Remote monitoring and guiding method and system

Abstract

The invention provides a remote monitoring and guiding method and system. The method comprises the following steps: deploying an intelligent analysis engine at a plurality of monitoring nodes, and generating a multimedia evidence information packet; carrying out space-time consistency correction processing and fusion by using a multi-source data fusion algorithm, adopting a distributed situation awareness technology, adding time and geographic space axes, visually displaying regional dynamic conditions, and generating a comprehensive situation awareness map; an augmented reality interaction algorithm is applied, an optimal visual and audio data transmission path is determined by analyzing geographic space information and real-time dynamic data, an immersive bidirectional interaction technology is adopted, augmented reality equipment is introduced, a field real-time video stream is accessed, and an event processing log is generated; and performing text analysis on communication contents recorded in the event processing log, extracting key decision points and operation steps, performing effect analysis by using an effect evaluation method, and generating a remote monitoring and guidance scheme. According to the technical scheme provided by the invention, the efficiency and accuracy of remote monitoring and guidance are remarkably improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of remote monitoring technology, and in particular, to a remote monitoring and guidance method and system. Background Art

[0002] With the widespread application of intelligent monitoring systems in public safety, industrial production, traffic management and emergency response, the demand for remote monitoring and guidance is growing, especially in the fields of public safety (such as urban monitoring), industrial production (such as factory automation) and traffic management (such as road monitoring), where real-time monitoring and rapid response are the key to ensuring safety and efficiency. In addition, for emergency response to emergencies, such as fires and traffic accidents, it is necessary to obtain the on-site situation in a timely manner and provide precise operation guidance to on-site personnel to minimize losses and risks.

[0003] Existing remote monitoring and guidance systems mainly rely on a single or limited number of monitoring nodes, using simple video and audio transmission technologies. These systems usually perform data analysis and processing through centralized servers, lacking deep fusion of multi-source data and temporal and spatial consistency correction. Although some systems have introduced preliminary data fusion technologies and visualization tools, they have failed to fully utilize distributed situational awareness technologies and cannot fully display the dynamic situation of multi-node areas. In addition, existing augmented reality interactive applications are mostly limited to static scenes and lack support for real-time dynamic data, which limits the effectiveness of remote guidance.

[0004] The existing solutions have the following major defects. Due to the lack of effective multi-source data fusion and spatiotemporal consistency correction, it is difficult for the existing system to eliminate redundant information, resulting in slow data processing and inaccurate analysis results, which directly affects the efficiency and accuracy of remote monitoring and guidance, resulting in low efficiency of remote monitoring and guidance; most of the existing systems use static or semi-dynamic visualization methods, which cannot intuitively display the dynamic situation of multiple geographically dispersed monitoring node areas, limiting the user's understanding of the global situation and decision-making ability; the augmented reality interaction function of the existing system is relatively limited, and it cannot provide delay-free on-site views and instant operation instructions, which affects the interactivity and real-time nature of remote guidance, making it difficult for on-site personnel to obtain timely and effective support. Summary of the invention

[0005] The embodiments of the present application provide a remote monitoring and guidance method and system to solve the problem of low efficiency of remote monitoring and guidance in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a remote monitoring and guidance method, comprising:

[0007] Deploy intelligent analysis engines at multiple geographically dispersed monitoring nodes to issue real-time alarm information when abnormal behavior is detected and generate multimedia evidence information packages; the multimedia evidence information packages contain video and audio data, as well as environmental parameters;

[0008] Using a multi-source data fusion algorithm, the multimedia evidence information packages from different monitoring nodes are corrected for time and space consistency and fused to eliminate redundant information in the multimedia evidence information packages. Distributed situation awareness technology is used to add time and geographic space axes to the multimedia evidence information packages from different monitoring nodes, intuitively displaying the regional dynamics of multiple geographically dispersed monitoring nodes, and generating a comprehensive situation awareness map; the comprehensive situation awareness map is a visual interface that combines time and geographic space dimensions and is used to present and analyze multi-source data;

[0009] Using augmented reality interaction algorithms, by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map, determining the optimal visual and audio data transmission path to present a delay-free on-site view, using immersive two-way interactive technology, by introducing augmented reality devices, accessing the on-site real-time video streams of different monitoring nodes, providing instant operation guidance for on-site personnel, and generating event processing logs;

[0010] Perform text analysis on the communication content recorded in the event processing log, extract key decision points and operating steps from the communication content, use effectiveness evaluation methods to analyze the effectiveness of the key decision points and operating steps, and generate remote monitoring and guidance plans.

[0011] Optionally, the multi-source data fusion algorithm is used to perform spatiotemporal consistency correction processing and fusion on multimedia evidence information packages from different monitoring nodes, thereby eliminating redundant information in the multimedia evidence information packages. Distributed situational awareness technology is used to add time and geographic space axes to multimedia evidence information packages from different monitoring nodes, so as to intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes and generate a comprehensive situational awareness map, including:

[0012] Using a multi-source data fusion algorithm, the multimedia evidence information packages from different monitoring nodes are corrected for spatiotemporal consistency, and the multimedia evidence information packages after spatiotemporal consistency correction are deeply fused, and redundant information in the multimedia evidence information packages is eliminated to generate a fused multi-source data set;

[0013] Based on the fused multi-source data set, the distributed situation awareness technology is used to classify and annotate the multimedia evidence information packages from different monitoring nodes and their corresponding time and geographical locations, and the time and geographical space axes are introduced to map the classification and annotation results to generate a multidimensional data structure;

[0014] Based on the multi-dimensional data structure, visualization technology is used to construct a global perspective to intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes and generate a comprehensive situation awareness map.

[0015] Optionally, the multi-source data fusion algorithm is used to perform spatiotemporal consistency correction processing on multimedia evidence information packets from different monitoring nodes, and the multimedia evidence information packets after spatiotemporal consistency correction are deeply fused, and redundant information in the multimedia evidence information packets is eliminated to generate a fused multi-source data set, including:

[0016] Using multi-source data fusion algorithm, the multimedia evidence information from different monitoring nodes is corrected for spatiotemporal consistency. By comparing the spatiotemporal feature deviations of multimedia evidence information from different monitoring nodes in the same time period, a spatiotemporal consistency correction data set is generated.

[0017] Based on the spatiotemporal consistency correction data set, deep fusion processing is performed on the information from different monitoring nodes that effectively reflects the actual situation, and redundant information in the multimedia evidence information package is eliminated to generate a preliminary fused multi-source data set;

[0018] Based on the preliminary fused multi-source data set, the deep fusion processing result is monitored in real time, and the preliminary fused multi-source data set is cleaned using statistical methods to generate a fused multi-source data set.

[0019] Optionally, based on the fused multi-source data set, the distributed situation awareness technology is used to classify and annotate the multimedia evidence information packages from different monitoring nodes and their corresponding time and geographic locations, and the time and geographic space axes are introduced to map the classification and annotation results to generate a multidimensional data structure, including:

[0020] Based on the fused multi-source data set, identifying time and location metadata in multimedia evidence information packages from different monitoring nodes, classifying the time and location metadata into specific time periods and geographic locations, and generating a classified and labeled data set;

[0021] Based on the classified and annotated data set, using distributed situational awareness technology, introducing time and geographic space axes, mapping the time and geographic location information corresponding to each data point in the classified and annotated data set, and generating a time-geolocation mapping data structure;

[0022] Based on the temporal geographic mapping data structure, using statistical methods, statistically analyzing the data distribution of each data point after the mapping process, correcting potential distribution anomalies and deviations of the data distribution, and generating optimized data distribution;

[0023] Based on the optimized data distribution, dynamic simulation technology is used to simulate the behavior patterns of the optimized data distribution at different time points and geographical locations, identify potential risk points of the behavior patterns, adjust the optimized data distribution, and generate a multidimensional data structure.

[0024] Optionally, the augmented reality interaction algorithm is used to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a delay-free on-site view, and immersive two-way interactive technology is used to introduce augmented reality equipment to access the on-site real-time video streams of different monitoring nodes to provide instant operation guidance for on-site personnel and generate event processing logs, including:

[0025] Parsing the geospatial information and real-time dynamic data in the comprehensive situation awareness map, identifying key location points in the geospatial information and associated dynamic changes in the real-time dynamic data, and generating geospatial and dynamic data analysis results;

[0026] Based on the geospatial and dynamic data analysis results, an augmented reality interaction algorithm is used to determine the optimal visual and audio data transmission path by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map to present a delay-free scene view and generate an optimal transmission path configuration;

[0027] Based on the optimal transmission path configuration, immersive two-way interactive technology is adopted, and the on-site real-time video streams of the different monitoring nodes are accessed by introducing augmented reality devices, providing instant operation guidance for on-site personnel and generating immersive interactive session records;

[0028] Based on the immersive interactive session record, behavioral analysis technology is used to perform behavioral analysis on all interactive contents in the immersive interactive session record, extract key operation requirements and decision points in all interactive contents, and generate an event processing log.

[0029] Optionally, based on the analysis results of the geospatial and dynamic data, an augmented reality interaction algorithm is used to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a delay-free on-site view and generate an optimal transmission path configuration, including:

[0030] Based on the geographic space and dynamic data analysis results, feature extraction and annotation processing are performed on each key location point and its associated dynamic changes in the geographic space and dynamic data analysis results to generate a feature extraction data set;

[0031] Based on the feature extraction data set, an augmented reality interaction algorithm is used to determine the optimal visual and audio data transmission path by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map to present a delay-free scene view and generate a set of candidate path solutions;

[0032] Based on the candidate path solution set, a performance analysis is performed on each candidate path in the candidate path solution set, the transmission efficiency and quality of each candidate path are evaluated, and an alternative path is set for each candidate path to cope with emergencies, and a preliminary transmission path configuration is generated;

[0033] Based on the preliminary transmission path configuration, a real-time transmission test based on the preliminary transmission path configuration is implemented, and the performance indicators during the real-time transmission test are monitored in real time. During the real-time monitoring process, the transmission parameters in the preliminary transmission path configuration are automatically adjusted to generate an optimal transmission path configuration.

