Group safety prevention and control management system, method and computer program

Abstract

The invention discloses a group safety prevention and control management system and method and a computer program, and the system comprises a data collection module which is used for collecting the related data of a wharf, including operation information and monitoring data; the data management module is used for managing the data collected by the data collection module; the early warning analysis module is used for performing mining and analysis by comprehensively utilizing a preset rule engine and the trained multi-task neural network model to generate early warning information; and the visualization module is used for displaying the data managed by the data management module and the early warning information generated by the early warning analysis module to a user by utilizing a visualization technology. According to the technical scheme provided by the invention, key data, early warning information and the like of each wharf are displayed to group management personnel in a visual and understandable form, so that the efficiency and the effect of safety management in the group are improved; the safety condition in the group can be monitored in real time, the potential safety risk can be pre-warned and analyzed, and a response can be quickly made when a safety accident occurs.

Description

Technical Field

[0001] The present invention relates to the field of digital intelligent management, and in particular to a group security prevention and control management system, method and computer program. Background Art

[0002] As the port enterprise group continues to expand in size and become increasingly powerful, internal production and management safety issues have gradually emerged and attracted widespread attention. Traditional safety prevention and control measures can no longer effectively cope with the security challenges currently faced by the group enterprises. Therefore, there is an urgent need to seek more advanced and comprehensive security solutions.

[0003] Therefore, it is of great practical necessity and far-reaching application value to develop a group security control management system that can integrate multiple security control technologies and means to provide all-round and multi-level security protection within the port enterprise group. Summary of the invention

[0004] The present invention provides a group security prevention and control management system, which can provide all-round and multi-level security protection for the group by integrating multiple security prevention and control technologies and means and applying visualization technology.

[0005] To achieve the above object, the embodiments of the present invention adopt the following technical solutions: A group safety prevention and control management system, comprising: Data collection module, used to collect terminal-related data, including operation information and monitoring data; A data management module is used to manage the data collected by the data collection module; The early warning analysis module is used to comprehensively utilize the preset rule engine and the trained multi-task neural network model to mine and analyze the data collected by the data collection module, discover potential security risks, and generate early warning information; A visualization module is used to display the data managed by the data management module and the warning information generated by the warning analysis module to the user using visualization technology; Wherein, the rule engine includes: Set early warning rules according to the group's security prevention and control management needs; Traverse the data collected by the data collection module and match it with the warning rules one by one. Once a suspicious problem that matches the rules is found, the rule engine will trigger the corresponding warning mechanism; Wherein, the multi-task neural network model includes: Construct branch networks for the security risk classification task and the security risk score prediction task respectively; The branch network adopts a multi-task loss function to integrate the weighted losses of the security risk classification task and the security risk score prediction task; Among them, the preset rule engine and the trained multi-task neural network model are used to mine and analyze the data collected by the data collection module, including: Based on the accuracy of historical predictions by the rule engine and the multi-task neural network model, the prediction results of the two are weighted and summed to obtain the final security risk prediction score. When the score exceeds a certain threshold, an early warning is issued.

[0006] According to one aspect of the present invention, the data collection module comprises: Data synchronization module, used to synchronize data and operation information of various terminals; The security monitoring module is used to obtain real-time monitoring data of the terminal through monitoring cameras and sensor devices.

[0007] According to one aspect of the present invention, the terminal-related data also includes: the number of ships scheduled to enter the port, berthed and scheduled to leave the port, and the number of online fire trucks and firefighters at each fire station.

[0008] According to one aspect of the present invention, the management of the data collected by the data collection module includes: aggregating, storing, managing and backing up the data collected by the data collection module.

[0009] According to one aspect of the present invention, the data management module further includes: querying the data collected by the data collection module.

[0010] According to one aspect of the present invention, the early warning analysis module supports multi-data source processing and analysis.

[0011] According to one aspect of the present invention, the multiple data sources include: group big data, sensor collection data, weather information capture data, and terminal safety data.

[0012] According to one aspect of the present invention, the visualization module comprises: The operation statistics subpage is used to display terminal-related data, including operation information; Warning subpage, used to display warning information; The security control layout subpage is used to display security control layout information; The prevention and control subpage is used to display the prevention and control work information; The prevention and investigation subpage is used to display the prevention and investigation management work information, including but not limited to hidden danger elimination, hidden danger management, and accident reporting information; The operation inspection work subpage is used to display the progress information of special operation inspection work at the terminal; Map subpage, used to display the dock map; The current cargo subpage is used to display the current amount of dangerous goods on site and the amount of dangerous goods entering and leaving within 24 hours; Inspection status subpage; used to display inspection status information, including but not limited to items to be rectified, abnormal items and inspection items.

