Dynamic processing method and system for intelligent supervision data

Abstract

The invention relates to a dynamic processing method and system for intelligent supervision data, and relates to the technical field of data processing, and the method comprises the steps: obtaining real-time supervision data, carrying out the data structure classification of the real-time supervision data, obtaining a first data set, a second data set and a third data set, and performing data processing on the first data set, the second data set and the third data set to obtain a first data segment, a second data segment and a third data segment, and importing the second data segment and the third data segment into a preset monitoring data center for abnormality diagnosis. The method comprises the steps of obtaining a historical diagnosed data segment, a current diagnosed data segment and a future diagnosed data segment according to a time node and a data type, performing data segment splicing on the historical diagnosed data segment, the current diagnosed data segment and the future diagnosed data segment according to the time node and the data type to obtain a diagnosis data segment, and sending the diagnosis data segment to a target terminal for display. The timeliness and effectiveness of supervision work are improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to a method and system for dynamically processing intelligent supervision data. Background Art

[0002] With the rapid development of information technology and the widespread promotion of intelligent applications, the construction industry is undergoing a profound transformation from traditional models to intelligent and digital transformation. In this context, the concept and practice of smart construction sites have provided new ideas and technical means for the supervision and management of engineering projects. As an important part of smart construction sites, smart supervision aims to achieve comprehensive perception, real-time interconnection, intelligent early warning and auxiliary decision-making of construction sites through the integrated application of modern information technologies such as the Internet of Things, big data, cloud computing and artificial intelligence, thereby greatly improving the efficiency and accuracy of engineering supervision.

[0003] However, in the actual practice of smart supervision, the processing and analysis of supervision data still face many challenges. On the one hand, the amount of data generated at the construction site is huge and diverse, including but not limited to video surveillance data, environmental monitoring data, construction machinery operation data, personnel activity data, etc. These data have the characteristics of high real-time requirements, high processing difficulty, and low value density. Traditional data processing methods are often difficult to cope with such a complex data environment, resulting in low data utilization and difficulty in effectively mining the potential value of data. On the other hand, most of the existing supervision data processing systems focus on static storage and post-analysis of data, and lack the ability of dynamic processing and real-time response. This makes it difficult for supervisors to obtain the latest developments on the construction site in a timely manner, and they cannot respond quickly to potential safety hazards or quality problems, which affects the timeliness and effectiveness of supervision work. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the present application provides a method and system for dynamic processing of intelligent supervision data.

[0005] In the first aspect, the present application provides a method for dynamically processing intelligent supervision data, which adopts the following technical solutions: A dynamic processing method for intelligent supervision data, comprising: Acquire real-time monitoring data, which is monitoring data of different monitoring objects in a preset monitoring area at different times; The real-time monitoring data is divided into data structure categories to obtain a first data set, a second data set and a third data set, wherein the first data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times, which is a complete set of structured data; the second data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times, which is an incomplete set of structured data; and the third data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times, which is an unstructured data; Processing the first data set, the second data set, and the third data set to obtain a first data segment, a second data segment, and a third data segment; Establishing a data connection with a preset monitoring data center, and determining whether the preset monitoring data center has the first data segment, and if so, importing the second data segment and the third data segment into the preset monitoring data center for abnormality diagnosis, and obtaining historically diagnosed data segments, currently diagnosed data segments, and future diagnosed data segments; The historical diagnosed data segments, the current diagnosed data segments and the future diagnosed data segments are concatenated according to time nodes and data types to obtain diagnostic data segments of different data types in different time periods; The diagnostic data segment is sent to a target terminal for display, and the target terminal is a terminal device used by a monitoring person.

[0006] By adopting the above technical solution, the supervision data in the preset monitoring area is obtained in real time, which ensures the timeliness and accuracy of the data and provides a solid foundation for subsequent data processing and analysis. Furthermore, by dividing the data structure categories of the real-time supervision data, the complex data is orderly divided into the first, second and third data sets, corresponding to the complete structured data, incomplete structured data and unstructured data, respectively, which greatly improves the efficiency and accuracy of data processing. The divided data sets are processed in a targeted manner to obtain the first, second and third data segments for subsequent analysis and diagnosis. Then, a data connection with the preset monitoring data center is established, and it is intelligently determined whether some data segments already exist in the data center, thereby avoiding repeated processing and saving resources. For non-existent data segments, the second and third data segments are imported into the data center for abnormal diagnosis, which can timely discover potential problems and generate historical, current and future diagnosed data segments, which provides the possibility for comprehensive evaluation of the supervision situation. The diagnostic data segments of different data types in different time periods are spliced ​​to form a complete and orderly data chain. This data splicing method not only makes it easier for monitoring personnel to intuitively understand the changing trends of supervision conditions, but also helps monitoring personnel quickly locate problems and improve the efficiency of problem solving. Finally, the integrated diagnostic data segments are sent to the target terminal for display, allowing monitoring personnel to grasp the supervision dynamics in real time and respond in a timely manner, further improving the timeliness and effectiveness of supervision work.

