Intelligent management system for chemical production safety based on video monitoring

Abstract

The application relates to the technical field of chemical production management, in particular to a chemical production safety intelligent management system based on video monitoring, which comprises a video stream optimization module, a behavior analysis and evaluation module, a real-time risk management module, a monitoring process optimization module, a behavior mode updating module and an emergency response correction module.In the application, the quality of the video stream is optimized, the high definition and real-time performance of the monitoring content are ensured, the system can more effectively identify abnormal conditions in production, through detailed behavior tracking and quantitative analysis, the deviation between operation and safety standards can be accurately identified and timely adjusted, the standardization of operation behavior can be accurately evaluated, the behavior deviating from the standards can be timely corrected, the effectiveness of safety management is improved, the real-time risk management improves the early warning capability for potential dangers through automatic safety level classification and early warning threshold adjustment, the safety response is more flexible, and the probability of accidents is greatly reduced.

Description

Technical Field

[0001] This invention relates to the field of chemical production management technology, and in particular to a video surveillance-based intelligent management system for chemical production safety. Background Technology

[0002] Chemical production management is an important technical field in the chemical industry, involving multiple aspects of the production, processing and control of chemicals. Its main objectives are to improve production efficiency, ensure product quality, optimize resource utilization and minimize environmental impact. With the advancement of technology, especially the development of information technology and automation technology, chemical production management is also gradually developing towards a more intelligent and automated direction.

[0003] Among them, the intelligent management system for chemical production safety with video surveillance focuses on using video surveillance technology to enhance the safety management of chemical production. The system uses cameras installed in key production areas to monitor the physical and chemical reactions in the production process and the safe behavior of workers in real time. By analyzing the video stream, the system can automatically detect potential safety hazards, such as abnormal equipment operation, leaks, or unauthorized personnel entering areas. This not only improves the response speed to emergencies and reduces the risk of accidents, but also provides decision support for future safety management through historical data analysis.

[0004] Traditional systems have limitations in image processing. For example, changes in lighting and dynamic capture are difficult to adjust in real time in rapidly changing production environments, affecting the accuracy of anomaly identification. The lack of automated safety level classification and threshold adjustment in the risk assessment process prevents the early warning system from being optimized in real time according to actual risks. This can lead to delays or false alarms when dealing with emergencies, increasing the complexity and resource consumption of accident handling. These limitations are particularly prominent in high-risk chemical production processes, often resulting in safety accidents due to untimely or inaccurate responses. Summary of the Invention

[0005] To address the limitations of existing technologies in image processing, such as the difficulty in real-time adjustment of lighting changes and motion capture in rapidly changing production environments, which affects the accuracy of anomaly identification, and the lack of automated safety level classification and threshold adjustment in risk assessment, which prevents early warning systems from optimizing in real time according to actual risks, leading to delays or false alarms in emergency situations and increasing the complexity and resource consumption of accident handling, these limitations are particularly prominent in high-risk chemical production processes. These limitations often result in safety accidents due to untimely or inaccurate responses. Therefore, this invention provides a video surveillance-based intelligent safety management system for chemical production. The technical solution is as follows:

[0006] On the one hand, a video surveillance-based intelligent management system for chemical production safety is provided, including:

[0007] The video stream optimization module performs image resolution and frame rate adjustments based on the real-time video stream of chemical production captured by the camera, eliminates the effects of background noise and lighting changes, decodes and compresses the signal, and then corrects color distortion and motion blur to obtain optimized video stream quality.

[0008] The behavior analysis and evaluation module tracks behavior changes between keyframes based on the optimized video stream quality, identifies deviations from standard operations, and evaluates the standardization of operational behavior based on the deviations to obtain a behavior deviation index.

[0009] Based on the behavioral deviation indicators, the real-time risk management module identifies behavioral patterns that deviate from standard operating procedures, automatically classifies safety levels, calculates and predicts the probability of occurrence of multiple levels of risks, and automatically adjusts the warning threshold to obtain risk level response information.

[0010] Based on the risk level response information, the monitoring process optimization module distributes the early warning notification to the monitoring center and the on-site operation area, monitors the personnel status in the on-site operation area, adjusts the monitoring parameters according to the personnel feedback information, evaluates the early warning response effect, and obtains the early warning effect evaluation result.

[0011] Based on the early warning effect evaluation results, the behavior pattern update module continuously monitors the behavior pattern characteristics in the video stream, compares them with historical behavior data, identifies new behavior trends or behavior changes, and obtains an updated behavior database.

[0012] Based on the updated behavior database, the emergency response correction module provides behavioral correction guidance to operators, formulates emergency shutdown and intervention measures for monitored risky behaviors, and obtains accident prevention and control management logs.

