Safety emergency processing data intelligent evaluation management system
By designing an intelligent evaluation and management system for security emergency processing data, the problem of poor results in collaborative sharing and regulatory analysis of security emergency processing data in the existing solutions is solved, and intelligent data matching and active prevention reminders between security emergency processing teams are achieved, which improves the effectiveness of data processing and regulatory analysis.
Patent Information
- Application Number
- CN202411600327.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-11
- Publication Date
- 2025-02-28
AI Technical Summary
The existing safety emergency response data evaluation and management plan is not effective in collaborative sharing and regulatory analysis, making it difficult for different safety emergency response teams to effectively share and process different types of safety emergency accident data.
An intelligent evaluation and management system for safety emergency processing data was designed, including a shared prompt module for safety emergency processing matching, a shared evaluation prompt module for local security emergency processing and an shared evaluation and management module for overall security emergency processing. The system synchronously pushes collaboratively shared data through matching and filtering of collaborative shared data, and supervises the processing process and actively prevents impact analysis.
It realizes intelligent matching of collaborative shared data between security emergency treatment teams and proactive prevention reminders, improves the independent collaborative shared processing and utilization effect of security emergency treatment data, and improves the effectiveness of autonomous supervision analysis and optimization management through multi-dimensional data processing and analysis optimization management.
Smart Images
Figure CN119539531A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data management, and in particular to a security emergency processing data intelligent evaluation and management system. Background Art
[0002] Safety emergency response refers to a series of emergency measures and actions taken to quickly and effectively control the development of events, reduce losses, and protect the lives and property of people when an emergency occurs. It covers four main stages: prevention, preparation, response, and recovery.
[0003] When the existing safety emergency response data assessment and management scheme is implemented, different safety emergency response teams are handling different types of safety emergency accidents. Since the safety emergency response hazard data between different safety emergency response teams cannot be collaboratively shared and targeted in response prompts in a timely and efficient manner, safety emergency response teams with different handling experiences cannot take preventive measures in advance for different types of safety emergency accidents. At the same time, they are also unable to conduct reliability supervision and analysis of the existing safety emergency response hazard data of different safety types in different dimensions and actively optimize management, resulting in poor utilization of autonomous collaborative sharing of safety emergency response data, as well as poor autonomous supervision, analysis and optimization management. Summary of the invention
[0004] The purpose of the present invention is to provide an intelligent assessment and management system for safety emergency processing data, which is used to solve the technical problems of poor autonomous collaborative sharing processing and utilization of safety emergency processing data in existing solutions, as well as poor autonomous supervision, analysis and optimization management.
[0005] The purpose of the present invention can be achieved through the following technical solutions:
[0006] A security emergency processing data intelligent evaluation and management system, comprising:
[0007] The safety emergency processing matching sharing prompt module is used to obtain the pending data corresponding to different safety emergency incidents to be processed by different safety emergency processing teams, and to match and screen the pending data for collaborative sharing data, obtain the collaborative sharing data corresponding to the safety emergency incident to which the pending data belongs, and synchronously push the collaborative sharing data to the safety emergency processing team that handles the safety emergency incident;
[0008] The local safety emergency response sharing assessment prompt module is used to supervise the entire process of the safety emergency response team that performs collaborative sharing data synchronous push prompts to handle safety emergency accidents, and to analyze the active prevention impact of the supervision data, obtain the active prevention impact status of the safety emergency team in handling safety emergency accidents and provide prompts;
[0009] The overall safety emergency response shared evaluation management module is used to analyze the active prevention implementation effects in different dimensions of the active prevention impact supervision data of safety emergency accidents handled by all safety emergency response teams that implement collaborative shared data synchronous push notifications, and dynamically optimize the active prevention implementation shared data related to different accident causes based on the analysis results of different dimensions.
[0010] Further, obtaining the pending data corresponding to the pending security emergency incident and the target security emergency response team to which it belongs;
[0011] When matching and screening the collaborative shared data of the target safety emergency response team for the data to be processed, the accident type and cause of the safety emergency accident in the data to be processed are obtained, the accident type and cause of the accident are analyzed through the accident processing identification function, and the accident processing experience value corresponding to the safety emergency accident to be processed by the target safety emergency response team is output;
[0012] Incident handling experience contains a value of 0, 1, or 2.