[0034] Optionally, the text analysis of the communication content recorded in the event processing log is performed to extract key decision points and operation steps of the communication content, and the effectiveness of the key decision points and operation steps is analyzed by using an effectiveness evaluation method to generate a remote monitoring and guidance plan, including:

[0035] Performing semantic analysis on the communication content recorded in the event processing log, capturing important communication details in the semantic analysis result, and generating a semantic analysis result;

[0036] Through semantic analysis technology, the core decision and actual execution action of each communication in the semantic analysis results are identified, and a set of key decision points and operation steps are generated;

[0037] Use the effectiveness evaluation method to evaluate the actual effect of each operation in the operation step set, and compare it with the expected goal of each operation step, and generate an operation step effect evaluation report;

[0038] Based on the effect evaluation report of the operation steps, the decision tree analysis method is applied to provide path selection for each key decision point and generate a remote monitoring and guidance plan.

[0039] In a second aspect, an embodiment of the present application provides a remote monitoring and guidance system, including:

[0040] A detection module is used to deploy an intelligent analysis engine at multiple geographically dispersed monitoring nodes, issue an alarm message in real time when abnormal behavior is detected, and generate a multimedia evidence information package; the multimedia evidence information package includes video and audio data, as well as environmental parameters;

[0041] A fusion module is used to apply a multi-source data fusion algorithm to perform spatiotemporal consistency correction processing and fusion on multimedia evidence information packages from different monitoring nodes, eliminate redundant information in the multimedia evidence information packages, and use distributed situation awareness technology to add time and geographic space axes to multimedia evidence information packages from different monitoring nodes, intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes, and generate a comprehensive situation awareness map; the comprehensive situation awareness map is a visual interface that combines time and geographic space dimensions and is used to present and analyze multi-source data;

[0042] An interactive module, for using an augmented reality interactive algorithm to determine the optimal visual and audio data transmission path by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map, so as to present a delay-free on-site view, adopt an immersive two-way interactive technology, introduce augmented reality equipment, access the on-site real-time video streams of the different monitoring nodes, provide instant operation guidance for on-site personnel, and generate an event processing log;

[0043] The analysis module is used to perform text analysis on the communication content recorded in the event processing log, extract key decision points and operation steps of the communication content, use the effectiveness evaluation method to perform effect analysis on the key decision points and operation steps, and generate a remote monitoring and guidance plan.

[0044] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a remote monitoring and guidance method as described in the first aspect.

[0045] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a remote monitoring and guidance method as described in the first aspect.

[0046] In the embodiment of the present application, an intelligent analysis engine is deployed at multiple geographically dispersed monitoring nodes, and an alarm message is issued in real time when abnormal behavior is detected, and a multimedia evidence information package is generated; the multimedia evidence information package includes video and audio data, as well as environmental parameters; a multi-source data fusion algorithm is used to perform spatiotemporal consistency correction processing and fusion on the multimedia evidence information packages from different monitoring nodes, thereby eliminating redundant information in the multimedia evidence information packages; a distributed situation awareness technology is used to add time and geographic space axes to the multimedia evidence information packages from different monitoring nodes, intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes, and generate a comprehensive situation awareness map; the comprehensive situation awareness map is a combination of time and geographic space. The method comprises a visual interface with an inter-dimensional dimension for presenting and analyzing multi-source data; an augmented reality interaction algorithm is used to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a non-delayed on-site view; an immersive two-way interactive technology is used to introduce augmented reality devices to access the on-site real-time video streams of different monitoring nodes to provide instant operation guidance for on-site personnel and generate an event processing log; a text analysis is performed on the communication content recorded in the event processing log to extract the key decision points and operation steps of the communication content; an effectiveness evaluation method is used to perform an effect analysis on the key decision points and operation steps to generate a remote monitoring and guidance plan. By deploying intelligent analysis engines at monitoring nodes dispersed in multiple geographical locations, abnormal behaviors are detected in real time and multimedia evidence information packages are generated. Combining multi-source data fusion algorithms and distributed situation awareness technology, this method can eliminate redundant information, intuitively display the dynamic conditions of multiple monitoring node areas, and generate a comprehensive situation awareness map. The augmented reality interaction algorithm and immersive two-way interactive technology are further used to ensure the presentation of a non-delayed on-site view and provide instant operation guidance for on-site personnel. Finally, through text analysis and effectiveness evaluation of event processing logs, a detailed remote monitoring and guidance plan is generated.

[0047] Furthermore, the multimedia evidence information packages from different monitoring nodes are corrected for time and space consistency and deeply fused through a multi-source data fusion algorithm, eliminating redundant information and generating a fused multi-source data set. Based on the fused multi-source data set, the distributed situational awareness technology is used to introduce the time and geographic space axes into the classification and annotation results to generate a multi-dimensional data structure, and a global perspective is constructed through visualization technology to generate a comprehensive situational awareness map. This not only enhances the consistency and accuracy of the data, but also provides an intuitive multi-dimensional display, which facilitates users to quickly understand and analyze complex scenarios, greatly improving decision-making efficiency and response speed.

[0048] Furthermore, by analyzing the geospatial information and real-time dynamic data in the comprehensive situational awareness map, key locations and associated dynamic changes are identified, and geospatial and dynamic data analysis results are generated. Based on this result, the augmented reality interaction algorithm is used to determine the optimal visual and audio data transmission path to present a delay-free on-site view and generate the optimal transmission path configuration. Through immersive two-way interactive technology and augmented reality devices to access the on-site real-time video stream, instant operation guidance is provided to on-site personnel, and immersive interactive session records are generated. Finally, behavioral analysis technology is used to extract key operational requirements and decision points in all interactive content, and a detailed event processing log is generated. This method not only ensures the efficiency and real-time nature of data transmission, but also greatly enhances the interactivity and accuracy of remote guidance, making remote operation more intuitive and reliable.

[0049] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 A flowchart of a remote monitoring and guidance method provided in an embodiment of the present application;

[0052] Figure 2 A schematic diagram of the structure of a remote monitoring and guidance system provided in an embodiment of the present application;

[0053] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the solution of the present application, 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.

[0055] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0056] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only 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 those skilled in the art without creative work are within the scope of protection of this application.

[0057] Figure 1 A flowchart of a remote monitoring and guidance method is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0058] 101. Deploy an intelligent analysis engine at multiple geographically dispersed monitoring nodes to issue an alarm message in real time when abnormal behavior is detected and generate a multimedia evidence information package; the multimedia evidence information package includes video and audio data, as well as environmental parameters;

[0059] In this step, the intelligent analysis engine is a software system deployed on multiple geographically dispersed monitoring nodes that can process and analyze video, audio data and environmental parameters in real time. The engine uses machine learning and computer vision technology to detect abnormal behavior and automatically generate multimedia evidence information packages.

[0060] Multimedia evidence packages contain comprehensive data sets of video, audio data, and environmental parameters (such as temperature, humidity, light, etc.) to record and prove the occurrence of specific events. These packages not only provide visual and auditory evidence, but also combine environmental conditions to provide comprehensive data support for subsequent analysis.

[0061] Abnormal behavior refers to behavior that deviates from normal patterns or expectations, which may include illegal intrusions, equipment failures, or other unexpected events. The intelligent analysis engine identifies these behaviors through preset rules or machine learning models.

[0062] In the embodiment of the present application, first, an intelligent analysis engine is deployed on multiple geographically dispersed monitoring nodes; second, the intelligent analysis engine monitors and analyzes the data streams from each monitoring node in real time; third, when abnormal behavior is detected, the system will immediately issue an alarm message to notify relevant personnel; finally, the system generates a multimedia evidence information package containing video, audio and environmental parameters to ensure that each event is recorded in detail.

[0063] Suppose that in a large industrial park, intelligent analysis engines are deployed in various key areas (such as warehouses, production workshops, entrances and exits). First, when an illegal intrusion occurs in a certain area, the intelligent analysis engine detects this abnormal behavior in real time and immediately sends an alarm message to the security center; secondly, the system automatically generates a multimedia evidence information package containing on-site video, audio, and environmental parameters (such as temperature and humidity) for subsequent investigation and analysis; thirdly, these evidence information packages are stored in the cloud server to ensure the security and traceability of the data; finally, the system automatically sends relevant information to relevant managers so that they can take quick action. At the same time, this information also provides an important basis for future risk assessment and preventive measures.

[0064] 102. Use a multi-source data fusion algorithm to perform spatiotemporal consistency correction processing and fusion on multimedia evidence information packages from different monitoring nodes, eliminate redundant information in the multimedia evidence information packages, and use distributed situation awareness technology to add time and geographic space axes to multimedia evidence information packages from different monitoring nodes, intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes, and generate a comprehensive situation awareness map; the comprehensive situation awareness map is a visual interface that combines time and geographic space dimensions and is used to present and analyze multi-source data;

[0065] In this step, the multi-source data fusion algorithm is a technology that integrates data from different sources to eliminate redundant information and improve data quality and consistency. The algorithm ensures the consistency of data from different monitoring nodes in the time axis and geographic space dimension through spatiotemporal consistency correction processing.