[0007] In a preferred example, the present application may be further configured as follows: the data processing of the first data set, the second data set, and the third data set to obtain the first data segment, the second data segment, and the third data segment includes: Performing digital semantic analysis on the third data set, and partially structurally processing the semantic analysis results obtained by the analysis to obtain a fourth data set; Aggregating the first data set, the second data set and the fourth data set to obtain an aggregated data set; Integrate the data in the summary data set to obtain supervision integrated data; The supervision integrated data is subjected to data segmentation deduction processing to obtain a first data segment, a second data segment and a third data segment.

[0008] In a preferred example, the present application may be further configured as follows: the first data set, the second data set and the fourth data set are aggregated to obtain an aggregated data set, including: Generate corresponding data set tasks according to the first data set, the second data set and the fourth data set; Constructing a summary data model and loading summary processing rules, wherein the summary processing rules include data cleaning rules, data review rules and data summary processing logic; According to the data integration task, data is collected from the first data set, the second data set and the fourth data set and input into the summary data model to obtain a summary data set.

[0009] In a preferred example, the present application may be further configured as follows: the data in the summary data set is integrated to obtain supervision integrated data, including: Determining a data aggregation task according to the aggregated data set; Build a data aggregation model and load data aggregation logic, which includes data governance logic, data lineage processing logic, data watermark processing logic, data fingerprint processing logic and data annotation processing logic; According to the data aggregation task, data is collected from the aggregated data set and input into the data aggregation model to obtain supervision integrated data.

[0010] In a preferred example, the present application may be further configured as follows: performing segmented deduction processing on the supervision integrated data to obtain a first data segment, a second data segment, and a third data segment, including: Determining object supervision data at different time nodes according to the supervision integrated data; Create a data coordinate system, where the X-axis of the data coordinate system represents different time nodes, and the Y-axis of the data coordinate system represents data unit parameters of different object dimensions; Importing the object supervision data into the data coordinate system respectively to obtain a data change curve corresponding to the object supervision data; Determine data change characteristics at different time nodes according to the data change curve, and reorganize the data change characteristics according to different supervision objects to obtain data feature sets corresponding to different supervision objects; Arrange each of the data feature sets in a matrix according to time sequence to obtain a data feature matrix corresponding to each supervision object; Deducing the data change characteristics of the data feature matrix according to the time period to obtain the prediction feature matrix corresponding to different supervision objects in the future preset time period; Determine a future data feature set according to the prediction feature matrix, and import the future data feature set into the data coordinate system to obtain a future data curve; Performing curve segmentation processing on the data change curve and the future data curve according to the time nodes to obtain a first data curve corresponding to the historical time period, a second data curve corresponding to the current time period, and a third data curve corresponding to the future time period; Data features are extracted from the first data curve, the second data curve, and the third data curve, and the extracted data features are sorted in time series to obtain a first data segment, a second data segment, and a third data segment.

[0011] In a preferred example, the present application may be further configured as follows: the step of determining whether the first data segment exists in the preset monitoring data center further includes: If the first data segment does not exist in the preset monitoring data center or part of the first data segment exists, the first data segment, the second data segment and the third data segment are imported into the preset monitoring data center for abnormal diagnosis to obtain historical diagnosed data segments, current diagnosed data segments and future diagnosed data segments.