[0013] Furthermore, the expression of the accident handling identification function is: Where LS and LY are the accident types and accident causes corresponding to the safety emergency accidents in the data to be processed, respectively; U1 and U2 are the historical accident type sets and historical accident cause sets corresponding to the target safety emergency response team, respectively.
[0014] Furthermore, the accident type is input into the collaborative shared processing database for traversal matching according to the accident processing experience value of 0 or 1, and a sample cause sharing set associated with the sample accident type having the same accident type is obtained;
[0015] The accident cause is traversed and matched with the sample cause shared set to obtain a sample cause attention set associated with the sample accident cause that is the same as the accident cause, and several precautions in the sample cause attention set are given real-time voice prompts to all safety emergency response personnel in the target safety emergency response team.
[0016] Furthermore, all accidents that occur during the handling process of the target safety emergency response team are monitored and counted, and the total number of all accidents is set as an active prevention impact indicator and analyzed;
[0017] If the active prevention impact indicator is 0, it indicates that the active prevention impact corresponding to the target security emergency response team is normal.
[0018] Furthermore, if the active prevention impact flag is not 0, then the formula Calculate and obtain the active prevention impact abnormal value ω corresponding to the target safety emergency response team; where i is a different accident, i = 1, 2, 3, ..., N; N is a positive integer, which is the total number of all accidents; YYi is the accident impact factor corresponding to different accidents; A is the active prevention impact abnormal standard value;
[0019] If the active prevention impact abnormal value is less than 1, the active prevention impact corresponding to the target security emergency response team is slightly abnormal;
[0020] If the active prevention impact abnormal value is greater than or equal to 1, the active prevention impact corresponding to the target security emergency response team is prompted to be severely abnormal.
[0021] Furthermore, when conducting supervision and analysis on the local active prevention implementation effects corresponding to different accident causes of accident types, all active prevention impact identifiers corresponding to the accident causes are obtained and the total number of active prevention normals with a statistical value of 0, as well as the total number of active prevention mild abnormalities and active prevention mild severe normals with a statistical value of non-0 are analyzed, and the formula is used to calculate the total number of active prevention mild abnormalities and the total number of active prevention mild severe normals. Calculate and obtain the local active prevention implementation validity XJ corresponding to the cause of the accident; where NY1, NY2, and NZ are the total number of active prevention mild abnormalities, the total number of active prevention mild severe normals, and the total number of active prevention normals, respectively; NB is the standard value of the local active prevention implementation validity; exp() represents an exponential function with the natural constant e as the base;
[0022] Conduct data analysis on the effectiveness of local active prevention implementation, and dynamically classify and mark the local active prevention implementation effects corresponding to the causes of the accidents based on the analysis results.
[0023] Furthermore, if the local active prevention implementation effectiveness is less than 1, it is indicated that the local active prevention implementation effect corresponding to the accident cause is normal, and the local active prevention implementation effect flag associated with the accident cause is set to 0;
[0024] If the local active prevention implementation effectiveness is greater than 1, it is prompted that the local active prevention implementation effect corresponding to the accident cause is abnormal, and the local active prevention implementation effect flag associated with the accident cause is set to 1, and an optimization management prompt is given for the active prevention implementation shared data of the accident cause.
[0025] Further, according to the local active prevention implementation effect identifier with a value of 0, all active prevention implementation shared data associated with the corresponding accident cause are obtained, and data processing and data analysis of the active prevention implementation shared hidden danger status are performed on the active prevention implementation shared data;
[0026] All accident causes belonging to the local active prevention implementation effect identifier with a value of 0 are marked as target accident causes and numbered, and the active prevention implementation shared data associated with different target accident causes are shared according to the numbers through the formula Calculate and obtain the active prevention content hidden danger degree FYj corresponding to different target accident causes; where j is a different target accident cause, j=1, 2, 3, ..., M; M is a positive integer, which is the total number of all target accident causes; NNj is the total number of active prevention implementation shared prompts in the active prevention implementation shared data associated with different target accident causes; NN′ and NN″ are the first active prevention content hidden danger standard value and the second active prevention content hidden danger standard value, respectively; min() means obtaining the minimum value of two real numbers; [*] is a rounding function, which means obtaining the maximum integer not exceeding the real number *.