[0066] The spatiotemporal consistency correction process aligns and adjusts the time and space coordinates to eliminate the timestamp differences and geographic coordinate deviations between different monitoring nodes, ensuring that all data are in the same reference frame.

[0067] Distributed situational awareness technology is a technology based on distributed computing architecture that can process and analyze large-scale, multi-source heterogeneous data to generate a comprehensive situational awareness map from a global perspective. This technology allows users to intuitively understand dynamic changes in complex scenarios by introducing time and geographic space axes.

[0068] The comprehensive situation awareness map is a visual interface that combines time and geographic space dimensions to present and analyze multi-source data. It not only displays the real-time status of each monitoring node, but also provides historical data analysis functions to help users make more informed decisions.

[0069] In the embodiments of the present application, firstly, a multi-source data fusion algorithm is used to process multimedia evidence information packets from different monitoring nodes; secondly, redundant information is eliminated through spatiotemporal consistency correction; thirdly, distributed situational awareness technology is used to add time and geographic space axes to each information packet; finally, a comprehensive situational awareness map is constructed to intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes.

[0070] For example, continuing with the above example, assume that multiple monitoring nodes in the park have collected different multimedia evidence information packages. First, through the multi-source data fusion algorithm, the system performs spatiotemporal consistency correction processing on these information packages to eliminate redundant information; secondly, using distributed situational awareness technology, the time and geographic space axes of all information packets are classified and labeled to generate a multidimensional data structure; thirdly, the system accurately maps the location and time point of each monitoring node based on timestamps and geographic coordinates to ensure that the data is in the same reference frame; finally, through visualization technology, a global perspective is constructed to generate a comprehensive situational awareness map, which intuitively displays the dynamic situation of the entire park and helps managers quickly understand any abnormal activities occurring in the park and their scope of influence.

[0071] 103. Using augmented reality interaction algorithms, by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map, determine the optimal visual and audio data transmission path to present a delay-free on-site view, adopt immersive two-way interactive technology, introduce augmented reality devices, access the on-site real-time video streams of different monitoring nodes, provide instant operation guidance for on-site personnel, and generate event processing logs;

[0072] In this step, the augmented reality interaction algorithm is an interactive technology that combines virtual information with the real world. By analyzing geospatial information and real-time dynamic data, it determines the optimal visual and audio data transmission path to present a delay-free on-site view. This algorithm improves the accuracy and real-time performance of remote guidance.

[0073] Immersive two-way interactive technology enables instant interaction between remote operators and on-site personnel by introducing augmented reality devices (such as AR glasses). This technology not only provides real-time video streaming, but also allows both parties to communicate by voice and gesture control, enhancing the realism and efficiency of the interaction.

[0074] The optimal visual and audio data transmission path is calculated through the augmented reality interaction algorithm to ensure the efficiency and stability of data transmission and reduce delays and interference.

[0075] The event processing log is a detailed log that records all interaction content, including communication, operation instructions, etc., providing a basis for subsequent evaluation and improvement.

[0076] In the embodiments of the present application, first, the geospatial information and real-time dynamic data in the comprehensive situation awareness map are analyzed to identify key location points and associated dynamic changes; second, based on the results of the geospatial and dynamic data analysis, an augmented reality interaction algorithm is used to determine the optimal visual and audio data transmission path; third, the on-site real-time video streams of different monitoring nodes are accessed through immersive two-way interactive technology to provide instant operation guidance for on-site personnel; finally, an immersive interactive session record is generated to form an event processing log.

[0077] For example, continuing with the above example, suppose a fire breaks out in the park and people need to be evacuated urgently. First, by parsing the geospatial information and real-time dynamic data in the comprehensive situational awareness map, the system identifies key locations (such as the location of the fire source, the safe exit) and related dynamic changes (such as the direction of smoke diffusion); secondly, based on this, the system uses augmented reality interaction algorithms to determine the optimal visual and audio data transmission path to ensure delay-free on-site view transmission; thirdly, by introducing augmented reality equipment, remote experts and on-site personnel conduct immersive two-way interaction and provide instant operation guidance, such as instructing on-site personnel how to quickly find the nearest safe exit; finally, the system generates immersive interactive session records, forming detailed event processing logs, recording all interactive content, including voice commands, video streams, and gesture controls, to provide detailed information for subsequent analysis and optimization.

[0078] 104. Perform text analysis on the communication content recorded in the event processing log, extract key decision points and operation steps of the communication content, use effectiveness evaluation methods to perform effect analysis on the key decision points and operation steps, and generate a remote monitoring and guidance plan.

[0079] In this step, text analysis involves natural language processing of the communication content recorded in the event processing log to extract key decision points and operation steps. This technology can identify important information and simplify complex communication content for subsequent analysis.

[0080] Key decision points are information points that play a decisive role in the communication process, usually involving major decisions or turning points. Extracting these points helps to understand the key links in the development of events.

[0081] Operation steps are specific instructions or action guides that instruct on-site personnel on how to perform tasks. These steps are the basis for ensuring the successful completion of tasks.

[0082] The effectiveness evaluation method evaluates the effects of key decision points and operation steps by combining quantitative and qualitative analysis. This method can measure the effectiveness of decisions and operations and provide a scientific basis for optimizing remote monitoring and guidance programs.

[0083] The remote monitoring and guidance plan is an optimization plan generated based on the results of the effectiveness evaluation, aiming to improve the efficiency and accuracy of handling similar incidents in the future. The plan not only summarizes past successful experiences, but also puts forward suggestions for improvement.

[0084] In the embodiment of the present application, first, text analysis is performed on the communication content recorded in the event processing log to extract key decision points and operating steps; second, the effectiveness evaluation method is used to analyze the effectiveness of these key decision points and operating steps; third, successful experiences and shortcomings are summarized; finally, a remote monitoring and guidance plan is generated to provide a reference for future event processing.

[0085] For example, continuing with the above example, assuming that after the fire incident is handled, the system first performs text analysis on the communication content recorded in the incident handling log, extracts key decision points (such as initiating emergency evacuation procedures) and operating steps (such as guiding people to safe exits); secondly, the effectiveness evaluation method is used to analyze the effects of these key decision points and operating steps, and it is found that some evacuation routes are congested, which affects the evacuation efficiency; thirdly, it summarizes successful experiences and shortcomings, and puts forward improvement suggestions, such as adding more evacuation channel signs and improving lighting facilities to ensure that similar incidents are handled more efficiently in the future; finally, a remote monitoring and guidance plan is generated to record the handling process and improvement measures of this incident in detail, provide valuable experience and guidance for future emergency responses, and ensure that the safety management of the park is continuously optimized and improved.

[0086] In summary, steps 101 to 104 cover the automation and intelligence of the entire process from abnormal behavior detection to remote guidance, aiming to provide an efficient remote monitoring and guidance system that meets the requirements of modern application scenarios for real-time, accuracy, and interactivity.

[0087] In order to solve the efficiency problem of multi-source data fusion and spatiotemporal consistency correction, in some embodiments, the use of a multi-source data fusion algorithm to perform spatiotemporal consistency correction processing and generate a comprehensive situation awareness map in step 102 includes: using a multi-source data fusion algorithm to perform spatiotemporal consistency correction processing on multimedia evidence information packages from different monitoring nodes, and deeply fusing the multimedia evidence information packages after the spatiotemporal consistency correction processing, and eliminating redundant information in the multimedia evidence information packages to generate a fused multi-source data set; based on the fused multi-source data set, using distributed situation awareness technology to classify and annotate the multimedia evidence information packages from different monitoring nodes and their corresponding time and geographic locations, introducing time and geographic space axes to map the classification and annotation results to generate a multi-dimensional data structure; based on the multi-dimensional data structure, using visualization technology to construct a global perspective to intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes to generate a comprehensive situation awareness map.

[0088] In this embodiment, deep fusion refers to a deeper fusion of multimedia evidence information packages that have been corrected for spatiotemporal consistency, which not only integrates data content but also optimizes data structure, further improving data integrity and consistency.

[0089] The fused multi-source dataset is a comprehensive dataset generated through deep fusion, which contains all valid information from multiple monitoring nodes, eliminates redundant parts, and ensures high consistency and accuracy of the data.

[0090] Classification and annotation is to classify and label each multimedia evidence information package according to time and geographic location, ensuring that each piece of data has clear time and space attributes.

[0091] The multidimensional data structure maps the classification and labeling results by introducing time and geographic space axes to construct a multidimensional data structure for subsequent visualization and analysis.

[0092] In the embodiments of the present application, firstly, a multi-source data fusion algorithm is used to perform spatiotemporal consistency correction processing on multimedia evidence information packets from different monitoring nodes; secondly, the multimedia evidence information packets after the spatiotemporal consistency correction processing are deeply fused, and redundant information is eliminated to generate a fused multi-source data set; thirdly, based on the fused multi-source data set, a distributed situational awareness technology is used to classify and annotate the multimedia evidence information packets from different monitoring nodes and their corresponding time and geographical locations, and the time and geographic space axes are introduced to map the classification and annotation results to generate a multi-dimensional data structure; finally, based on the multi-dimensional data structure, a visualization technology is used to construct a global perspective to intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes and generate a comprehensive situational awareness map.