[0012] In the second aspect, the present application provides a dynamic processing system for intelligent supervision data, which adopts the following technical solutions: A dynamic processing system for intelligent supervision data, comprising: A data acquisition module is used to acquire real-time monitoring data, wherein the real-time monitoring data is monitoring data of different monitoring objects in a preset monitoring area at different times; A data division module, used for dividing the real-time monitoring data into data structure categories to obtain a first data set, a second data set and a third data set, wherein the first data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times as complete structured data, the second data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times as incomplete structured data, and the third data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times as unstructured data; a data processing module, configured to process the first data set, the second data set, and the third data set to obtain a first data segment, a second data segment, and a third data segment; an abnormality diagnosis module, used to establish a data connection with a preset monitoring data center, and determine whether the preset monitoring data center has the first data segment, and if so, import the second data segment and the third data segment into the preset monitoring data center for abnormality diagnosis, and obtain historical diagnosed data segments, current diagnosed data segments, and future diagnosed data segments; A data splicing module, used to splice the historical diagnosed data segments, the current diagnosed data segments and the future diagnosed data segments according to time nodes and data types, to obtain diagnostic data segments of different data types in different time periods; The real-time display module is used to send the diagnostic data segment to a target terminal for display. The target terminal is a terminal device used by monitoring personnel.

[0013] In a possible implementation, when the data processing module processes the first data set, the second data set, and the third data set to obtain a first data segment, a second data segment, and a third data segment, the data processing module is specifically configured to: Performing digital semantic analysis on the third data set, and partially structurally processing the semantic analysis results obtained by the analysis to obtain a fourth data set; Aggregating the first data set, the second data set and the fourth data set to obtain an aggregated data set; Integrate the data in the summary data set to obtain supervision integrated data; The supervision integrated data is subjected to data segmentation deduction processing to obtain a first data segment, a second data segment and a third data segment.

[0014] In another possible implementation, when the data processing module aggregates the first data set, the second data set, and the fourth data set to obtain an aggregated data set, the data processing module is specifically configured to: Generate corresponding data set tasks according to the first data set, the second data set and the fourth data set; Constructing a summary data model and loading summary processing rules, wherein the summary processing rules include data cleaning rules, data review rules and data summary processing logic; According to the data integration task, data is collected from the first data set, the second data set and the fourth data set and input into the summary data model to obtain a summary data set.

[0015] In another possible implementation, when the data processing module integrates the data in the summary data set to obtain the supervision integrated data, it is specifically used to: Determining a data aggregation task according to the aggregated data set; Build a data aggregation model and load data aggregation logic, which includes data governance logic, data lineage processing logic, data watermark processing logic, data fingerprint processing logic and data annotation processing logic; According to the data aggregation task, data is collected from the aggregated data set and input into the data aggregation model to obtain supervision integrated data.

[0016] In another possible implementation, when the data processing module performs segmented deduction processing on the supervision integrated data to obtain the first data segment, the second data segment, and the third data segment, it is specifically used to: Determining object supervision data at different time nodes according to the supervision integrated data; Create a data coordinate system, where the X-axis of the data coordinate system represents different time nodes, and the Y-axis of the data coordinate system represents data unit parameters of different object dimensions; Importing the object supervision data into the data coordinate system respectively to obtain a data change curve corresponding to the object supervision data; Determine data change characteristics at different time nodes according to the data change curve, and reorganize the data change characteristics according to different supervision objects to obtain data feature sets corresponding to different supervision objects; Arrange each of the data feature sets in a matrix according to time sequence to obtain a data feature matrix corresponding to each supervision object; Deducing the data change characteristics of the data feature matrix according to the time period to obtain the prediction feature matrix corresponding to different supervision objects in the future preset time period; Determine a future data feature set according to the prediction feature matrix, and import the future data feature set into the data coordinate system to obtain a future data curve; Performing curve segmentation processing on the data change curve and the future data curve according to the time nodes to obtain a first data curve corresponding to the historical time period, a second data curve corresponding to the current time period, and a third data curve corresponding to the future time period; Data features are extracted from the first data curve, the second data curve, and the third data curve, and the extracted data features are sorted in time series to obtain a first data segment, a second data segment, and a third data segment.

[0017] In another possible implementation, the system further includes: an abnormality diagnosis module, wherein: The abnormality diagnosis module is used to import the first data segment, the second data segment and the third data segment into the preset monitoring data center for abnormality diagnosis when the first data segment does not exist or part of the first data segment exists in the preset monitoring data center, so as to obtain historical diagnosed data segments, current diagnosed data segments and future diagnosed data segments.

[0018] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for dynamic processing of intelligent supervision data are implemented.

[0019] In a fourth aspect, the present application provides a computer storage medium, as follows: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for dynamic processing of intelligent supervision data.