[0013] On the other hand, the optimized video stream quality includes signal integration, dynamic range, and frame integration; the behavioral deviation indicators include deviation intensity, consistency index, and behavioral deviation frequency; the risk level response information includes risk discrimination threshold, response level classification, and warning signal strength; the warning effect evaluation results include response effectiveness, reaction adjustment time, and monitoring efficiency; and the updated behavioral database specifically includes behavioral pattern recognition results, pattern change records, and behavioral sequence analysis results.

[0014] On the other hand, the video stream optimization module includes an image quality adjustment submodule and an image stabilization submodule;

[0015] The image quality adjustment submodule adjusts the resolution and frame rate of the image based on the real-time video stream of chemical production captured by the camera, matches the environmental requirements of different monitoring, and optimizes the impact of background noise and lighting changes to obtain video quality optimized recordings.

[0016] The signal processing submodule decodes and compresses the video quality optimization recording, adjusts the data transmission parameters and storage requirements, corrects color distortion and motion blur, optimizes the clarity of the monitoring image, and obtains optimized video stream quality.

[0017] On the other hand, the behavior analysis and evaluation module includes a behavior tracking submodule, a deviation analysis submodule, and a normative evaluation submodule;

[0018] The behavior tracking submodule monitors the behavior in the video frame sequence based on the optimized video stream quality, captures behavior changes between key frames, marks each key frame with a timestamp, associates behavior events with operation areas, refines the correspondence between time and space data, and obtains behavior tracking mapping data.

[0019] The deviation analysis submodule compares the operator's behavior with the standard behavior frame by frame based on the behavior tracking mapping data, identifies the behavior deviation, determines the degree of deviation, and counts the frequency and time distribution of the deviation behavior to obtain the behavior deviation metric.

[0020] The normative assessment submodule performs a behavior compliance check based on the behavior deviation metric, compares it with the safety operation standard, assesses the compliance range of the behavior deviation, quantifies the potential impact of the deviation on the safety baseline, and obtains the behavior deviation index.

[0021] On the other hand, the process of comparing operator behavior with standard behavior frame by frame to identify behavioral deviations and determine the degree of deviation is carried out using the following formula:

[0022]

[0023] Calculate the behavioral deviation metric D, and statistically analyze the frequency and temporal distribution of the deviant behavior to obtain the behavioral deviation metric, where α k Represents the critical weight of the k-th frame, O k S represents the operator behavior data in the k-th frame. k This represents the standard behavioral data in the k-th frame, where m is the total number of frames.

[0024] On the other hand, the real-time risk management module includes a behavior pattern recognition submodule, a security level classification submodule, and an early warning adjustment submodule;

[0025] The behavior pattern recognition submodule identifies behavior patterns that deviate from standard operations based on the behavior deviation index, classifies them according to the degree of deviation between the behavior and the standard, determines the behaviors that deviate from standard operations, and obtains a behavior pattern recognition list.

[0026] The security level classification submodule classifies the monitored behavior patterns according to the severity of risk based on the behavior pattern recognition list, assesses the potential threat of each behavior pattern to security, and classifies the security level according to the severity of risk to obtain risk level distribution information.

[0027] The early warning adjustment submodule automatically adjusts the early warning threshold based on the risk level distribution information and dynamically adjusts the early warning response level according to the changing safety risk data to obtain risk level response information.

[0028] On the other hand, the monitored behavioral patterns are classified according to risk severity, and the potential threat to security of each behavioral pattern is assessed using the following formula:

[0029]

[0030] The safety levels are then classified according to the severity of the risk, resulting in risk level distribution information, where R i β represents the risk level of the i-th behavioral pattern. j The risk coefficient f(B) represents the j-th behavioral characteristic. j ) represents the risk function value of the j-th behavioral feature, and n represents the total number of behavioral features.

[0031] On the other hand, the monitoring process optimization module includes an early warning information distribution submodule, a monitoring optimization submodule, and an early warning effect evaluation submodule;

[0032] The early warning information distribution submodule extracts the early warning content based on the risk level response information, prioritizes the transmission of high-risk level early warning notifications to the monitoring center, determines the sending status of the notification information, records the sending time and received feedback information, and obtains the notification transmission record.

[0033] The monitoring optimization submodule analyzes the response behavior data of on-site personnel based on the notification transmission record, adjusts the direction and focal length range of the monitoring camera, monitors the dynamic changes of the on-site operation area in real time, corrects the monitoring blind spot position, and generates monitoring adjustment details.

[0034] The early warning effect evaluation submodule, based on the monitoring adjustment details, filters the received early warning feedback data, statistically analyzes the response time and coverage of different early warning levels, compares it with the processing status of early warning information and standard processing requirements, evaluates the early warning response effect, and obtains the early warning effect evaluation result.

[0035] On the other hand, the behavior pattern update module includes a behavior analysis submodule, a behavior comparison submodule, and a database update submodule;

[0036] Based on the early warning effect evaluation results, the behavior analysis submodule performs real-time analysis on the behavior data captured in the video stream, extracts key behavior features, separates and marks key behavior changes, and obtains behavior feature extraction results.