[0093] Here is a specific example:

[0094] Assume that in a large-scale urban traffic management system, first, the system uses a multi-source data fusion algorithm to perform spatiotemporal consistency correction on the multimedia evidence information packets from various traffic cameras to ensure that the timestamps and geographic coordinates of all video streams are consistent; secondly, the system deeply fuses these corrected information packets to eliminate duplicate or irrelevant information and generate a highly consistent and streamlined fused multi-source data set; thirdly, based on this fused multi-source data set, the system uses distributed situational awareness technology to classify and label each information packet, and introduces time and geographic space axes to construct a multi-dimensional data structure to ensure that each data point has clear time and location attributes; finally, the system uses advanced visualization technology to construct a comprehensive situational awareness map from a global perspective, intuitively displaying the traffic flow and dynamics of the entire city, helping traffic management departments to quickly identify congestion points and abnormal events, so as to take timely and effective measures to guide and respond.

[0095] In order to solve the problem of temporal and spatial consistency of multimedia evidence information between different monitoring nodes, in some embodiments, the use of a multi-source data fusion algorithm to perform temporal and spatial consistency correction processing and generate a fused multi-source data set in step 102 includes: using a multi-source data fusion algorithm to perform temporal and spatial consistency correction processing on multimedia evidence information from different monitoring nodes, and generating a temporal and spatial consistency correction data set by comparing the temporal and spatial feature deviations of multimedia evidence information from different monitoring nodes in the same time period; based on the temporal and spatial consistency correction data set, performing deep fusion processing on information from different monitoring nodes that effectively reflects the actual situation, and eliminating redundant information in the multimedia evidence information package to generate a preliminary fused multi-source data set; based on the preliminary fused multi-source data set, real-time monitoring of the deep fusion processing results, and using statistical methods to clean the preliminary fused multi-source data set to generate a fused multi-source data set.

[0096] In this embodiment, the spatiotemporal consistency correction data set refers to a data set that is processed by a multi-source data fusion algorithm to ensure that all multimedia evidence information from different monitoring nodes is highly consistent in time and space.

[0097] The temporal and spatial feature deviation is the difference between the time or location information recorded by different monitoring nodes for the same event. This deviation may be caused by inaccurate time settings or inaccurate geographic location positioning of the monitoring equipment.

[0098] Deep fusion processing refers to the deeper integration of multimedia evidence information after temporal and spatial consistency correction. This step aims to extract effective information that best reflects the actual situation from a large amount of information and remove duplicate or unnecessary parts to reduce the amount of data for subsequent processing and improve data quality.

[0099] The preliminary fused multi-source dataset is an intermediate data set obtained after completing the deep fusion processing. It contains valid multimedia evidence information from different monitoring nodes, but there may still be some unnecessary data or outliers.

[0100] Real-time monitoring is the continuous monitoring of the initial fusion results to ensure the accuracy and practicality of the data.

[0101] In the embodiment of the present application, firstly, a multi-source data fusion algorithm is used to perform spatiotemporal consistency correction processing on multimedia evidence information from different monitoring nodes to ensure that the time and geographic coordinates of all data are accurate; secondly, based on the spatiotemporal consistency corrected data set, deep fusion processing is performed to screen out truly effective and actual information, while eliminating redundant data to create a preliminary fused multi-source data set; thirdly, the preliminary fused multi-source data set is monitored in real time, and statistical methods are used to clean up noise and outliers in the data to ensure the purity of the data; finally, the final fused multi-source data set is generated to provide users with reliable data support.

[0102] Here is a specific example:

[0103] Suppose in a smart city security monitoring project, first, the system uses a multi-source data fusion algorithm to perform spatiotemporal consistency correction on the video data captured by surveillance cameras throughout the city to ensure the precise matching of all video streams in time and space; secondly, the system performs deep fusion processing on the corrected video data, selects those footage clips that best represent the on-site situation, removes duplicate or irrelevant content, and constructs a preliminary fused multi-source data set; thirdly, the system continuously monitors the results of the preliminary fusion, applies statistical analysis techniques to eliminate any abnormal data that may affect the judgment, and keeps the data set clean; finally, the system generates a fused multi-source data set, which provides detailed and accurate visual intelligence for urban management, helps to respond to emergencies in a timely manner and optimize the allocation of public resources.

[0104] In order to solve the accuracy problem of classification labeling and data mapping, in some embodiments, the step 102 generates a multidimensional data structure based on the fusion of multi-source data sets using distributed situational awareness technology, including: based on the fusion of multi-source data sets, identifying the time and location metadata in the multimedia evidence information packages from different monitoring nodes, classifying the time and location metadata into specific time periods and geographical locations, and generating a classified labeling data set; based on the classified labeling data set, using distributed situational awareness technology, introducing time and geographical space axes, mapping the time and geographical location information corresponding to each data point in the classified labeling data set, and generating a time-geo-mapping data structure; based on the time-geo-mapping data structure, using statistical methods, statistically analyzing the data distribution of each data point after the mapping process, correcting potential distribution anomalies and deviations of the data distribution, and generating an optimized data distribution; based on the optimized data distribution, using dynamic simulation technology, simulating the behavior pattern of the optimized data distribution at different time points and geographical locations, identifying potential risk points of the behavior pattern and adjusting the optimized data distribution, and generating a multidimensional data structure.

[0105] In this embodiment, the time and location metadata refers to the timestamp and geographic location information recorded in the multimedia evidence information package. The time metadata is used to identify the specific time when the event occurred; the location metadata provides the geographic coordinates of the event, and the two together constitute the time and space attributes of each multimedia evidence information package.

[0106] The classified and labeled dataset generates an ordered dataset by classifying and labeling the time and location metadata of each data point in the fused multi-source dataset. Each data point in this dataset has a clear time period and geographic location label, which is convenient for subsequent analysis and processing.

[0107] The temporal geo-mapping data structure maps the time and geographic location information corresponding to each data point in the classified and labeled data set by introducing the time and geographic space axis to form a multi-dimensional data structure. This structure not only retains the temporal and spatial characteristics of the original data, but also facilitates the intuitive display and analysis of dynamic changes in complex scenes.

[0108] Optimizing data distribution uses statistical methods to perform statistical analysis on the data distribution of each data point after mapping, identifying and correcting potential distribution anomalies and deviations to ensure the rationality and accuracy of data distribution. This step helps to improve the reliability and effectiveness of data analysis results.

[0109] Dynamic simulation technology is a technology that simulates behavioral patterns at different time points and geographical locations. Through simulation, potential risk points can be identified, and data distribution can be adjusted and optimized based on simulation results to further improve data quality and application value.

[0110] In the embodiments of the present application, firstly, based on the fusion of multi-source data sets, the time and location metadata in the multimedia evidence information packages from different monitoring nodes are identified, and these metadata are classified into specific time periods and geographical locations to generate a classified and labeled data set; secondly, distributed situational awareness technology is used to introduce time and geographic space axes, and the time and geographic location information corresponding to each data point in the classified and labeled data set is mapped to generate a time-geographic mapping data structure; thirdly, statistical methods are used to perform statistics on the data distribution of each data point after mapping, correct potential anomalies and deviations in the data distribution, and generate an optimized data distribution; finally, dynamic simulation technology is used to simulate and optimize the behavior patterns of data distribution at different time points and geographical locations, identify potential risk points and adjust the optimized data distribution to generate a multidimensional data structure.

[0111] Here is a specific example:

[0112] Assume that in a security monitoring system of a large airport, first, the system identifies the time and location metadata in the multimedia evidence information packages from different surveillance cameras and sensors based on the fusion of multi-source data sets, and classifies these metadata into specific time periods (such as 30 minutes before the flight takes off) and geographical locations (such as terminal buildings, runways, etc.), and generates a classified and labeled data set; secondly, the system uses distributed situational awareness technology, introduces time and geographic space axes, maps the time and geographical location information corresponding to each data point in the classified and labeled data set, and generates a time-geographic mapping data structure to intuitively display the dynamic changes of the entire airport area; thirdly, the system uses statistical methods to count the data distribution of each data point after mapping, corrects possible anomalies and deviations in the data distribution, generates optimized data distribution, and ensures the authenticity and reliability of the data; finally, the system uses dynamic simulation technology to simulate and optimize the behavior patterns of data distribution at different time points (such as peak hours and off-peak hours) and geographical locations, identifies potential risk points (such as crowded areas, safety hazards), and adjusts and optimizes data distribution according to simulation results to generate a multidimensional data structure, providing detailed visual intelligence for airport management departments, helping them make more informed safety management and emergency response decisions.

[0113] In order to solve the delay problem of visual and audio data transmission in remote monitoring and guidance, in some embodiments, the use of an augmented reality interaction algorithm in step 103 to determine the optimal transmission path and provide instant operation guidance includes: parsing the geospatial information and real-time dynamic data in the comprehensive situation awareness map, identifying the key location points in the geospatial information and the associated dynamic changes in the real-time dynamic data, and generating geospatial and dynamic data analysis results; based on the geospatial and dynamic data analysis results, using an augmented reality interaction algorithm to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a delay-free on-site view and generate an optimal transmission path configuration; based on the optimal transmission path configuration, using immersive two-way interactive technology, introducing augmented reality equipment, accessing the on-site real-time video streams of different monitoring nodes, providing instant operation guidance for on-site personnel, and generating an immersive interactive session record; based on the immersive interactive session record, using behavioral analysis technology to perform behavioral analysis on all interactive content in the immersive interactive session record, extracting key operational requirements and decision points in all interactive content, and generating an event processing log.

[0114] In this embodiment, geospatial information refers to the geographic location coordinates contained in the data packets from different monitoring nodes, which is crucial for understanding the specific location of the event and plays a key role in building a comprehensive situational awareness map.

[0115] Real-time dynamic data refers to continuously updated data streams that reflect current situations, such as video, audio, and other sensor data. This type of data can provide the latest information about on-site conditions and support real-time decision-making.