[0020] In summary, this application has the following beneficial technical effects: Real-time acquisition of supervision data within the preset monitoring area ensures the timeliness and accuracy of the data, and provides a solid foundation for subsequent data processing and analysis. Furthermore, by dividing the data structure categories of real-time supervision data, the complex data is orderly divided into the first, second and third data sets, corresponding to complete structured data, incomplete structured data and unstructured data, respectively, which greatly improves the efficiency and accuracy of data processing. Targeted data processing is performed on the divided data sets to obtain the first, second and third data segments for subsequent analysis and diagnosis. Then, a data connection with the preset monitoring data center is established, and it is intelligently determined whether some data segments already exist in the data center, thereby avoiding repeated processing and saving resources. For non-existent data segments, the second and third data segments are imported into the data center for abnormal diagnosis, which can timely discover potential problems and generate historical, current and future diagnosed data segments, which provides the possibility for a comprehensive assessment of the supervision situation. The diagnostic data segments of different data types in different time periods are spliced ​​to form a complete and orderly data chain. This data splicing method not only facilitates monitoring personnel to intuitively understand the changing trend of the supervision situation, but also helps monitoring personnel to quickly locate the problem and improve the efficiency of problem solving. Finally, the integrated diagnostic data segments are sent to the target terminal for display, allowing monitoring personnel to grasp the supervision dynamics in real time and anywhere, and respond promptly, further improving the timeliness and effectiveness of the supervision work. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a method for dynamic processing of intelligent supervision data in one of the embodiments of the present application.

[0022] Figure 2 It is a structural diagram of a dynamic processing system for intelligent supervision data according to one embodiment of the present application.

[0023] Figure 3 It is a principle block diagram of an electronic device in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The following is combined with Figure 1 To Attachment Figure 3 This application is described in further detail.

[0025] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0028] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0029] The embodiment of the present application provides a method for dynamically processing smart supervision data, which is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. Figure 1 As shown, the method includes: Step S10: Acquire real-time monitoring data.

[0030] Among them, the real-time supervision data is the monitoring data of different supervision objects in the preset monitoring area at different times.

[0031] For the embodiments of the present application, real-time monitoring data represents monitoring information obtained for a preset monitoring area at a specific time point or time period. These data are dynamic and will be updated over time. The preset monitoring area refers to a geographical range or specific area that is determined in advance and needs to be monitored. The supervision object refers to an object, event or phenomenon selected as a monitoring target within the monitoring area, such as a building, equipment, environmental quality, personnel, etc. The monitoring data at different times are used to represent the specific data collected for each supervision object at each monitoring time point, and these data may include various types of data such as temperature, humidity, pressure, displacement, image, etc.

[0032] Step S11: Divide the real-time monitoring data into data structure categories to obtain a first data set, a second data set and a third data set.

[0033] Among them, the first data set is a collection of complete structured data of the monitoring data of the monitoring objects in the preset monitoring area at different times, the second data set is a collection of incomplete structured data of the monitoring data of the monitoring objects in the preset monitoring area at different times, and the third data set is a collection of unstructured data of the monitoring data of the monitoring objects in the preset monitoring area at different times.

[0034] For the embodiments of the present application, the completely structured data refers to the digital data obtained by monitoring the supervision object, the incomplete structured data refers to the partially digital data obtained by monitoring the supervision object and the data in JSON and XML formats, and the unstructured data refers to the images, audio, video and other data obtained by monitoring the supervision object.

[0035] Step S12: Process the first data set, the second data set and the third data set to obtain a first data segment, a second data segment and a third data segment.

[0036] For the embodiment of the present application, the third data set is subjected to digital semantic analysis, and the semantic analysis results obtained by the analysis are partially structured to obtain a fourth data set, the first data set, the second data set and the fourth data set are summarized to obtain a summarized data set, the data in the summarized data set are integrated to obtain supervision integrated data, and the supervision integrated data is subjected to data segmentation deduction processing to obtain a first data segment, a second data segment and a third data segment.

[0037] Specifically, for the third data set, such as pictures, audio or video, natural language processing (NLP) or other related technologies are used to understand the content, and then based on the results of the understanding, attempts are made to convert it into a structured format that is easier to manage and analyze.

[0038] For images and videos, computer vision technology can be used, while for audio, speech recognition technology can be used. By generating a digital semantic analysis result containing key information extraction, classification, summary, etc., this result is an understanding and analysis of the original unstructured data content.

[0039] It should be noted that partial structured processing based on the results of digital semantic analysis will produce a partially structured data set; this data set retains some of the original characteristics of the original unstructured data, and also includes structured information extracted after semantic understanding. By associating the two, it not only maintains the richness of the original data, but also facilitates subsequent data processing and analysis, and can provide a more comprehensive data perspective to help companies discover potential patterns and trends, thereby making more accurate business decisions.

