The data processing method based on the embedded industrial control screen according to claim 1 is characterized in that: The using a visualization rendering engine to process the component data in real time includes: According to a preset visualization template, the component data is converted into visualization feature data through data mapping technology; The visual feature data is drawn in real time using a rendering engine, and the rendering results are dynamically refreshed to obtain real-time trend data.

3. The data processing method based on the embedded industrial control screen according to claim 2 is characterized in that: The method further comprises: Get historical trend data; Calculating the cosine similarity between the real-time trend data and the historical trend data to obtain a first cosine similarity; When the first cosine similarity is lower than a third preset threshold, the historical trend data set is updated according to the real-time trend data to obtain an updated historical trend data set.

4. The data processing method based on the embedded industrial control screen according to claim 3 is characterized in that: The method further comprises: Constructing a real-time trend feature vector set based on the real-time trend data; Using a K-means algorithm to cluster the historical trend data set to obtain a clustering result, wherein the clustering result includes a dynamic trend model and cluster center point data; Constructing a historical trend feature vector set based on the clustering results; Calculating the Euclidean distance between the real-time trend feature vector set and the historical trend feature vector set to obtain a first Euclidean distance; When the first Euclidean distance is greater than a fourth preset threshold, calculating a difference vector between the real-time trend feature vector and the historical trend feature vector, wherein the difference vector constitutes a trend comparison data set; The trend comparison data set is processed using vector graphics acceleration technology, a preset rendering frequency, and preset scaling parameters to obtain a first dynamic trend chart.

5. The data processing method based on the embedded industrial control screen according to claim 4 is characterized in that: The method further comprises: Get real-time interaction data; Extracting feature point data and metadata from the real-time interaction data through event monitoring technology, wherein the feature point data and metadata constitute an interaction event feature set; Performing clustering processing on the feature point data using a K-means algorithm to obtain zoom focus data and annotation clarity data, wherein the zoom focus data and the annotation clarity data constitute an interactive response parameter set; When the interaction response parameter set does not match the preset rendering parameter threshold, the zoom focus data and the annotation clarity data are adjusted by a preset algorithm, and the adjusted zoom focus data and the adjusted annotation clarity data constitute an interaction configuration data set; the interaction configuration data set is parameter mapped with the first dynamic trend chart to obtain interaction enhancement trend data, and the interaction enhancement trend data contains trend information of the interaction configuration.

6. The data processing method based on the embedded industrial control screen according to claim 4 is characterized in that: The method further comprises: Obtain preset style configuration data and resolution adaptation requirement data; According to the resolution adaptation requirement data, the style configuration data is adjusted by a CSS preprocessor to obtain a multi-resolution adaptation style sheet, wherein the multi-resolution adaptation style sheet constitutes a dynamic style configuration set; Rendering the trend comparison dataset at multiple resolutions using a WebGL renderer according to the dynamic style configuration set and the rendering frequency to obtain rendering data containing icon dynamic information; the multi-resolution rendering includes: adjusting a frequency synchronization parameter when the rendering frequency is lower than a fifth preset threshold; Using a K-means algorithm to cluster the feature points of the rendered data to obtain feature point annotation data; According to the scaling parameter and the feature point annotation data, a second dynamic trend chart including feature point annotations is generated by vector graphics rendering technology, and the second dynamic trend chart constitutes visual analysis report data.

7. The data processing method based on the embedded industrial control screen according to claim 6 is characterized in that: The method further comprises: Extracting core data from the visual analysis report data; Compress the core data using the LZ77 compression algorithm to obtain compressed data; storing the compressed data in a designated database; determining storage stability of the compressed data in the designated database to obtain a determination result, the determination result including whether the storage stability reaches a sixth preset threshold; Generate a storage report, which includes the write success rate, time consumption, the core data and the judgment result.

8. The data processing method based on the embedded industrial control screen according to claim 7 is characterized in that: The method further comprises: Receive data query instructions and resolution parameters of the target terminal; Extracting core data from the storage report through an interactive query interface according to the data query instruction; Using JSON parsing technology to structure the core data to obtain structured query data; According to the resolution parameter, adjusting the output format style parameter of the structured query data through CSS media query to obtain first display data adapted to the terminal; Interactive zooming technology is used to calculate the zoom focus coordinates; Generate a dynamic trend chart including the zoom focus coordinates by using Canvas drawing technology to obtain interactive enhanced chart data; If the frame rate of the interactive enhanced chart data is lower than a seventh preset threshold, adjusting a rendering frequency parameter of the interactive enhanced chart data through a WebGL renderer to obtain terminal output data; The terminal outputs the data to the target terminal.

9. A data processing system based on an embedded industrial control screen, characterized in that: The system is used to implement a data processing method based on an embedded industrial control screen as described in any one of claims 1 to 8, comprising: A first data acquisition module is used to acquire multi-source heterogeneous sensor data; A segmentation calculation module is used to process the multi-source heterogeneous sensor data by a sliding window technology to obtain segmented data; calculate statistical feature data of the segmented data, wherein the statistical feature data constitutes first statistical data; a priority module, configured to establish a task priority list based on the first statistical data and preset priority rules; A format conversion module, configured to convert the segmented data into a standard format data set; A data annotation module, configured to annotate the standard format data set with characteristic points to obtain an annotated data set; a virtual unit module, configured to process the labeled data set using a virtual processing unit according to a task priority list to obtain a multidimensional feature vector; A component extraction module, configured to extract component data of the multidimensional feature vector, wherein the component data includes a trend component and a seasonal component; The data rendering module is used to use a visual rendering engine to process the component data in real time to obtain real-time trend data.

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