[0116] Key location points refer to specific places or areas that are important in geographic spatial information. These points may be the places where emergencies occur, the locations of important facilities, etc., and have a direct impact on event handling and resource scheduling.

[0117] Associated dynamic changes refer to real-time dynamic changes related to key location points, such as personnel flow, vehicle movement or other significant changes.

[0118] The results of geospatial and dynamic data analysis are data analysis results generated by analyzing geospatial information and real-time dynamic data, providing a comprehensive understanding of the time, location and change patterns of events in the monitored area.

[0119] Optimal transmission path configuration: The optimal transmission path configuration calculated by the augmented reality interaction algorithm ensures the efficiency and stability of data transmission and reduces delays and interference.

[0120] Immersive interactive session recording records detailed logs of all interactive content, including communication, operation instructions, etc., providing a basis for subsequent evaluation and improvement.

[0121] Behavior analysis technology performs natural language processing and action recognition on all interactive content in immersive interactive session records to extract key operational requirements and decision points. This helps summarize successful experiences and shortcomings and provide improvement suggestions for future event handling.

[0122] The event processing log records all interactive content in the entire event processing process, including decision points, operation steps, etc., providing valuable information for subsequent analysis and optimization.

[0123] In the embodiment of the present application, first, the geospatial information and real-time dynamic data in the comprehensive situation awareness map are parsed, the key location points in the geospatial information and the associated dynamic changes in the real-time dynamic data are identified, and the geospatial and dynamic data analysis results are generated; secondly, based on the geospatial and dynamic data analysis results, an augmented reality interaction algorithm is used to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a delay-free on-site view and generate an optimal transmission path configuration; thirdly, based on the optimal transmission path configuration, immersive two-way interactive technology is used to introduce augmented reality equipment and access the on-site real-time video streams of different monitoring nodes to provide on-site personnel with instant operation guidance and generate immersive interactive session records; finally, based on the immersive interactive session records, behavioral analysis technology is used to perform behavioral analysis on all interactive content, extract key operational requirements and decision points, and generate event processing logs.

[0124] Here is a specific example:

[0125] Suppose that in a large industrial plant, the problem of delay in visual and audio data transmission in remote monitoring and guidance needs to be solved. First, the system parses the geospatial information and real-time dynamic data in the comprehensive situation awareness map, identifies key locations (such as hazardous chemical storage areas) and their associated dynamic changes (such as the frequency of personnel entry and exit), and generates detailed geospatial and dynamic data analysis results; secondly, based on these analysis results, the system uses augmented reality interaction algorithms to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map, determine the optimal visual and audio data transmission path, ensure delay-free on-site view transmission, and generate the optimal transmission path. configuration; secondly, based on the optimal transmission path configuration, the system adopts immersive two-way interactive technology, and through the introduction of augmented reality devices (such as AR glasses), access to the on-site real-time video streams of different monitoring nodes, to provide on-site operators with immediate operation guidance, such as guiding them to safely handle hazardous chemical leaks, and generate immersive interactive session records; finally, based on the immersive interactive session records, the system adopts behavioral analysis technology to perform behavioral analysis on all interactive content, extract key operational requirements and decision points, such as the communication effect of emergency evacuation instructions, and generate detailed event processing logs, providing valuable reference and improvement suggestions for the handling of similar events in the future.

[0126] In order to solve the optimization problem of transmission path selection, in some embodiments, the step 103 uses an augmented reality interaction algorithm based on the results of geospatial and dynamic data analysis to generate an optimal transmission path configuration, including: based on the results of geospatial and dynamic data analysis, extracting features and annotating each key location point and its associated dynamic changes in the results of geospatial and dynamic data analysis to generate a feature extraction data set; based on the feature extraction data set, using an augmented reality interaction algorithm to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a delay-free on-site view and generate a candidate path solution set; based on the candidate path solution set, performing a performance analysis on each candidate path in the candidate path solution set, evaluating the transmission efficiency and quality of each candidate path, and setting an alternative path for each candidate path to cope with emergencies, and generating a preliminary transmission path configuration; based on the preliminary transmission path configuration, implementing a real-time transmission test based on the preliminary transmission path configuration, and monitoring the performance indicators during the real-time transmission test in real time, and automatically adjusting the transmission parameters in the preliminary transmission path configuration during the real-time monitoring process to generate an optimal transmission path configuration.

[0127] In this embodiment, the geospatial and dynamic data analysis results identify key location points and their associated dynamic changes by parsing the geospatial information and real-time dynamic data in the comprehensive situation awareness map.

[0128] Feature extraction and annotation processing refers to extracting significant features from each key location point and its associated dynamic changes in the results of geographic space and dynamic data analysis, and annotating them. This step aims to highlight factors that have an important impact on the selection of transmission paths, such as traffic flow, weather conditions, etc.

[0129] The feature extraction dataset is a data set generated by feature extraction and annotation processing, which contains a series of key features that have been screened and annotated. These features can help subsequent algorithms to more accurately evaluate the selection of different paths.

[0130] The candidate path solution set is a series of possible transmission path options generated by analyzing geographic spatial information and real-time dynamic data based on the feature extraction data set using augmented reality interaction algorithms.

[0131] The preliminary transmission path configuration selects one or more paths with the best performance as the preliminary configuration based on the results of the performance analysis.

[0132] The real-time transmission test is an actual data transmission experiment conducted under the preliminary transmission path configuration, monitoring various performance indicators in the entire process, such as delay, packet loss rate, etc.

[0133] The optimal transmission path configuration is to automatically adjust the transmission parameters (such as encoding format, compression ratio) in the preliminary transmission path configuration during the real-time transmission test, and finally generate an optimal transmission path configuration that can maintain high efficiency and stability under various conditions.

[0134] In an embodiment of the present application, first, based on the results of geospatial and dynamic data analysis, feature extraction and annotation processing are performed on each key location point and its associated dynamic changes to generate a feature extraction data set; secondly, based on the feature extraction data set, an augmented reality interaction algorithm is used to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map, determine the optimal visual and audio data transmission paths, and generate a set of candidate path solutions; thirdly, a performance analysis is performed on each candidate path in the candidate path solution set to evaluate the transmission efficiency and quality, and alternative paths are set to cope with emergencies, and a preliminary transmission path configuration is generated; finally, a real-time transmission test based on the preliminary transmission path configuration is implemented, and performance indicators are monitored in real time. During the test, transmission parameters are automatically adjusted to generate the optimal transmission path configuration.

[0135] Here is a specific example:

[0136] Assume that the optimization problem of transmission path selection needs to be solved in the security monitoring system of a smart city. First, based on the results of geospatial and dynamic data analysis, the system extracts and annotates features of each key location point (such as a busy traffic intersection) and its associated dynamic changes (such as traffic speed and pedestrian density) to generate a feature extraction data set. Secondly, based on the feature extraction data set, the system uses an augmented reality interactive algorithm to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map, determine the optimal visual and audio data transmission path, and generate multiple candidate path solution sets. Thirdly, the system performs performance analysis on each candidate path in the candidate path solution set, evaluates the transmission efficiency and quality, and sets an alternative path for each path to deal with network failures or emergencies, and generates a preliminary transmission path configuration. Finally, the system implements a real-time transmission test based on the preliminary transmission path configuration, monitors performance indicators such as delay and packet loss rate during the transmission process in real time, and automatically adjusts transmission parameters (such as changing video resolution and adjusting data compression ratio) during the test process, and finally generates the optimal transmission path configuration to ensure that stable and efficient remote monitoring and guidance services can be provided under any circumstances.

[0137] In order to solve the problems of depth and accuracy of event processing log analysis, in some embodiments, the text analysis of the communication content recorded in the event processing log and the generation of a remote monitoring and guidance plan described in step 104 include: semantically analyzing the communication content recorded in the event processing log, capturing important communication details in the semantic analysis result, and generating a semantic analysis result; using semantic analysis technology, identifying the core decision and actual execution action of each communication in the semantic analysis result, and generating a key decision point and a set of operation steps; using an effectiveness evaluation method to evaluate the actual effect of each operation in the operation step set, and compare it with the expected goal of each operation step, and generate an operation step effect evaluation report; based on the operation step effect evaluation report, applying a decision tree analysis method, providing path selection for each key decision point, and generating a remote monitoring and guidance plan.

[0138] In this embodiment, the event processing log is a detailed log that records all interactive contents in the entire event processing process, including decision points, operation steps, etc.

[0139] Semantic parsing refers to the use of natural language processing technology to gain an in-depth understanding of the communication content in event processing logs and capture important details.

[0140] The semantic analysis result is a data set generated by semantically analyzing the event processing log, which contains the deep meaning and important details of the communication content.

[0141] Semantic analysis technology is an advanced language processing method that can identify the core concepts, sentiment tendencies, logical structures, etc. in the text and convert them into quantifiable features. It is used to extract key information from the semantic analysis results.

[0142] The set of key decision points and operation steps is to identify the core decisions of each communication (such as launching an emergency plan) and the actual actions performed (such as evacuating the crowd) through semantic analysis technology, forming a set of all important decisions and operations.

[0143] The effectiveness evaluation method is a combination of quantitative and qualitative methods used to evaluate the actual effect of each operation and compare it with the expected goals. This method can help identify which operations have achieved the expected results and which ones need improvement.

[0144] The operation step effect evaluation report is a document generated based on the effectiveness evaluation method, which records in detail the actual effect of each operation step and the difference with the expected goal. This report provides a specific basis for subsequent optimization.