[0040] Specifically, according to the first data set, the second data set and the fourth data set, corresponding data set tasks are generated, a summary data model is constructed and summary processing rules are loaded, the summary processing rules include data cleaning rules, data review rules and data summary processing logic, according to the data integration task, data is collected from the first data set, the second data set and the fourth data set and input into the summary data model to obtain a summary data set.

[0041] The data collection task refers to a series of operations and workflows defined in the data collection process to achieve data processing goals. The main purpose of generating data collection tasks is to collect data from different sources in an orderly manner to ensure that the data can be processed according to the predetermined process. Through the systematic and standardized data processing process, the efficiency of data integration is improved and manual intervention and errors are reduced.

[0042] Specifically, the data aggregation task is determined according to the aggregated data set, a data aggregation model is constructed and the data aggregation logic is loaded. The data aggregation logic includes data governance logic, data lineage processing logic, data watermark processing logic, data fingerprint processing logic and data labeling processing logic. According to the data aggregation task, data is collected from the aggregated data set and input into the data aggregation model to obtain the supervision integrated data.

[0043] In the embodiment of the present application, the description of the data aggregation logic is as follows: Data governance logic: defines how to ensure data quality, consistency, and compliance; for example, setting data cleaning rules, data quality inspection standards, etc. Data lineage processing logic: Track the entire process of data from source to final use to ensure that the source and historical changes of data can be traced; Data watermark processing logic: adding invisible identifiers to data to ensure data ownership and integrity; Data fingerprint processing logic: Calculate the unique identifier of the data to detect whether the data has been tampered with or copied; Data annotation processing logic: automatically add labels according to the content of the data, such as classification, clustering, etc., to facilitate subsequent retrieval and analysis.

[0044] Specifically, the object supervision data at different time nodes are determined according to the integrated supervision data, and a data coordinate system is created. The X-axis of the data coordinate system represents different time nodes, and the Y-axis of the data coordinate system represents data unit parameters of different object dimensions. The object supervision data are respectively imported into the data coordinate system to obtain a data change curve corresponding to the object supervision data. The data change characteristics at different time nodes are determined according to the data change curve, and the data change characteristics are reorganized according to different supervision objects to obtain a data feature set corresponding to different supervision objects. Arrange each data feature set in a matrix according to time series to obtain the data feature matrix corresponding to each supervision object, deduce the data change characteristics of the data feature matrix according to the time period, and obtain the prediction feature matrix corresponding to different supervision objects in the future preset time period, determine the future data feature set according to the prediction feature matrix, and import the future data feature set into the data coordinate system to obtain the future data curve, perform curve segmentation processing on the data change curve and the future data curve according to the time node, and obtain the first data curve corresponding to the historical time period, the second data curve corresponding to the current time period, and the third data curve corresponding to the future time period, extract the data features in the first data curve, the second data curve and the third data curve, and sort the extracted data features in time series to obtain the first data segment, the second data segment and the third data segment.

[0045] In the embodiment of the present application, the method of deducing data change characteristics includes but is not limited to implementing it using a bidirectional LTSM model.

[0046] Step S13: Establish a data connection with the preset monitoring data center, and determine whether the first data segment exists in the preset monitoring data center. If so, import the second data segment and the third data segment into the preset monitoring data center for abnormal diagnosis to obtain historical diagnosed data segments, current diagnosed data segments, and future diagnosed data segments.

[0047] For the embodiment of the present application, when the first data segment does not exist or part of the first data segment exists in the preset monitoring data center, the first data segment, the second data segment and the third data segment are imported into the preset monitoring data center for abnormal diagnosis to obtain historical diagnosed data segments, current diagnosed data segments and future diagnosed data segments.

[0048] Step S14: historical diagnosed data segments, current diagnosed data segments and future diagnosed data segments are concatenated according to time nodes and data types to obtain diagnostic data segments of different data types in different time periods.

[0049] Step S15: Send the diagnostic data segment to the target terminal for display.

[0050] Among them, the target terminal is the terminal device used by the monitoring personnel.