[0013] A group security prevention and control management method, comprising: Collect terminal-related data, including operation information and monitoring data; Manage terminal-related data; Comprehensively utilize the preset rule engine and the trained multi-task neural network model to mine and analyze the relevant data of the terminal, discover potential safety risks, and generate early warning information; The terminal-related data and warning information are displayed to users using visualization technology.

[0014] A computer program which, when executed, implements the group security prevention and control management system as described above.

[0015] Advantages of the present invention: The present invention provides a group safety prevention and control management system, which can provide all-round and multi-level security protection for the group by integrating multiple safety prevention and control technologies and means and applying visualization technology; through visualization technology, key data and early warning information of each terminal can be displayed to group managers in an intuitive and easy-to-understand form, thereby improving the efficiency and effectiveness of safety management within the group; it can monitor the safety status within the group in real time, conduct early warning analysis of potential safety risks, and respond quickly when a safety accident occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is a structural diagram of a group security control management system according to the first embodiment of the present invention; Figure 2 This is a structural diagram of a group security control management system according to the second embodiment of the present invention; Figure 3 This is a visualization page diagram of a group security prevention and control management system described in Example 2 of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1

[0020] like Figure 1 As shown, a group safety prevention and control management system includes: a data collection module 1, a data management module 2, an early warning analysis module 3, and a visualization module 4.

[0021] Data collection module 1: used to collect terminal-related data, including operation information, monitoring data, etc.

[0022] Operation information includes: planned throughput, operating personnel, operating equipment, hidden danger inspection, violations, etc.

[0023] In this embodiment, the terminal-related data includes: operation information, the number of ships scheduled to enter, berthed, and scheduled to leave the port, the number of online fire trucks and firefighters at each fire station, real-time monitoring data of the terminal obtained through monitoring cameras and sensor equipment, and other multi-dimensional content.

[0024] Afterwards, the collected data on various aspects of the terminal are summarized, counted, processed and analyzed.

[0025] Data management module 2: used to manage the data collected by the data collection module.

[0026] The management of the data collected by the data collection module includes: summarizing, storing, managing and backing up the data collected by the data collection module.

[0027] Ensure data security and reliability by storing, managing and backing up data.

[0028] Early warning analysis module 3: It is used to comprehensively utilize the preset rule engine and the trained multi-task neural network model to mine and analyze the data collected by the data collection module, discover potential security risks, and generate early warning information.

[0029] The rule engine comprises: Set early warning rules according to the group's security prevention and control management needs; The data collected by the data collection module is traversed and matched with the warning rules one by one. Once a suspicious problem that matches the rules is found, the rule engine will trigger the corresponding warning mechanism.

[0030] The multi-task neural network model includes: Construct branch networks for the security risk classification task and the security risk score prediction task respectively; The branch network adopts a multi-task loss function to integrate the weighted losses of the security risk classification task and the security risk score prediction task.

[0031] Among them, the branch network for the security risk classification task adopts the classification task decoder; the branch network for the security risk score prediction task adopts the risk score estimation decoder.

[0032] Comprehensively utilize the preset rule engine and the trained multi-task neural network model to mine and analyze the data collected by the data collection module, including: Based on the accuracy of historical predictions by the rule engine and the multi-task neural network model, the prediction results of the two are weighted and summed to obtain the final security risk prediction score. When the score exceeds a certain threshold, an early warning is issued.

[0033] The early warning analysis module uses data cleaning, data mining and data analysis technologies to conduct in-depth mining and analysis of the monitoring data transmitted by each terminal, conduct in-depth analysis of large amounts of data, extract data with security risks, and generate early warning messages.

[0034] The early warning analysis module supports multi-data source processing and analysis.

[0035] The multiple data sources include: group big data, sensor collection data, weather information capture data, and terminal safety data.

[0036] Visualization module 4: used to display the data managed by the data management module and the warning information generated by the warning analysis module to the user using visualization technology.

[0037] The visualization module 4 includes: a job statistics sub-page 41 and an early warning sub-page 42 .

[0038] Operation statistics subpage 41: used to display terminal related data, including operation information, etc.