[0037] Based on the extracted behavioral features, the behavior comparison submodule compares the current behavioral data with the behavioral patterns stored in the database to identify new or changing behavioral trends and obtain behavioral trend comparison information.

[0038] The database update submodule incorporates newly mined behavioral trends and key data points into the database based on the behavioral trend comparison information, and optimizes the data structure to obtain an updated behavioral database.

[0039] On the other hand, the emergency response correction module includes a behavior guidance submodule and an accident management submodule;

[0040] The behavior guidance submodule provides behavioral guidance to operators based on the updated behavior database, analyzes unsafe behavior patterns, and refines operators' understanding of risky behaviors through simulation training and behavior correction to obtain behavior correction guidance data.

[0041] Based on the behavior correction guidance data, the incident management submodule formulates emergency shutdown and intervention measures for high-risk behaviors, adjusts operating procedures and matches potential security threats, optimizes the probability of incident occurrence and the effectiveness of intervention, and obtains incident prevention and control management logs.

[0042] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0043] By optimizing the quality of the video stream, high definition and real-time monitoring of the content are ensured, enabling the system to more effectively identify anomalies in production. Through meticulous behavior tracking and quantitative analysis, deviations between operations and safety standards can be accurately identified and timely adjustments made. This allows for precise assessment of the standardization of operational behaviors, timely correction of deviations from standards, and improved effectiveness of safety management. Real-time risk management, through automated safety level classification and early warning threshold adjustment, enhances the ability to warn of potential hazards, making safety responses more flexible and significantly reducing the probability of accidents. The real-time evaluation of comprehensive monitoring and early warning effects further enhances the dynamic adaptability of risk management, ensuring the simultaneous improvement of production safety and efficiency. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the system of the present invention;

[0046] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0047] Figure 3 This is a flowchart of the video stream optimization module of the present invention;

[0048] Figure 4 This is a flowchart of the behavior analysis and evaluation module of the present invention;

[0049] Figure 5 This is a flowchart of the real-time risk management module of the present invention;

[0050] Figure 6 This is a flowchart of the monitoring process optimization module of the present invention;

[0051] Figure 7 This is a flowchart of the behavior pattern update module of the present invention;

[0052] Figure 8 This is a flowchart of the emergency response correction module of the present invention. Detailed Implementation

[0053] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0054] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0055] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0056] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0057] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0058] This invention provides a video surveillance-based intelligent management system for chemical production safety, such as... Figure 1 As shown, the system includes:

[0059] The video stream optimization module performs image resolution and frame rate adjustments based on the real-time video stream of chemical production captured by the camera, eliminates the effects of background noise and lighting changes, decodes and compresses the signal, and then corrects color distortion and motion blur to obtain optimized video stream quality.

[0060] The behavior analysis and evaluation module tracks behavior changes between keyframes based on optimized video stream quality. By quantitatively analyzing inter-frame differences, it identifies deviations from standard operations and evaluates the standardization of operational behavior based on these deviations, thus obtaining a behavior deviation index.

[0061] The real-time risk management module identifies behavioral patterns that deviate from standard operating procedures based on behavioral deviation indicators, automatically classifies safety levels according to the degree of deviation, calculates and predicts the probability of occurrence of multiple levels of risks, and automatically adjusts the warning threshold to obtain risk level response information.

[0062] The monitoring process optimization module distributes early warning notifications to the monitoring center and on-site operation area based on risk level response information, monitors the status of personnel in the on-site operation area, adjusts monitoring parameters based on personnel feedback, evaluates the early warning response effect, and obtains early warning effect evaluation results.

[0063] The behavior pattern update module continuously monitors the behavior pattern characteristics in the video stream based on the early warning effect evaluation results, compares them with historical behavior data, identifies new behavior trends or behavior changes, and obtains an updated behavior database.

[0064] The emergency response correction module, based on the updated behavior database, provides behavioral correction guidance to operators, formulates emergency shutdown and intervention measures for monitored risky behaviors, optimizes the probability of accidents and potential risks, and generates accident prevention and control management logs.

[0065] Optimized video stream quality includes signal integration, dynamic range, and frame integration; behavioral deviation indicators include deviation intensity, consistency index, and behavioral deviation frequency; risk level response information includes risk discrimination threshold, response level classification, and warning signal strength; warning effect evaluation results include response effectiveness, reaction adjustment time, and monitoring efficiency; and the updated behavioral database specifically includes behavioral pattern recognition results, pattern change records, and behavioral sequence analysis results.

[0066] like Figure 2 and Figure 3 As shown, the video stream optimization module includes an image quality adjustment submodule and an image stabilization submodule;

[0067] The image quality adjustment submodule adjusts the resolution and frame rate of the image based on the real-time video stream of chemical production captured by the camera, matches the environmental requirements of different monitoring, and optimizes the impact of background noise and lighting changes to obtain video quality optimized recordings.