[0051] In the embodiment of the present application, the supervision data in the preset monitoring area is obtained in real time, which ensures the timeliness and accuracy of the data and provides a solid foundation for subsequent data processing and analysis. Further, by dividing the data structure category of the real-time supervision data, the complex data is orderly divided into the first, second and third data sets, corresponding to the complete structured data, the incomplete structured data and the unstructured data, respectively, which greatly improves the efficiency and accuracy of data processing. The divided data sets are subjected to targeted data processing to obtain the first, second and third data segments for subsequent analysis and diagnosis. Then, a data connection with the preset monitoring data center is established, and it is intelligently determined whether some data segments already exist in the data center, thereby avoiding repeated processing and saving resources. For the data segments that do not exist, the second and third data segments are imported into the data center for abnormal diagnosis, which can timely discover potential problems and generate historical, current and future diagnosed data segments, which provides the possibility for comprehensive evaluation of the supervision situation. The diagnostic data segments of different data types in different time periods are spliced ​​to form a complete and orderly data chain. This data splicing method not only facilitates the monitoring personnel to intuitively understand the changing trend of the supervision situation, but also helps the monitoring personnel to quickly locate the problem and improve the efficiency of problem solving. Finally, the integrated diagnostic data segments are sent to the target terminal for display, allowing monitoring personnel to grasp the supervision dynamics in real time and anywhere, and respond promptly, further improving the timeliness and effectiveness of the supervision work.

[0052] The above-mentioned embodiment introduces a dynamic processing method of intelligent supervision data from the perspective of method flow. The following embodiment introduces a dynamic processing system of intelligent supervision data from the perspective of virtual module or virtual unit. For details, please refer to the following embodiment.

[0053] The present application embodiment provides a dynamic processing system 20 for intelligent supervision data, such as Figure 2 As shown, Figure 2 A schematic diagram of the structure of a dynamic processing system for intelligent supervision data provided in an embodiment of the present application. The system 20 may specifically include: The data acquisition module 21 is used to acquire real-time monitoring data, which is monitoring data of different monitoring objects in a preset monitoring area at different times; The data division module 22 is used to divide the real-time monitoring data into data structure categories to obtain a first data set, a second data set and a third data set, wherein the first data set is a set of monitoring data of the monitoring objects in the preset monitoring area at different times, which is a complete set of structured data; the second data set is a set of monitoring data of the monitoring objects in the preset monitoring area at different times, which is an incomplete set of structured data; and the third data set is a set of monitoring data of the monitoring objects in the preset monitoring area at different times, which is an unstructured data. A data processing module 23, configured to process the first data set, the second data set and the third data set to obtain a first data segment, a second data segment and a third data segment; The abnormality diagnosis module 24 is used to establish a data connection with a preset monitoring data center, and determine whether the preset monitoring data center has the first data segment. If so, the second data segment and the third data segment are imported into the preset monitoring data center for abnormality diagnosis, and the historical diagnosed data segment, the current diagnosed data segment and the future diagnosed data segment are obtained; The data splicing module 25 is used to splice the historical diagnosed data segments, the current diagnosed data segments and the future diagnosed data segments according to the time nodes and data types to obtain the diagnosis data segments of different data types in different time periods; The real-time display module 26 is used to send the diagnostic data segment to the target terminal for display. The target terminal is a terminal device used by the monitoring personnel.

[0054] In a possible implementation of the embodiment of the present application, when the data processing module 23 processes the first data set, the second data set, and the third data set to obtain the first data segment, the second data segment, and the third data segment, it is specifically used to: Performing digital semantic analysis on the third data set, and partially structuring the semantic analysis results obtained by the analysis to obtain a fourth data set; Aggregating the first data set, the second data set, and the fourth data set to obtain an aggregated data set; Integrate the data in the summary data set to obtain supervision integrated data; The supervision integrated data is processed by segmentation deduction to obtain a first data segment, a second data segment and a third data segment.

[0055] In another possible implementation of the embodiment of the present application, when the data processing module 23 aggregates the first data set, the second data set, and the fourth data set to obtain the aggregated data set, it is specifically used to: Generate corresponding data set tasks according to the first data set, the second data set and the fourth data set; Build a summary data model and load summary processing rules, which include data cleaning rules, data review rules and data summary processing logic; According to the data integration task, data is collected from the first data set, the second data set and the fourth data set and input into the summary data model to obtain the summary data set.