[0039] In this embodiment, the information displayed on the operation statistics subpage 41 is the port operation statistics, including the planned throughput, operating personnel, operating equipment, today's hazardous goods container yard occupancy rate, today's key cargo business volume proportion, today's planned hazardous goods in and out volume, today's planned key cargo loading and unloading volume, planned incoming ships, berthed ships, planned outgoing ships, standby firefighting units, standby firefighting personnel, standby firefighting vehicles, etc.

[0040] Warning subpage 42: used to display warning information.

[0041] In this embodiment, the warning information displayed on the warning subpage 42 includes the dangerous goods vessel information of the port company, the dangerous goods vessel operation information, etc.

[0042] The visualization module, combined with the data aggregation and organization of the data management module, uses advanced visualization technology to present the key data summarized by each terminal to users in an intuitive and easy-to-understand form, thereby improving the readability and comprehensibility of the information.

[0043] The beneficial effects of this embodiment are: the system uses visualization technology to display the key data of each terminal to the group management personnel in an intuitive and easy-to-understand form, thereby improving the efficiency and effectiveness of internal safety management of the group; it can monitor the safety status within the group in real time, conduct early warning analysis of potential safety risks, and respond quickly when a safety incident occurs.

[0044] Embodiment 2

[0045] like Figure 2 As shown, a group safety prevention and control management system includes: a data collection module 1, a data management module 2, an early warning analysis module 3, and a visualization module 4.

[0046] Data collection module 1: used to collect terminal-related data, including operation information, monitoring data, etc.

[0047] The data collection module includes: a data synchronization module 11 and a security monitoring module 12 .

[0048] Data synchronization module 11: used to synchronize the data and operation information of each terminal. The data synchronization module 11 can pull the data summarized by each terminal through the interface.

[0049] Security monitoring module 12: used to obtain real-time monitoring data of the terminal through monitoring cameras and sensor equipment. By installing monitoring cameras, sensors and other equipment, the terminal is monitored in real time, and the monitoring data is transmitted to the data management module 2 for processing and analysis.

[0050] Operation information includes: planned throughput, operating personnel, operating equipment, hidden danger inspection, and violations.

[0051] In this embodiment, the terminal-related data includes: operation information, the number of ships scheduled to enter, berthed, and scheduled to leave the port, the number of online fire trucks and firefighters at each fire station, real-time monitoring data of the terminal obtained through monitoring cameras and sensor equipment, and other multi-dimensional content.

[0052] Afterwards, the collected data on various aspects of the terminal are summarized, counted, processed and analyzed.

[0053] Data management module 2: used to manage the data collected by the data collection module.

[0054] The management of the data collected by the data collection module includes: summarizing, storing, managing and backing up the data collected by the data collection module.

[0055] Ensure data security and reliability by storing, managing and backing up data.

[0056] The data management module also includes: querying the data collected by the data collection module. The data management module also provides a data query function, which is convenient for users to understand the safety status of each terminal at any time.

[0057] Early warning analysis module 3: It is used to comprehensively utilize the preset rule engine and the trained multi-task neural network model to mine and analyze the data collected by the data collection module, discover potential security risks, and generate early warning information.

[0058] The rule engine comprises: Set early warning rules according to the group's security prevention and control management needs; The data collected by the data collection module is traversed and matched with the warning rules one by one. Once a suspicious problem that matches the rules is found, the rule engine will trigger the corresponding warning mechanism.

[0059] The multi-task neural network model includes: Construct branch networks for the security risk classification task and the security risk score prediction task respectively; The branch network adopts a multi-task loss function to integrate the weighted losses of the security risk classification task and the security risk score prediction task.

[0060] Among them, the branch network for the security risk classification task adopts the classification task decoder; the branch network for the security risk score prediction task adopts the risk score estimation decoder.

[0061] Comprehensively utilize the preset rule engine and the trained multi-task neural network model to mine and analyze the data collected by the data collection module, including: Based on the accuracy of historical predictions by the rule engine and the multi-task neural network model, the prediction results of the two are weighted and summed to obtain the final security risk prediction score. When the score exceeds a certain threshold, an early warning is issued.

[0062] The early warning analysis module uses data cleaning, data mining and data analysis technologies to conduct in-depth mining and analysis of the monitoring data transmitted by each terminal, conduct in-depth analysis of large amounts of data, extract data with security risks, and generate early warning messages.

[0063] The early warning analysis module supports multi-data source processing and analysis.

[0064] The multiple data sources include: group big data, sensor collection data, weather information capture data, and terminal safety data.

[0065] Visualization module 4: used to display the data managed by the data management module and the warning information generated by the warning analysis module to the user using visualization technology.