[0068] The video data is connected to the processing module via an interface. To adjust the image resolution, the specific parameters of the resolution are dynamically set to adjust the clarity. At the same time, the time interval of the frame sequence is adjusted to adapt to the needs of different environments. Then, the noise processing module is used to remove noise from the video background. Unnecessary high-frequency noise and spot effects are filtered out through iterative calculation. At the same time, changes in lighting are gradually corrected, and the brightness changes of pixels are limited and abnormal parts are repaired. Finally, the optimized video stream is saved as a log file for subsequent operations.

[0069] The signal processing submodule decodes and compresses the video quality optimization recording, adjusts the data transmission parameters and storage requirements, corrects color distortion and motion blur, optimizes the clarity of the monitoring image, and obtains optimized video stream quality.

[0070] The signal features in the video are extracted using standard decoding methods. The color signal is decomposed into basic channel information and abnormal data that does not conform to the normal range is removed. Then, compression technology is used to reduce storage space requirements. The compression parameters are adjusted to reduce the file size. At the same time, to address the color distortion problem in the picture, the color display is gradually corrected by comparing the color deviation data of the video signal. Furthermore, the motion blur problem in the picture is optimized by adjusting the pixel position information of each frame to compensate and repair the blurred parts, generating an optimized and clear video stream.

[0071] like Figure 2 and Figure 4 As shown, the behavior analysis and evaluation module includes a behavior tracking submodule, a deviation analysis submodule, and a normative evaluation submodule;

[0072] The behavior tracking submodule monitors behavior in video frame sequences based on optimized video stream quality, captures behavior changes between key frames, marks each key frame with timestamps, associates behavior events with operation areas, refines the correspondence between time and space data, and obtains behavior tracking mapping data.

[0073] The video frames are sequence-analyzed to extract keyframes. Based on the feature regions that change significantly between frames, a frame difference threshold is set to determine which frames are keyframes. Then, the behavioral features of the keyframes are marked, and the occurrence time of each keyframe is recorded using a timestamp. At the same time, specific action patterns and spatial regions involved in each frame are identified, and actions are divided into multiple subcategories. The correspondence between subcategories and spatial locations is recorded. Subsequently, a time-space mapping table is constructed to record the occurrence time and specific spatial operation area of ​​each behavioral event. The generated mapping data is checked to ensure that the time record of the data is complete, the spatial region is unique and accurate, and to obtain behavior tracking mapping data that can reflect the correspondence between behavior and operation area.

[0074] The deviation analysis submodule compares the operator's behavior with the standard behavior frame by frame based on the behavior tracking mapping data, identifies the behavior deviation, determines the degree of deviation, and counts the frequency and time distribution of the deviation behavior to obtain the behavior deviation metric.

[0075] The actual operator behaviors collected are compared one by one with predefined standard behavior templates. The spatial position changes and temporal differences of each behavior are analyzed frame by frame. The changes are recorded and classified. Different thresholds are set for spatial position offset and temporal offset, and the groups are grouped according to the offset. The frequency and temporal distribution of behavior offset are statistically analyzed. At the same time, a time distribution histogram is drawn using statistical methods to record the concentrated distribution characteristics of offset behaviors at different time points. In addition, by statistically analyzing the overall data of each type of offset behavior, the average value and distribution range of offset behaviors are calculated to comprehensively evaluate the offset of operator behavior and obtain specific measurement data on the degree of deviation of operator behavior.

[0076] The operator's behavior is compared frame by frame with the standard behavior to identify behavioral deviations and determine the degree of deviation using the following formula:

[0077]

[0078] Calculate the behavioral deviation metric D, and statistically analyze the frequency and temporal distribution of the deviant behavior to obtain the behavioral deviation metric, where α k Represents the critical weight of the k-th frame, O k S represents the operator behavior data in the k-th frame. k This represents the standard behavioral data in the k-th frame, where m is the total number of frames.

[0079] Considering three frames of data within one operation cycle, the following data was collected:

[0080] α1=0.3, O1=10, S1=8;

[0081] α2=0.5, O2=14, S2=10;

[0082] α3=0.2, O3=7, S3=7;

[0083] For each frame, calculate the absolute value of the difference multiplied by the weight:

[0084] α1|O1-S1|=0.3×|10-8|=0.6;

[0085] α2|O2-S2|=0.5×|14-10|=2.0;

[0086] α3|O3-S3|=0.2×|7-7|=0.0;

[0087] Sum and calculate the overall deviation measure:

[0088]

[0089] The results showed that the overall behavioral deviation measure was 0.87, indicating that there was a certain degree of deviation between the operator's behavior and the standard behavior during the monitoring period. This reflects the overall degree of behavioral deviation and is used to assess the operator's level of behavioral standardization.