[0145] The decision tree analysis method is a decision model based on a tree structure that can select the best path based on different conditions. When applied to key decision points, it can provide multiple possible options and their potential impacts to help formulate better guidance strategies.

[0146] The remote monitoring and guidance plan is the final optimization plan, which aims to improve the efficiency and accuracy of handling similar incidents in the future.

[0147] In the embodiment of the present application, first, semantic analysis is performed on the communication content recorded in the event processing log to capture important communication details in the semantic analysis result, and generate a semantic analysis result; secondly, through the semantic analysis technology, the core decision and actual execution action of each communication in the semantic analysis result are identified, and key decision points and a set of operation steps are generated; thirdly, the effectiveness evaluation method is used to evaluate the actual effect of each operation in the operation step set, and compare it with the expected goal of each operation step, and generate an operation step effectiveness evaluation report; finally, based on the operation step effectiveness evaluation report, the decision tree analysis method is applied to provide path selection for each key decision point, and generate a remote monitoring and guidance plan.

[0148] Here is a specific example:

[0149] Suppose that the depth of event processing log analysis needs to be improved in the emergency response system of a large hospital. First, the system performs semantic analysis on the communication content between medical staff and the command center recorded in the event processing log, captures important communication details such as emergency surgery arrangements and drug allocation, and generates semantic analysis results; secondly, the system uses semantic analysis technology to identify the core decisions (such as dispatching an ambulance) and the actual actions performed (such as dispatching a specific medical team) in each communication, and generates key decision points and operation steps. Set; thirdly, the system uses the effectiveness evaluation method to evaluate the actual effect of each step in the operation step set, such as checking whether the ambulance arrives at the scene on time, and compares it with the expected goal, and generates an operation step effectiveness evaluation report; finally, based on the operation step effectiveness evaluation report, the system applies the decision tree analysis method to provide multiple path choices and their potential impacts for each key decision point (such as deciding whether to activate a backup operating room), and generates detailed remote monitoring and guidance plans to ensure that future emergency responses are more efficient and accurate.

[0150] This application considers that in order to solve the problem of inaccurate spatiotemporal consistency calibration between different monitoring nodes in the prior art, the invention embodiment proposes this optional solution. In the prior art, due to the existence of time stamp and geographic location information deviation, data synchronization difficulties and other issues, it is difficult to maintain high consistency and accuracy when multi-source data is fused, thus affecting the effect of remote monitoring and guidance. Therefore, a new optional solution is proposed, which includes:

[0151] Based on the preliminary screening data set, a multi-source data fusion algorithm is used to perform spatiotemporal consistency correction processing on the information from different monitoring nodes, and the spatiotemporal feature deviations of the information from different monitoring nodes in the same time period are compared to generate a spatiotemporal consistency correction data set, including:

[0152] Based on the preliminary screening data set, preprocessing the data of each monitoring node in the preliminary screening data set, synchronizing the data of all monitoring nodes on the time axis through the timestamp alignment technology, and using the spatial coordinate correction method to eliminate the deviation caused by the geographical location error, so as to generate a spatiotemporal feature deviation index;

[0153] The temporal and spatial characteristic deviation index is calculated by the following formula:

[0154]

[0155] Among them, Δ ij (t) is the temporal and spatial characteristic deviation index between monitoring node i and monitoring node j at time t; α is the comprehensive adjustment coefficient; β is the time deviation weight factor; T i (t) and T j(t) are the timestamps of monitoring nodes i and j at time t; γ is the position deviation correction coefficient; δ is the position influence factor; S i (t) and S j (t) are the spatial coordinates of monitoring nodes i and j at time t, respectively; t is a time variable used to refer to a specific time;

[0156] Based on the spatiotemporal characteristic deviation index, the spatiotemporal characteristic deviation index is combined with the data volume and verification value of the monitoring node to evaluate the consistency level between different monitoring nodes, and the change factors in the actual environment are simulated by introducing a dynamic adjustment coefficient and a fluctuation adjustment factor to generate a spatiotemporal consistency correction coefficient;

[0157] The spatiotemporal consistency correction coefficient is calculated using the following formula:

[0158]

[0159] Where, C(t) is the spatiotemporal consistency correction coefficient at time t; Δ ij (t) is the temporal and spatial characteristic deviation index between monitoring node i and monitoring node j at time t; ζ is the dynamic adjustment coefficient; D i (t) and D j (t) are the data volume of monitoring nodes i and j at time t; λ is the fluctuation adjustment coefficient; μ is the fluctuation frequency factor; V i (t) and V j (t) are the verification values ​​of monitoring nodes i and j at time t; η is the reliability adjustment coefficient; ν is the additional correction coefficient; ρ is the correlation factor; R i (t) and R j (t) are the correlation indices of monitoring nodes i and j at time t; i and j are the indexes of monitoring nodes, ranging from 1 to N; N is the number of monitoring nodes; t is a time variable, used to refer to a specific time;

[0160] Based on the spatiotemporal consistency correction coefficient, the correction coefficient is applied to adjust the timestamps and spatial coordinates of different monitoring nodes to maintain the spatiotemporal consistency of the different monitoring nodes in the same time period. The correction result is further refined through an iterative optimization method to generate a spatiotemporal consistency correction data set.

[0161] This method aims to quantify the temporal and spatial differences between monitoring nodes and optimize these differences through a series of adjustment coefficients, ultimately ensuring the temporal and spatial consistency of data from all monitoring nodes, solving the temporal and spatial synchronization problems existing in existing technologies, and providing more reliable and accurate data support to improve the performance of remote monitoring and guidance systems.

[0162] In the spatiotemporal characteristic deviation index, the time deviation term It is used to measure the timestamp difference between two monitoring nodes at a specific moment. Through the combination of cube root and hyperbolic sine function, it can effectively amplify the smaller time difference and suppress the influence of the larger time difference, so as to avoid the extreme value from causing too much interference to the result. The position deviation term γ·log(1+δ·|S i (t)-S j (t)|): used to evaluate the spatial coordinate difference between two monitoring nodes at the same time. The application of the logarithmic function makes the influence of position deviation on the total deviation gradually weaken as the distance increases, reflecting the natural attenuation characteristics in geographic space;

[0163] Among them, α and β can be preset according to system requirements; for the position deviation term, γ and δ select appropriate values ​​according to the actual application scenario; T i (t) and T j (t) is the time information collected from each monitoring node, usually obtained through a built-in clock synchronization protocol (such as NTP); S i (t) and S j (t) is the spatial coordinate of each monitoring node at the corresponding time, usually obtained through GPS or other positioning services;

[0164] In the spatiotemporal consistency correction coefficient, the spatiotemporal deviation weighting term Δ ij (t)·exp(-ζ·|D i (t)-D j (t)|): Combine the temporal and spatial characteristic deviation index with the data volume difference, and use the exponential decay function to reduce the impact of nodes with large data volume on other nodes; the fluctuation adjustment term 1+λ·sin 2 (μ·(V i (t)-V j (t))): Considering the fluctuation of verification value, the sine square function is used to simulate periodic changes to adapt to the dynamic fluctuations that may exist in the actual environment; reliability adjustment item Enhance the sensitivity to the difference in verification values, highlight the influence of small differences through the square root function, and ensure the stability of the correction coefficient; the correlation correction term v·log(1+ρ·|R i (t)-R j (t)|): Introducing correlation index and using logarithmic function smoothing to ensure that a reasonable correction coefficient can be obtained even when the correlation is low;

[0165] Among them, ζ is dynamically adjusted according to the actual environment; λ and μ are set according to historical data analysis; η is set according to the requirements of system stability; v and ρ are determined through experimental tests; D i (t) and D j (t) are the data volumes of the monitoring nodes, which can be obtained through statistical analysis; Vi (t) and V j (t) is the verification value of the monitoring node, which is usually automatically generated by the system or set manually; R i (t) and R j (t) is the correlation index of the monitoring node, which can be calculated by data analysis tools;

[0166] Assume that in a security monitoring system of a large warehouse, we first need to ensure data synchronization between multiple cameras and sensors; Assume that α=0.8, β=0.5, γ=0.6, δ=0.4, and assume that there are two monitoring nodes A and B, with timestamps of T at time A (t) = 15:30:00 and T B (t)=15:30:02, the spatial coordinates are S A (t) = (34.0522, -118.2437) and S B (t) = (34.0525, -118.2439). The first step of substituting the data:

[0167] Assume that ζ=0.3,λ=0.2,μ=0.1,η=0.5,ν=0.3,ρ=0.2, and there are two monitoring nodes A and B with the data volumes of D A (t) = 500KB and D B (t) = 450KB, the verification values ​​are V A (t) = 0.9 and V B (t)=0.85, and the correlation indices are R A (t) = 0.8 and R B (t) = 0.75;

[0168]

[0169] Assuming that the threshold is set to 0.9, since the calculated result 0.92 is greater than the set threshold, it shows that the spatiotemporal consistency correction scheme has high effectiveness and accuracy, and can ensure good spatiotemporal data synchronization between monitoring nodes. This is because the higher correction coefficient reflects that in the current environment, the corrected data can better maintain the consistency of time and space without affecting the integrity and real-time performance of the data. Through the above steps, the accuracy and reliability of monitoring data transmission are ensured, the response speed and decision-making efficiency of the security monitoring system are improved, and the stability and scientificity of the entire monitoring system are enhanced.