[0056] In another possible implementation of the embodiment of the present application, when the data processing module 23 integrates the data in the summary data set to obtain the supervision integrated data, it is specifically used to: Determine data aggregation tasks based on the aggregated data set; Build a data aggregation model and load data aggregation logic. The data aggregation logic includes data governance logic, data lineage processing logic, data watermark processing logic, data fingerprint processing logic, and data annotation processing logic. According to the data aggregation task, data is collected from the aggregated data set and input into the data aggregation model to obtain the supervision integrated data.

[0057] In another possible implementation of the embodiment of the present application, when the data processing module 23 performs segmented deduction processing on the supervision integrated data to obtain the first data segment, the second data segment, and the third data segment, it is specifically used to: Determine the object supervision data at different time nodes based on the supervision integrated data; Create a data coordinate system, where the X-axis of the data coordinate system is different time nodes, and the Y-axis of the data coordinate system is the data unit parameters of different object dimensions; Importing the object supervision data into the data coordinate system respectively, and obtaining the data change curve corresponding to the object supervision data; Determine the data change characteristics of different time nodes according to the data change curve, and reorganize the data change characteristics according to different supervision objects to obtain the data feature sets corresponding to different supervision objects; Arrange each data feature set in a matrix according to time sequence to obtain the data feature matrix corresponding to each supervision object; The data feature matrix is ​​deduced according to the time period to determine the data change characteristics, and the prediction feature matrix corresponding to different supervision objects in the future preset time period is obtained; Determine the future data feature set according to the prediction feature matrix, and import the future data feature set into the data coordinate system to obtain the future data curve; Perform curve segmentation processing on the data change curve and the future data curve according to the time nodes to obtain a first data curve corresponding to the historical time period, a second data curve corresponding to the current time period, and a third data curve corresponding to the future time period; Data features are extracted from the first data curve, the second data curve, and the third data curve, and the extracted data features are sorted in time series to obtain a first data segment, a second data segment, and a third data segment.

[0058] In another possible implementation of the embodiment of the present application, the system 20 further includes: a diagnosis abnormality module, wherein: The abnormality diagnosis module is used to import the first data segment, the second data segment and the third data segment into the preset monitoring data center for abnormality diagnosis when the first data segment does not exist or part of the first data segment exists in the preset monitoring data center, so as to obtain the historical diagnosed data segments, the current diagnosed data segments and the future diagnosed data segments.

[0059] Technical personnel in the relevant field can clearly understand that, for the convenience and simplicity of description, the specific working process of the dynamic processing system 20 of intelligent supervision data described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0060] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0061] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0062] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0063] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0064] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0065] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0066] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0067] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by 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 they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0068] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A dynamic processing method for intelligent supervision data, characterized in that: include: Acquire real-time monitoring data, which is monitoring data of different monitoring objects in a preset monitoring area at different times; The real-time monitoring data is divided into data structure categories to obtain a first data set, a second data set and a third data set, wherein the first data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times, which is a complete set of structured data; the second data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times, which is an incomplete set of structured data; and the third data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times, which is an unstructured data; Processing the first data set, the second data set, and the third data set to obtain a first data segment, a second data segment, and a third data segment; Establishing a data connection with a preset monitoring data center, and determining whether the preset monitoring data center has the first data segment, and if so, importing the second data segment and the third data segment into the preset monitoring data center for abnormality diagnosis, and obtaining historically diagnosed data segments, currently diagnosed data segments, and future diagnosed data segments; The historical diagnosed data segments, the current diagnosed data segments and the future diagnosed data segments are concatenated according to time nodes and data types to obtain diagnostic data segments of different data types in different time periods; The diagnostic data segment is sent to a target terminal for display, and the target terminal is a terminal device used by a monitoring person.

2. According to the method for dynamic processing of intelligent supervision data of claim 1, it is characterized in that: The performing data processing on the first data set, the second data set, and the third data set to obtain a first data segment, a second data segment, and a third data segment includes: Performing digital semantic analysis on the third data set, and partially structuring the semantic analysis results obtained by the analysis to obtain a fourth data set; Aggregating the first data set, the second data set and the fourth data set to obtain an aggregated data set; Integrate the data in the summary data set to obtain supervision integrated data; The supervision integrated data is subjected to data segmentation deduction processing to obtain a first data segment, a second data segment and a third data segment.

3. According to claim 2, a dynamic processing method of intelligent supervision data is characterized in that: The step of aggregating the first data set, the second data set, and the fourth data set to obtain a summarized data set includes: Generate corresponding data set tasks according to the first data set, the second data set and the fourth data set; Constructing a summary data model and loading summary processing rules, wherein the summary processing rules include data cleaning rules, data review rules and data summary processing logic; According to the data integration task, data is collected from the first data set, the second data set and the fourth data set and input into the summary data model to obtain a summary data set.