[0090] The normative assessment submodule performs behavioral compliance checks based on behavioral deviation metrics, compares the results with safe operating standards, assesses the compliance range of behavioral deviations, quantifies the potential impact of deviations on the safety baseline, and obtains behavioral deviation indicators.

[0091] By comparing and analyzing deviation data with safe operating standards, deviation data related to safety compliance is extracted. The potential impact of each data point on the operational safety baseline is analyzed item by item. Using a pre-defined scoring table, different importance weights are assigned to each deviation data point. The impact of different deviation factors is analyzed based on the weights. Subsequently, all deviation factors are categorized, and the compliance scope and importance score of each deviation category are calculated. The scores are compared with the requirements of safety standards to quantify the safety impact of behavioral deviations and derive an overall behavioral deviation index.

[0092] like Figure 2 and Figure 5 As shown, the real-time risk management module includes a behavior pattern recognition submodule, a security level classification submodule, and an early warning adjustment submodule;

[0093] The behavior pattern recognition submodule identifies behavior patterns that deviate from standard operations based on behavior deviation indicators, classifies them according to the degree of deviation from the standard, determines the behaviors that deviate from standard operations, and obtains a list of behavior pattern recognition.

[0094] Feature extraction is performed on behaviors that deviate from standard operations. Based on the generated deviation data set, the specific deviation type and characteristic parameters of each behavior are identified. Key information in the deviation parameter set is extracted, including the time period, location features, nature and frequency of the behavior. The deviation parameters are classified according to their degree of deviation from the standard behavior template. The degree of deviation is divided into different classification levels. By sorting the classification levels, the behavioral pattern features corresponding to each type of deviation are determined, and a behavioral pattern recognition list containing specific classification levels is output.

[0095] The security level classification submodule is based on the behavior pattern recognition list. It classifies the monitored behavior patterns according to the severity of risk, assesses the potential threat of each behavior pattern to security, and divides the security level according to the severity of risk to obtain risk level distribution information.

[0096] Extract all identified behavioral pattern feature data, conduct risk assessment for each behavioral pattern according to classification level, and assess the degree of threat posed by each behavioral pattern in actual operation. Refer to historical data and risk level parameters defined in safety operation standards to classify behavioral patterns according to risk level. By statistically analyzing the distribution frequency of each behavioral pattern in different risk levels, and at the same time compiling and outputting the correlation information between each behavioral pattern and its risk level, risk level distribution information is obtained.

[0097] The monitored behavioral patterns are categorized according to risk severity, and the potential security threat posed by each behavioral pattern is assessed using the following formula:

[0098]

[0099] The safety levels are then classified according to the severity of the risk, resulting in risk level distribution information, where R i β represents the risk level of the i-th behavioral pattern. j The risk coefficient f(B) represents the j-th behavioral characteristic. j ) represents the risk function value of the j-th behavioral feature, and n represents the total number of behavioral features;

[0100] There are three behavioral characteristics, with corresponding risk coefficients β. j and the risk function value f(B) of behavioral characteristics j )as follows:

[0101] β1 = 0.5, f(B1) = 20;

[0102] β2 = 1.2, f(B2) = 15;

[0103] β3 = 0.8, f(B3) = 10;

[0104] β j The value is obtained from the relevant security analysis report, where f(B) j This is obtained through correlation analysis between behavioral patterns and known risk events, for example, f(B) j This can be calculated by statistically analyzing the frequency of security incidents caused by similar past behaviors. The calculation formula is as follows:

[0105] Calculate the product of each term and its square root:

[0106]

[0107]

[0108] Calculate R i Value:

[0109]

[0110] The results indicate that the average risk level of the i-th behavioral pattern is 3.41, reflecting the comprehensive risk level after considering all relevant behavioral characteristics. This indicates the risk level of the behavioral pattern and can be further used to identify potential threats and formulate preventive measures in the monitoring system.

[0111] The early warning adjustment submodule automatically adjusts the early warning threshold based on the risk level distribution information and dynamically adjusts the early warning response level according to the changing safety risk data to obtain risk level response information.

[0112] The system performs a tiered analysis of various risk levels. Based on the latest risk distribution table, it extracts the level parameters and frequency data of high-risk behavior patterns. By comparing the behavior data corresponding to different risk levels, it resets the threshold parameters for triggering early warnings and inputs the new thresholds into the early warning system for automatic updates. At the same time, based on the dynamically adjusted early warning thresholds, it sets different response strategy levels. By checking the actual operation results of the adjusted early warning response, it generates risk level response information that includes the dynamically adjusted early warning thresholds and corresponding response strategy levels.