[0170] This application considers that in order to solve the problem of lack of adaptability and real-time performance in transmission path selection in the prior art, the embodiment of the invention proposes this optional solution. In the prior art, due to the inconsistency of geographic space and timestamps between different monitoring nodes and insufficient assessment of environmental factors, the remote monitoring and guidance system is not accurate enough in selecting the optimal visual and audio data transmission path, which in turn affects the user experience and decision-making efficiency. Therefore, a new optional solution is proposed, which includes:

[0171] Based on the feature extraction data set, an augmented reality interaction algorithm is used to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a delay-free scene view and generate a candidate path solution set, including:

[0172] Based on the feature extraction data set, the spatial coordinates of different monitoring nodes are standardized by geocoding technology, the spatial deviations between different monitoring nodes are eliminated by applying a multidimensional scaling algorithm, and potential erroneous data are identified and corrected by using an anomaly detection algorithm to generate an augmented reality interaction score;

[0173] The augmented reality interaction score is calculated using the following formula:

[0174] R AR (i,j,t)=α′·(1-exp(-β′·|G i (t)-G j (t)|))+γ′·sin(δ′·(T i (t)-T j (t)))

[0175] Among them, R AR (i, j) is the augmented reality interaction score between monitoring node i and monitoring node j at time t; α′ is the geographic space influence coefficient; β′ is the geographic space distance attenuation factor; G i and G j are the geographic spatial coordinates of monitoring nodes i and j respectively; γ′ is the timestamp correction coefficient; δ′ is the time difference period factor; T i and T j are the timestamps of monitoring nodes i and j respectively; t is a time variable, used to refer to a specific moment;

[0176] Based on the augmented reality interaction score, an environmental perception model is introduced to simulate the transmission effect under different environmental conditions, a Bayesian network is used to analyze the performance of each transmission path under different environmental parameters, and the transmission path weight is dynamically adjusted through a reinforcement learning method to generate an adaptability index;

[0177] The adaptability index is calculated using the following formula:

[0178]

[0179] Among them, A path (t) is the adaptability index of the transmission path at time t; σ is the interaction score weight coefficient; κ is the interaction score influence factor; ρ′ is the position deviation correction coefficient; λ′ is the position difference period factor; S i (t) and S j (t) are the spatial coordinates of monitoring nodes i and j at time t; θ is the environmental factor adjustment coefficient; v′ is the environmental factor influencing factor; F i (t) and F j (t) are the environmental parameters of monitoring nodes i and j at time t, respectively; i and j are the indexes of monitoring nodes, from 1 to N; N is the number of monitoring nodes; t is a time variable, used to refer to a specific moment;

[0180] Based on the adaptability index, a multi-level evaluation system is constructed using the hierarchical analysis method. The comprehensive score of each transmission path is calculated through weight allocation and scoring matrix. The simulated annealing algorithm is used to perform global search to generate a set of candidate path solutions.

[0181] This method aims to improve the accuracy and real-time performance of transmission path selection in remote monitoring and guidance systems. By quantifying the interaction quality between monitoring nodes, it ensures that the selected path can provide the best data transmission effect in various environments, solves the path selection problem existing in the existing technology, and provides more reliable and efficient data support to improve the performance of the system.

[0182] In the augmented reality interaction score, the geospatial influence term α'·(1-exp(-β'·|G i (t)-G j (t)|)) measures the impact of the difference in geospatial coordinates between two monitoring nodes on the interaction score. The exponential decay function is used to reduce the impact of distant nodes on the score. The timestamp correction term γ'·sin(δ'·(T i (t)-T j (t))): Considering the periodic changes of timestamp differences, a sine function is used to simulate the fluctuations in time;

[0183] Among them, α' and β' can be pre-set according to system requirements; γ' and δ' can select appropriate values ​​according to actual application scenarios; G i (t) and G j (t) is the spatial coordinate of the monitoring node, usually obtained through GPS or other positioning services; T i (t) and T j (t) is the timestamp information of the monitoring node, which can be obtained through the built-in clock synchronization protocol (such as NTP);

[0184] In the fitness index, the interaction score weighting term σ·log(1+κ·R AR (i,j)): Combined with the augmented reality interaction score, the logarithmic function is used to smooth the score to ensure that the paths with higher scores have a greater contribution to the total index; the position deviation correction term ρ'·cos(λ'·|S i (t)-S j (t)|): Considering the influence of spatial coordinate difference on adaptability index, the cosine function is used to simulate the periodic change of position deviation; environmental factor adjustment item Introducing environmental parameter differences, the square root function is used to highlight the impact of smaller differences and ensure the stability of the adaptability index;

[0185] Among them, σ and κ can be pre-set according to system requirements; ρ' and λ' select appropriate values ​​according to the actual application scenario; for environmental factor adjustment items, θ and v' are determined through experimental tests; R AR (i, j) is the calculated augmented reality interaction score; S i (t) and S j (t) is the spatial coordinate of the monitoring node, usually obtained through GPS or other positioning services; F i (t) and F j (t) is the environmental parameter of the monitoring node, such as temperature, humidity, etc., which can be collected by sensors;

[0186] Assume that in a security monitoring system of a large manufacturing industrial park, the first thing to do is to ensure data synchronization between multiple cameras and sensors; Assume that α'=0.7, β'=0.4, γ'=0.5, δ'=0.3, and assume that there are two monitoring nodes A and B, and the timestamps at time t are T A (t) = 16:00:00 and T B (t) = 16:00:02, the spatial coordinates are G A (t) = (34.0522, -118.2437) and G B (t) = (34.0525, -118.2439);

[0187] R AR (A,B,t)=0.7·(1-exp(-0.4·∣(34.0522,-118.2437)-(34.0525,-118.2439)∣))+0.5·sin(0.3·(16:00:00-16:00:02))=0.68;

[0188] Assume that σ=0.6,κ=0.5,ρ'=0.4,λ'=0.3,θ=0.5,v'=0.2, and assume that there are two monitoring nodes A and B with spatial coordinates S A(t) = (34.0522, -118.2437) and S B (t)=(34.0525,-118.2439), the environmental parameters are F A (t) = 25°C and F B (t) = 26°C;

[0189]

[0190] Assuming that the threshold is set to 0.9, since the calculated result 0.91 is greater than the set threshold, it shows that the transmission path optimization scheme has high effectiveness and adaptability, and can ensure the selection of the best data transmission path between monitoring nodes under different environmental conditions. This is because the higher adaptability index reflects that in the current environment, the optimized path can better adapt to various changing factors while maintaining the stability and real-time performance of data transmission. Through the above steps, the optimization and reliability of monitoring data transmission are ensured, the response speed and decision-making efficiency of the security monitoring system are improved, and the stability and scientificity of the entire monitoring system are enhanced.

[0191] Figure 2 A schematic diagram of a remote monitoring and guidance system is provided for an embodiment of the present application. Figure 2 As shown, the device comprises:

[0192] The detection module 21 is used to deploy intelligent analysis engines at multiple geographically dispersed monitoring nodes, issue alarm information in real time when abnormal behavior is detected, and generate a multimedia evidence information package; the multimedia evidence information package includes video and audio data, as well as environmental parameters;

[0193] The fusion module 22 is used to use a multi-source data fusion algorithm to perform spatiotemporal consistency correction processing and fusion on multimedia evidence information packages from different monitoring nodes, eliminate redundant information in the multimedia evidence information packages, and use distributed situation awareness technology to add time and geographic space axes to multimedia evidence information packages from different monitoring nodes, intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes, and generate a comprehensive situation awareness map; the comprehensive situation awareness map is a visual interface that combines time and geographic space dimensions and is used to present and analyze multi-source data;

[0194] Interaction module 23, used to use augmented reality interaction algorithm, by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map, determine the optimal visual and audio data transmission path to present a delay-free on-site view, adopt immersive two-way interaction technology, by introducing augmented reality equipment, access the on-site real-time video stream of different monitoring nodes, provide instant operation guidance for on-site personnel, and generate event processing logs;

[0195] The analysis module 24 is used to perform text analysis on the communication content recorded in the event processing log, extract key decision points and operation steps of the communication content, use the effectiveness evaluation method to perform effect analysis on the key decision points and operation steps, and generate a remote monitoring and guidance plan.

[0196] Figure 2 The remote monitoring and guidance system can be implemented Figure 1 The implementation principle and technical effect of the remote monitoring and guidance method described in the embodiment are not described in detail. The specific way in which each module and unit performs operations in the remote monitoring and guidance system in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0197] In one possible design, Figure 2 A remote monitoring and guidance system of the illustrated embodiment may be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0198] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0199] The processing component 32 is used to: deploy intelligent analysis engines at multiple geographically dispersed monitoring nodes, issue alarm information in real time when abnormal behavior is detected, and generate multimedia evidence information packages; the multimedia evidence information packages contain video and audio data, as well as environmental parameters; use a multi-source data fusion algorithm to perform spatiotemporal consistency correction processing and fusion on multimedia evidence information packages from different monitoring nodes, eliminate redundant information in the multimedia evidence information packages, use distributed situation awareness technology to add time and geographic space axes to multimedia evidence information packages from different monitoring nodes, intuitively display the dynamic situation of multiple geographically dispersed monitoring node areas, and generate a comprehensive situation awareness map; the comprehensive situation awareness map is a combination of time and geography. A visualization interface of geographical space dimension is used to present and analyze multi-source data; an augmented reality interaction algorithm is used to analyze the geographic space information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a delay-free on-site view; an immersive two-way interactive technology is used to introduce augmented reality equipment to access the on-site real-time video streams of different monitoring nodes, provide instant operation guidance for on-site personnel, and generate an event processing log; text analysis is performed on the communication content recorded in the event processing log, key decision points and operation steps of the communication content are extracted, and the effectiveness evaluation method is used to analyze the effectiveness of the key decision points and operation steps to generate a remote monitoring and guidance plan.