4. According to claim 2, a dynamic processing method of intelligent supervision data is characterized in that: The step of integrating the data in the summary data set to obtain supervision integrated data includes: Determining a data aggregation task according to the aggregated data set; Build a data aggregation model and load data aggregation logic, which includes data governance logic, data lineage processing logic, data watermark processing logic, data fingerprint processing logic and data annotation processing logic; According to the data aggregation task, data is collected from the aggregated data set and input into the data aggregation model to obtain supervision integrated data.

5. According to claim 2, a dynamic processing method of intelligent supervision data is characterized in that: The step of performing segmented deduction processing on the supervision integrated data to obtain a first data segment, a second data segment, and a third data segment includes: Determining object supervision data at different time nodes according to the supervision integrated data; Create a data coordinate system, where the X-axis of the data coordinate system represents different time nodes, and the Y-axis of the data coordinate system represents data unit parameters of different object dimensions; Importing the object supervision data into the data coordinate system respectively to obtain a data change curve corresponding to the object supervision data; Determine data change characteristics at different time nodes according to the data change curve, and reorganize the data change characteristics according to different supervision objects to obtain data feature sets corresponding to different supervision objects; Arrange each of the data feature sets in a matrix according to time sequence to obtain a data feature matrix corresponding to each supervision object; Deducing the data change characteristics of the data feature matrix according to the time period to obtain the prediction feature matrix corresponding to different supervision objects in the future preset time period; Determine a future data feature set according to the prediction feature matrix, and import the future data feature set into the data coordinate system to obtain a future data curve; Performing curve segmentation processing on the data change curve and the future data curve according to the time nodes to obtain a first data curve corresponding to the historical time period, a second data curve corresponding to the current time period, and a third data curve corresponding to the future time period; Data features are extracted from the first data curve, the second data curve, and the third data curve, and the extracted data features are sorted in time series to obtain a first data segment, a second data segment, and a third data segment.

6. According to claim 1, a dynamic processing method of intelligent supervision data is characterized in that: The determining whether the first data segment exists in the preset monitoring data center further includes: If the first data segment does not exist in the preset monitoring data center or part of the first data segment exists, the first data segment, the second data segment and the third data segment are imported into the preset monitoring data center for abnormal diagnosis to obtain historical diagnosed data segments, current diagnosed data segments and future diagnosed data segments.

7. A dynamic processing system for intelligent supervision data, characterized in that: include: A data acquisition module is used to acquire real-time monitoring data, wherein the real-time monitoring data is monitoring data of different monitoring objects in a preset monitoring area at different times; A data division module, used for dividing the real-time monitoring data into data structure categories to obtain a first data set, a second data set and a third data set, wherein the first data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times as complete structured data, the second data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times as incomplete structured data, and the third data set is a collection of monitoring data of the monitoring objects in the preset monitoring area at different times as unstructured data; a data processing module, configured to process the first data set, the second data set, and the third data set to obtain a first data segment, a second data segment, and a third data segment; an abnormality diagnosis module, used to establish a data connection with a preset monitoring data center, and determine whether the preset monitoring data center has the first data segment, and if so, import the second data segment and the third data segment into the preset monitoring data center for abnormality diagnosis, and obtain historical diagnosed data segments, current diagnosed data segments, and future diagnosed data segments; A data splicing module, used to splice the historical diagnosed data segments, the current diagnosed data segments and the future diagnosed data segments according to time nodes and data types, to obtain diagnostic data segments of different data types in different time periods; The real-time display module is used to send the diagnostic data segment to a target terminal for display. The target terminal is a terminal device used by monitoring personnel.

8. The dynamic processing system of intelligent supervision data according to claim 7 is characterized in that: When the data processing module aggregates the first data set, the second data set and the fourth data set to obtain an aggregated data set, the data processing module is specifically used to: Generate corresponding data set tasks according to the first data set, the second data set and the fourth data set; Constructing a summary data model and loading summary processing rules, wherein the summary processing rules include data cleaning rules, data review rules and data summary processing logic; According to the data integration task, data is collected from the first data set, the second data set and the fourth data set and input into the summary data model to obtain a summary data set.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes a dynamic processing method for intelligent supervision data as claimed in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute a method for dynamically processing intelligent supervision data as claimed in any one of claims 1 to 6.

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