[0066] The visualization module 4 includes: an operation statistics subpage 41, an early warning subpage 42, a safety control arrangement subpage 43, a prevention and control subpage 44, a prevention and investigation subpage 45, an operation inspection work subpage 46, a map subpage 47, a current cargo subpage 48, and an inspection situation subpage 49.

[0067] Job statistics subpage 41: used to display terminal related data, package job information, etc.

[0068] In this embodiment, the information displayed on the operation statistics subpage 41 is the port operation statistics, including the planned throughput, operating personnel, operating equipment, today's hazardous goods container yard occupancy rate, today's key cargo business volume proportion, today's planned hazardous goods in and out volume, today's planned key cargo loading and unloading volume, planned incoming ships, berthed ships, planned outgoing ships, standby firefighting units, standby firefighting personnel, standby firefighting vehicles, etc.

[0069] Warning subpage 42: used to display warning information.

[0070] In this embodiment, the warning information displayed on the warning subpage 42 includes the dangerous goods vessel information of the port company, the dangerous goods vessel operation information, etc.

[0071] The security control arrangement subpage 43 is used to display security control arrangement information.

[0072] The prevention and control subpage 44 is used to display the prevention and control graded work information.

[0073] In this embodiment, the prevention and control subpage 44 displays the prevention and control hierarchical management work information including: identified risk sources, average daily inspection coverage, number of first-level, second-level, and third-level risks, etc.

[0074] The prevention and investigation subpage 45 is used to display the prevention and investigation management work information, including but not limited to hidden danger elimination, hidden danger management, and accident reporting information.

[0075] In this embodiment, the prevention and investigation sub-page 45 displays the prevention and investigation management work information including hidden danger elimination, hidden danger management, the number of accident reports, statistics, etc.

[0076] The operation inspection work subpage 46 is used to display the progress information of the special operation inspection work at the terminal.

[0077] In this embodiment, the progress information of the special operation inspection work at the terminal includes the status of pending review, pending execution, completed, detected abnormalities, etc. of hot work, high-altitude work, hot work on board, etc.

[0078] The map subpage 47 is used to display the wharf map. The wharf map can be displayed using Amap, Baidu Map, etc.

[0079] The current cargo subpage 48 is used to display the current amount of dangerous cargo on site and the amount of dangerous cargo entering and leaving within 24 hours.

[0080] Inspection status subpage 49; used to display inspection status information, including but not limited to items to be rectified, abnormal items and inspection items.

[0081] The visualization module, combined with the data aggregation and organization of the data management module, uses advanced visualization technology to present the key data summarized by each terminal to users in an intuitive and easy-to-understand form, thereby improving the readability and comprehensibility of the information.

[0082] This system comprehensively summarizes and displays data information of each terminal of the group, including multi-dimensional contents such as operating personnel, operating equipment, planned throughput, hidden danger inspection and violation. Through intuitive data display, key data of dangerous goods and key goods of the day can be clearly understood. In addition, this system also counts the number of ships planned to enter, berthed and planned to leave the port, and summarizes the number of online fire trucks and firefighters of each fire station.

[0083] At the same time, the visualization module also displays key information such as early warning information, safety prevention and control deployment measures, and special operation inspections of each terminal, so as to fully understand the safety status of each terminal. In order to more intuitively display the number of hidden dangers, violations and accidents that occurred that year, the visualization module uses a line graph to present them. In addition, users can also switch to different terminals according to their needs to view detailed information on the corresponding number of dangerous goods entering and leaving and the inspection status.

[0084] The visual page display effect of this system is as follows Figure 3 shown.

[0085] The beneficial effects of this embodiment are as follows: the system obtains data information of various aspects of the terminal through the data synchronization module and the security monitoring module, and uses visualization technology to display the key data and early warning information of each terminal to the group managers in an intuitive and easy-to-understand form, thereby improving the efficiency and effectiveness of the group's internal security management and control.

[0086] Embodiment 3

[0087] A group security prevention and control management method, comprising: Collect terminal-related data, including but not limited to operation information and monitoring data; Manage terminal-related data; Using the preset rule engine and the trained multi-task neural network model, the terminal-related data is mined and analyzed to discover potential safety risks and generate early warning information; The terminal-related data and warning information are displayed to users using visualization technology.

[0088] Embodiment 4

[0089] A computer program, when executed, implements the group security prevention and control management system as described in Example 1 or Example 2.