[0113] like Figure 2 and Figure 6 As shown, the monitoring process optimization module includes an early warning information distribution submodule, a monitoring optimization submodule, and an early warning effect evaluation submodule;

[0114] The early warning information distribution submodule extracts the early warning content based on the risk level response information, prioritizes the transmission of high-risk level early warning notifications to the monitoring center, determines the sending status of the notification information, records the sending time and received feedback information, and obtains the notification transmission record.

[0115] High-risk warning notification data is extracted and categorized according to the severity priority of the warning content. The categorized notifications are then transmitted to the monitoring center. Simultaneously, the sending status of each notification is tracked, recording the specific sending time and the recipient's feedback information for each notification. When receiving feedback information, the confirmation status and response time are extracted, and a notification delivery record table is generated according to the transmission order. The record includes the risk level, transmission time, receipt confirmation status, and feedback time of each notification, ensuring that all warning notifications are delivered effectively.

[0116] The monitoring optimization submodule analyzes the response behavior data of on-site personnel based on notification transmission records, adjusts the direction and focal length range of monitoring cameras, monitors the dynamic changes of the on-site operation area in real time, corrects the location of monitoring blind spots, and generates monitoring adjustment details.

[0117] The system collects and analyzes on-site response data from the monitored area, extracts personnel behavior data recorded during the response process, adjusts the direction and focal length of the monitoring cameras based on the dynamic changes in the operating area, and ensures that key locations in the operating area are within the monitoring range by real-time correction of the camera angle and focal length. It also adjusts blind spots, analyzes areas missed in the monitoring screen, resets the optimal position and angle of the cameras, and records the adjusted monitoring parameters in the monitoring adjustment log, including specific camera direction, focal length, and angle correction time.

[0118] The early warning effect evaluation submodule, based on monitoring and adjustment details, filters the received early warning feedback data, statistically analyzes the response time and coverage of different early warning levels, compares it with the processing status of early warning information and standard processing requirements, evaluates the early warning response effect, and obtains the early warning effect evaluation result.

[0119] The received early warning feedback data is sorted and filtered to extract response time data for different early warning levels. The coverage of each early warning level is statistically analyzed, and the actual processing of early warning information is compared with the standard processing requirements. By analyzing the differentiated response time and coverage, the processing efficiency of each early warning level is statistically analyzed. The overall early warning response effect is evaluated based on the quality of processing efficiency. The analysis results are recorded in the early warning effect evaluation table, including response time, coverage, processing efficiency and their comparison results.

[0120] like Figure 2 and Figure 7As shown, the behavior pattern update module includes a behavior analysis submodule, a behavior comparison submodule, and a database update submodule;

[0121] Based on the early warning effect evaluation results, the behavior analysis submodule performs real-time analysis on the behavior data captured in the video stream, extracts key behavior features, separates and marks key behavior changes, and obtains behavior feature extraction results.

[0122] A continuous frame sequence is extracted from the video stream. Keyframes containing significant behavioral changes are identified through inter-frame difference analysis and marked. During the marking process, behavioral feature data within the frames is extracted. The feature data includes the time point, direction, amplitude, and spatial range of the behavior. The data is separated into different subcategories, and each subcategory independently records specific behavioral features and change information. Subsequently, the key behavioral changes are further filtered to remove behavioral categories that do not meet the feature change conditions. A feature extraction result table is generated from the classified behavioral feature data. The result table includes the classification, time point, and action range of the key behaviors.

[0123] The behavior comparison submodule compares the current behavior data with the behavior patterns stored in the database based on the behavior feature extraction results, identifies new or changing behavior trends, and obtains behavior trend comparison information.

[0124] The extracted current behavioral features are compared with the behavioral pattern data stored in the database. Based on the classification and time point of each behavioral feature, behavioral templates are matched step by step. Feature parameters similar to the current behavior in the database are extracted, including action type, amplitude range and time distribution pattern. The degree of matching between the current behavior and the database template is divided into different matching levels. For behavioral features with low matching degree or no matching, the new behavioral trends they represent are further analyzed. By comparing their frequency, change amplitude and spatial distribution, the unmatched behaviors are classified and marked, and the information on the change trend of behavior is sorted out.

[0125] The database update submodule incorporates newly mined behavioral trends and key data points into the database based on behavioral trend comparison information, and optimizes the data structure to obtain an updated behavioral database.

[0126] The identified new behavioral trends and corresponding key data points are entered into the database. During the entry process, the new behavioral trends are numbered and classified. The behavioral trend data is inserted into the existing database structure in chronological order. The database is then optimized based on the structural characteristics of the new data. The optimization includes adjusting the storage method of the data index, adding index tags to frequently accessed data, and cleaning up duplicate or invalid data. After optimization, all newly added behavioral trends and key data are re-retrieved and verified to ensure data consistency and integrity, ultimately resulting in an updated behavioral database.