[0200] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0201] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0202] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0203] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0204] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0205] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0206] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A remote monitoring and guidance method of the illustrated embodiment.

[0207] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0208] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0209] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0210] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A remote monitoring and guidance method, characterized in that: include: Deploy intelligent analysis engines at multiple geographically dispersed monitoring nodes to issue real-time alerts and generate multimedia evidence packages when abnormal behavior is detected; The multimedia evidence information package includes video and audio data, and environmental parameters; Using a multi-source data fusion algorithm, the multimedia evidence information packages from different monitoring nodes are corrected for time and space consistency and fused to eliminate redundant information in the multimedia evidence information packages. Distributed situation awareness technology is used to add time and geographic space axes to the multimedia evidence information packages from different monitoring nodes, intuitively displaying the regional dynamics of multiple geographically dispersed monitoring nodes, and generating a comprehensive situation awareness map; the comprehensive situation awareness map is a visual interface that combines time and geographic space dimensions and is used to present and analyze multi-source data; Using augmented reality interaction algorithms, by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map, determining the optimal visual and audio data transmission path to present a delay-free on-site view, using immersive two-way interactive technology, by introducing augmented reality devices, accessing the on-site real-time video streams of different monitoring nodes, providing instant operation guidance for on-site personnel, and generating event processing logs; Perform text analysis on the communication content recorded in the event processing log, extract key decision points and operating steps from the communication content, use effectiveness evaluation methods to analyze the effectiveness of the key decision points and operating steps, and generate remote monitoring and guidance plans.

2. The method according to claim 1, characterized in that The multi-source data fusion algorithm is used to perform spatiotemporal consistency correction processing and fusion on multimedia evidence information packages from different monitoring nodes, thereby eliminating redundant information in the multimedia evidence information packages. The distributed situation awareness technology is used to add time and geographic space axes to multimedia evidence information packages from different monitoring nodes, intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes, and generate a comprehensive situation awareness map, including: Using a multi-source data fusion algorithm, the multimedia evidence information packages from different monitoring nodes are corrected for spatiotemporal consistency, and the multimedia evidence information packages after spatiotemporal consistency correction are deeply fused, and redundant information in the multimedia evidence information packages is eliminated to generate a fused multi-source data set; Based on the fused multi-source data set, the distributed situation awareness technology is used to classify and annotate the multimedia evidence information packages from different monitoring nodes and their corresponding time and geographical locations, and the time and geographical space axes are introduced to map the classification and annotation results to generate a multidimensional data structure; Based on the multi-dimensional data structure, visualization technology is used to construct a global perspective to intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes and generate a comprehensive situation awareness map.

3. The method according to claim 2, characterized in that The multi-source data fusion algorithm is used to perform spatiotemporal consistency correction processing on multimedia evidence information packages from different monitoring nodes, and the multimedia evidence information packages after spatiotemporal consistency correction are deeply fused, and redundant information in the multimedia evidence information packages is eliminated to generate a fused multi-source data set, including: Using multi-source data fusion algorithm, the multimedia evidence information from different monitoring nodes is corrected for spatiotemporal consistency. By comparing the spatiotemporal feature deviations of multimedia evidence information from different monitoring nodes in the same time period, a spatiotemporal consistency correction data set is generated. Based on the spatiotemporal consistency correction data set, deep fusion processing is performed on the information from different monitoring nodes that effectively reflects the actual situation, and redundant information in the multimedia evidence information package is eliminated to generate a preliminary fused multi-source data set; Based on the preliminary fused multi-source data set, the deep fusion processing result is monitored in real time, and the preliminary fused multi-source data set is cleaned using statistical methods to generate a fused multi-source data set.

4. The method according to claim 2, characterized in that: Based on the fused multi-source data set, the distributed situation awareness technology is used to classify and annotate the multimedia evidence information packages from different monitoring nodes and their corresponding time and geographical locations, and the time and geographical space axes are introduced to map the classification and annotation results to generate a multidimensional data structure, including: Based on the fused multi-source data set, identifying time and location metadata in multimedia evidence information packages from different monitoring nodes, classifying the time and location metadata into specific time periods and geographic locations, and generating a classified and labeled data set; Based on the classified and annotated data set, using distributed situational awareness technology, introducing time and geographic space axes, mapping the time and geographic location information corresponding to each data point in the classified and annotated data set, and generating a time-geolocation mapping data structure; Based on the temporal geographic mapping data structure, using statistical methods, statistically analyzing the data distribution of each data point after the mapping process, correcting potential distribution anomalies and deviations of the data distribution, and generating optimized data distribution; Based on the optimized data distribution, dynamic simulation technology is used to simulate the behavior patterns of the optimized data distribution at different time points and geographical locations, identify potential risk points of the behavior patterns, adjust the optimized data distribution, and generate a multidimensional data structure.

5. The method according to claim 1, characterized in that: The augmented reality interaction algorithm is used to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a delay-free on-site view, and the immersive two-way interactive technology is used to introduce augmented reality equipment to access the on-site real-time video streams of different monitoring nodes to provide instant operation guidance for on-site personnel and generate event processing logs, including: Parsing the geospatial information and real-time dynamic data in the comprehensive situation awareness map, identifying key location points in the geospatial information and associated dynamic changes in the real-time dynamic data, and generating geospatial and dynamic data analysis results; Based on the geospatial and dynamic data analysis results, an augmented reality interaction algorithm is used to determine the optimal visual and audio data transmission path by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map to present a delay-free scene view and generate an optimal transmission path configuration; Based on the optimal transmission path configuration, immersive two-way interactive technology is adopted, and the on-site real-time video streams of the different monitoring nodes are accessed by introducing augmented reality devices, providing instant operation guidance for on-site personnel and generating immersive interactive session records; Based on the immersive interactive session record, behavioral analysis technology is used to perform behavioral analysis on all interactive contents in the immersive interactive session record, extract key operation requirements and decision points in all interactive contents, and generate an event processing log.

6. The method according to claim 5, characterized in that Based on the analysis results of the geospatial and dynamic data, an augmented reality interaction algorithm is used to analyze the geospatial information and real-time dynamic data in the comprehensive situation awareness map to determine the optimal visual and audio data transmission path to present a delay-free on-site view and generate an optimal transmission path configuration, including: Based on the geographic space and dynamic data analysis results, feature extraction and annotation processing are performed on each key location point and its associated dynamic changes in the geographic space and dynamic data analysis results to generate a feature extraction data set; Based on the feature extraction data set, an augmented reality interaction algorithm is used to determine the optimal visual and audio data transmission path by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map to present a delay-free scene view and generate a set of candidate path solutions; Based on the candidate path solution set, a performance analysis is performed on each candidate path in the candidate path solution set, the transmission efficiency and quality of each candidate path are evaluated, and an alternative path is set for each candidate path to cope with emergencies, and a preliminary transmission path configuration is generated; Based on the preliminary transmission path configuration, a real-time transmission test based on the preliminary transmission path configuration is implemented, and the performance indicators during the real-time transmission test are monitored in real time. During the real-time monitoring process, the transmission parameters in the preliminary transmission path configuration are automatically adjusted to generate an optimal transmission path configuration.

7. The method according to claim 1, characterized in that The text analysis of the communication content recorded in the event processing log is performed to extract the key decision points and operation steps of the communication content, and the effectiveness evaluation method is used to perform effect analysis on the key decision points and operation steps to generate a remote monitoring and guidance plan, including: Performing semantic analysis on the communication content recorded in the event processing log, capturing important communication details in the semantic analysis result, and generating a semantic analysis result; Through semantic analysis technology, the core decision and actual execution action of each communication in the semantic analysis results are identified, and key decision points and operation steps are generated; Use the effectiveness evaluation method to evaluate the actual effect of each operation in the operation step set, and compare it with the expected goal of each operation step, and generate an operation step effect evaluation report; Based on the effect evaluation report of the operation steps, the decision tree analysis method is applied to provide path selection for each key decision point and generate a remote monitoring and guidance plan.

8. A remote monitoring and guidance system, characterized in that: include: The detection module is used to deploy intelligent analysis engines in multiple geographically dispersed monitoring nodes, issue real-time alarm information when abnormal behavior is detected, and generate multimedia evidence information packages; The multimedia evidence information package includes video and audio data, and environmental parameters; A fusion module is used to apply a multi-source data fusion algorithm to perform spatiotemporal consistency correction processing and fusion on multimedia evidence information packages from different monitoring nodes, eliminate redundant information in the multimedia evidence information packages, and use distributed situation awareness technology to add time and geographic space axes to multimedia evidence information packages from different monitoring nodes, intuitively display the regional dynamics of multiple geographically dispersed monitoring nodes, and generate a comprehensive situation awareness map; the comprehensive situation awareness map is a visual interface that combines time and geographic space dimensions and is used to present and analyze multi-source data; An interactive module, for using an augmented reality interactive algorithm to determine the optimal visual and audio data transmission path by analyzing the geospatial information and real-time dynamic data in the comprehensive situation awareness map, so as to present a delay-free on-site view, adopt an immersive two-way interactive technology, introduce augmented reality equipment, access the on-site real-time video streams of the different monitoring nodes, provide instant operation guidance for on-site personnel, and generate an event processing log; The analysis module is used to perform text analysis on the communication content recorded in the event processing log, extract key decision points and operation steps of the communication content, use the effectiveness evaluation method to perform effect analysis on the key decision points and operation steps, and generate a remote monitoring and guidance plan.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a remote monitoring and guidance method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a remote monitoring and guiding method as claimed in any one of claims 1 to 7 is implemented.

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