[0090] Embodiment 5

[0091] A readable storage medium stores a computer program as described in Example 4, and when the computer program is executed, the group security prevention and control management system as described in Example 1 or Example 2 is implemented.

[0092] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the art within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A group security prevention and control management system, characterized in that: include: Data collection module, used to collect terminal-related data, including operation information and monitoring data; A data management module is used to manage the data collected by the data collection module; The early warning analysis module is used to comprehensively utilize the preset rule engine and the trained multi-task neural network model to mine and analyze the data collected by the data collection module, discover potential security risks, and generate early warning information; A visualization module is used to display the data managed by the data management module and the warning information generated by the warning analysis module to the user using visualization technology; Wherein, the rule engine includes: Set early warning rules according to the group's security prevention and control management needs; Traverse the data collected by the data collection module and match it with the warning rules one by one. Once a suspicious problem that matches the rules is found, the rule engine will trigger the corresponding warning mechanism; Wherein, the multi-task neural network model includes: Construct branch networks for the security risk classification task and the security risk score prediction task respectively; The branch network adopts a multi-task loss function to integrate the weighted losses of the security risk classification task and the security risk score prediction task; Among them, the preset rule engine and the trained multi-task neural network model are used to mine and analyze the data collected by the data collection module, including: Based on the accuracy of historical predictions by the rule engine and the multi-task neural network model, the prediction results of the two are weighted and summed to obtain the final security risk prediction score. When the score exceeds a certain threshold, an early warning is issued.

2. The group security control management system according to claim 1 is characterized in that: The data collection module includes: Data synchronization module, used to synchronize data and operation information of various terminals; The security monitoring module is used to obtain real-time monitoring data of the terminal through monitoring cameras and sensor devices.

3. The group security control management system according to claim 1 is characterized in that: The terminal-related data also includes: the number of ships scheduled to enter the port, berthed, and scheduled to leave the port, and the number of online fire trucks and firefighters at each fire station.

4. The group security control management system according to claim 1 is characterized in that: The management of the data collected by the data collection module includes: summarizing, storing, managing and backing up the data collected by the data collection module.

5. The group security control management system according to claim 1 is characterized in that: The data management module also includes: querying the data collected by the data collection module.

6. The group security control management system according to claim 1 is characterized in that: The early warning analysis module supports multi-data source processing and analysis.

7. The group security control management system according to claim 6 is characterized in that: The multiple data sources include: group big data, sensor collection data, weather information capture data, and terminal safety data.

8. The group security control management system according to claim 1 is characterized in that: The visualization module comprises: The operation statistics subpage is used to display terminal-related data, including operation information; Warning subpage, used to display warning information; The security control layout subpage is used to display security control layout information; The prevention and control subpage is used to display the prevention and control work information; The prevention and investigation subpage is used to display the prevention and investigation management work information, including but not limited to hidden danger elimination, hidden danger management, and accident reporting information; The operation inspection work subpage is used to display the progress information of special operation inspection work at the terminal; Map subpage, used to display the dock map; The current cargo subpage is used to display the current amount of dangerous goods on site and the amount of dangerous goods entering and leaving within 24 hours; Inspection status subpage; used to display inspection status information, including but not limited to items to be rectified, abnormal items and inspection items.

9. A group security prevention and control management method, characterized in that: include: Collect terminal-related data, including operation information and monitoring data; Manage terminal-related data; Comprehensively utilize the preset rule engine and the trained multi-task neural network model to mine and analyze the relevant data of the terminal, discover potential safety risks, and generate early warning information; Display terminal-related data and warning information to users using visualization technology; Wherein, the rule engine includes: Set early warning rules according to the group's security prevention and control management needs; Traverse the data collected by the data collection module and match it with the warning rules one by one. Once a suspicious problem that matches the rules is found, the rule engine will trigger the corresponding warning mechanism; Wherein, the multi-task neural network model includes: Construct branch networks for the security risk classification task and the security risk score prediction task respectively; The branch network adopts a multi-task loss function to integrate the weighted losses of the security risk classification task and the security risk score prediction task; Among them, the preset rule engine and the trained multi-task neural network model are used to mine and analyze the data collected by the data collection module, including: Based on the accuracy of historical predictions by the rule engine and the multi-task neural network model, the prediction results of the two are weighted and summed to obtain the final security risk prediction score. When the score exceeds a certain threshold, an early warning is issued.

10. A computer program, characterized in that When the computer program is executed, the group security prevention and control management system as described in any one of claims 1 to 8 is implemented.

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