[0127] like Figure 2 and Figure 8 As shown, the emergency response correction module includes a behavior guidance submodule and an incident management submodule;

[0128] The behavior guidance submodule provides behavioral guidance to operators based on the updated behavior database, analyzes unsafe behavior patterns, and refines operators' understanding of risky behaviors through simulation training and behavior correction, thereby obtaining behavior correction guidance data.

[0129] First, a dataset of unsafe behavior patterns is extracted from the database. The classification information of unsafe behaviors and their corresponding key feature parameters are analyzed, including behavioral deviations, action frequency, and spatial location range in the operation steps. The parameters are used to construct a simulation training task. The feature data of unsafe behaviors are input into the simulation training module to generate corresponding scenarios. In the simulation scenarios, operators are guided to identify risky behaviors. The feedback time and behavior adjustment process of operators to risky behaviors are recorded. At the same time, each operation step is decomposed. By recording and analyzing the response characteristics of personnel when correcting behaviors, including operation accuracy and adjustment frequency, all simulation training results and correction process data are finally integrated to form complete behavior correction guidance data.

[0130] Based on behavior correction guidance data, the incident management submodule formulates emergency shutdown and intervention measures for high-risk behaviors, adjusts operating procedures and matches potential security threats, optimizes the probability of incident occurrence and the effectiveness of intervention, and obtains incident prevention and control management logs.

[0131] Extract classification information involving high-risk behaviors, and formulate emergency shutdown plans for each type of high-risk behavior based on the classification information. The emergency shutdown plan includes the setting of shutdown condition parameters, triggering steps, and recovery procedures after shutdown. Match shutdown parameters with actual operation procedures, identify steps in the operation procedures that are associated with high-risk behaviors, match potential security threats with the procedures, adjust the operation procedures to reduce the probability of triggering high-risk behaviors, analyze the operational efficiency of the adjusted procedures, generate intervention effectiveness evaluation records based on the adjustment results, and integrate all emergency shutdown plans and process optimization data into the incident management log to form an incident prevention and control management log.

[0132] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0133] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0134] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0135] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0136] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0137] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0140] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0141] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A video surveillance-based intelligent management system for chemical production safety, characterized in that, The system includes: The video stream optimization module performs image resolution and frame rate adjustments based on the real-time video stream of chemical production captured by the camera, eliminates the effects of background noise and lighting changes, decodes and compresses the signal, and then corrects color distortion and motion blur to obtain optimized video stream quality. The behavior analysis and evaluation module tracks behavior changes between keyframes based on the optimized video stream quality, identifies deviations from standard operations, and evaluates the standardization of operational behavior based on the deviations to obtain a behavior deviation index. The behavior analysis and evaluation module includes a behavior tracking submodule, a deviation analysis submodule, and a normative evaluation submodule; The behavior tracking submodule monitors the behavior in the video frame sequence based on the optimized video stream quality, captures behavior changes between key frames, marks each key frame with a timestamp, associates behavior events with operation areas, refines the correspondence between time and space data, and obtains behavior tracking mapping data. The deviation analysis submodule compares the operator's behavior with the standard behavior frame by frame based on the behavior tracking mapping data, identifies the behavior deviation, determines the degree of deviation, and counts the frequency and time distribution of the deviation behavior to obtain the behavior deviation metric. The normative assessment submodule performs a behavior compliance check based on the behavior deviation metric, compares it with the safety operation standard, assesses the compliance range of the behavior deviation, quantifies the potential impact of the deviation on the safety baseline, and obtains the behavior deviation index. The process of comparing operator behavior with standard behavior frame by frame to identify behavioral deviations and determine the degree of deviation is performed using the following formula: ； Calculate behavioral deviation measure By statistically analyzing the frequency and temporal distribution of deviant behaviors, a behavioral deviation metric is obtained. Represents the key weights of the k-th frame. This represents the operator behavior data in the k-th frame. Represents the standard behavioral data in the k-th frame. Total number of frames; Based on the behavioral deviation indicators, the real-time risk management module identifies behavioral patterns that deviate from standard operating procedures, automatically classifies safety levels, calculates and predicts the probability of occurrence of multiple levels of risks, and automatically adjusts the warning threshold to obtain risk level response information. Based on the risk level response information, the monitoring process optimization module distributes the early warning notification to the monitoring center and the on-site operation area, monitors the personnel status in the on-site operation area, adjusts the monitoring parameters according to the personnel feedback information, evaluates the early warning response effect, and obtains the early warning effect evaluation result. Based on the early warning effect evaluation results, the behavior pattern update module continuously monitors the behavior pattern characteristics in the video stream, compares them with historical behavior data, identifies new behavior trends or behavior changes, and obtains an updated behavior database. Based on the updated behavior database, the emergency response correction module provides behavioral correction guidance to operators, formulates emergency shutdown and intervention measures for monitored risky behaviors, and obtains accident prevention and control management logs.

2. The intelligent management system for chemical production safety based on video surveillance according to claim 1, characterized in that, The optimized video stream quality includes signal integration, dynamic range, and frame integration; the behavioral deviation indicators include deviation intensity, consistency index, and behavioral deviation frequency; the risk level response information includes risk discrimination threshold, response level classification, and warning signal strength; the warning effect evaluation results include response effectiveness, reaction adjustment time, and monitoring efficiency; and the updated behavioral database specifically includes behavioral pattern recognition results, pattern change records, and behavioral sequence analysis results.

3. The intelligent management system for chemical production safety based on video surveillance according to claim 1, characterized in that, The video stream optimization module includes an image quality adjustment submodule and an image stabilization submodule; The image quality adjustment submodule adjusts the resolution and frame rate of the image based on the real-time video stream of chemical production captured by the camera, matches the environmental requirements of different monitoring, and optimizes the impact of background noise and lighting changes to obtain video quality optimized recordings. The signal processing submodule decodes and compresses the video quality optimization recording, adjusts the data transmission parameters and storage requirements, corrects color distortion and motion blur, optimizes the clarity of the monitoring image, and obtains optimized video stream quality.

4. The intelligent management system for chemical production safety based on video surveillance according to claim 1, characterized in that, The real-time risk management module includes a behavior pattern recognition submodule, a security level classification submodule, and an early warning adjustment submodule. The behavior pattern recognition submodule identifies behavior patterns that deviate from standard operations based on the behavior deviation index, classifies them according to the degree of deviation between the behavior and the standard, determines the behaviors that deviate from standard operations, and obtains a behavior pattern recognition list. The security level classification submodule classifies the monitored behavior patterns according to the severity of risk based on the behavior pattern recognition list, assesses the potential threat of each behavior pattern to security, and classifies the security level according to the severity of risk to obtain risk level distribution information. The early warning adjustment submodule automatically adjusts the early warning threshold based on the risk level distribution information and dynamically adjusts the early warning response level according to the changing safety risk data to obtain risk level response information.

5. The intelligent management system for chemical production safety based on video surveillance according to claim 4, characterized in that, The monitored behavioral patterns are categorized according to risk severity, and the potential threat to security posed by each behavioral pattern is assessed using the following formula: ； The safety levels are then classified according to the severity of the risks, resulting in risk level distribution information. Represents the risk level of the i-th behavioral pattern. The risk coefficient represents the j-th behavioral characteristic. The risk function value represents the j-th behavioral characteristic. Represents the total number of behavioral characteristics.

6. The intelligent management system for chemical production safety based on video surveillance according to claim 1, characterized in that, The monitoring process optimization module includes an early warning information distribution submodule, a monitoring optimization submodule, and an early warning effect evaluation submodule. The early warning information distribution submodule extracts the early warning content based on the risk level response information, prioritizes the transmission of high-risk level early warning notifications to the monitoring center, determines the sending status of the notification information, records the sending time and received feedback information, and obtains the notification transmission record. The monitoring optimization submodule analyzes the response behavior data of on-site personnel based on the notification transmission record, adjusts the direction and focal length range of the monitoring camera, monitors the dynamic changes of the on-site operation area in real time, corrects the monitoring blind spot position, and generates monitoring adjustment details. The early warning effect evaluation submodule, based on the monitoring adjustment details, filters the received early warning feedback data, statistically analyzes the response time and coverage of different early warning levels, compares it with the processing status of early warning information and standard processing requirements, evaluates the early warning response effect, and obtains the early warning effect evaluation result.

7. The intelligent management system for chemical production safety based on video surveillance according to claim 1, characterized in that, The behavior pattern update module includes a behavior analysis submodule, a behavior comparison submodule, and a database update submodule; Based on the early warning effect evaluation results, the behavior analysis submodule performs real-time analysis on the behavior data captured in the video stream, extracts key behavior features, separates and marks key behavior changes, and obtains behavior feature extraction results. Based on the extracted behavioral features, the behavior comparison submodule compares the current behavioral data with the behavioral patterns stored in the database to identify new or changing behavioral trends and obtain behavioral trend comparison information. The database update submodule incorporates newly mined behavioral trends and key data points into the database based on the behavioral trend comparison information, and optimizes the data structure to obtain an updated behavioral database.

8. The intelligent management system for chemical production safety based on video surveillance according to claim 1, characterized in that, The emergency response correction module includes a behavior guidance submodule and an accident management submodule; The behavior guidance submodule provides behavioral guidance to operators based on the updated behavior database, analyzes unsafe behavior patterns, and refines operators' understanding of risky behaviors through simulation training and behavior correction to obtain behavior correction guidance data. Based on the behavior correction guidance data, the incident management submodule formulates emergency shutdown and intervention measures for high-risk behaviors, adjusts operating procedures and matches potential security threats, optimizes the probability of incident occurrence and the effectiveness of intervention, and obtains incident prevention and control management logs.

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