[0027] Furthermore, if the hidden danger degree of the active prevention content is 1, it is indicated that the shared hidden danger status of the active prevention implementation of the target accident cause is normal;
[0028] If the hidden danger degree of the active prevention content is 0, it will be prompted that the shared hidden danger status of the active prevention implementation of the target accident cause is abnormal, and targeted prevention optimization management will be implemented on the shared data of the active prevention implementation of the accident cause.
[0029] Compared with the existing solutions, the present invention achieves the following beneficial effects:
[0030] The present invention obtains the collaborative shared data corresponding to the security emergency accident to which the data to be processed belongs by matching and screening the collaborative shared data, and synchronously pushes the collaborative shared data to the security emergency response team that handles the security emergency accident, and implements preventive reminders of several precautions for targeted security emergency response, thereby realizing intelligent matching of the collaborative shared data of the security emergency response team and active preventive reminders, and improving the autonomous collaborative shared processing and utilization effect of historical security emergency response data.
[0031] The present invention supervises the entire process of handling safety emergency accidents by the safety emergency response team that performs collaborative shared data synchronous push notifications, and performs active prevention impact processing and analysis on the supervision data and prompts the active prevention impact status. This can not only realize the digital processing and representation of the active prevention impact anomalies of single safety emergency accidents of different safety emergency response teams, but also provide reliable local supervision data support for the dynamic optimization management of shared data for subsequent active prevention corresponding to different accident causes.
[0032] The present invention analyzes the active prevention implementation effect in different dimensions of the active prevention impact supervision data of all safety emergency response teams that implement collaborative shared data synchronous push notifications for handling safety emergency accidents, and adaptively and dynamically optimizes the active prevention implementation shared data associated with different accident causes according to the analysis results of different dimensions, thereby realizing active multi-dimensional data processing and analysis optimization management of the existing safety emergency response collaborative sharing scheme, and improving the autonomous supervision, analysis and optimization management effect of the safety emergency response data. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] The present invention will be further described below in conjunction with the accompanying drawings.
[0034] Figure 1 This is a module block diagram of a security emergency processing data intelligent evaluation and management system of the present invention.
[0035] Figure 2 The present invention is a flowchart of the implementation steps of a security emergency processing data intelligent evaluation management system.
[0036] Figure 3 This is a flowchart of the present invention for pushing a sample cause attention set to a target security emergency response team.
[0037] Figure 4 It is a flowchart of data analysis on the effectiveness of local active prevention implementation in the present invention. DETAILED DESCRIPTION
[0038] 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.
[0039] like Figure 1 to Figure 2 As shown, the present invention is a security emergency processing data intelligent evaluation management system, including a security emergency processing matching sharing prompt module, a local security emergency processing sharing evaluation prompt module and an overall security emergency processing sharing evaluation management module;
[0040] The safety emergency processing matching sharing prompt module is used to obtain the pending data corresponding to different safety emergency incidents to be processed by different safety emergency processing teams, and to match and screen the pending data for collaborative sharing data, to obtain the collaborative sharing data corresponding to the safety emergency incident to which the pending data belongs, and to synchronously push the collaborative sharing data to the safety emergency processing team that handles the safety emergency incident; the specific steps include:
[0041] Obtain the pending data corresponding to the pending safety emergency incident and the target safety emergency response team to which it belongs; the target safety emergency response team may be determined based on the safety emergency response team closest to the location where the safety emergency incident occurred;
[0042] The safety emergency response team in the embodiment of the present invention is specifically composed of volunteers, who have certain professional knowledge and skills and are able to provide preliminary rescue and support before the arrival of government and professional rescue teams;
[0043] When matching and screening the collaborative shared data of the target safety emergency response team for the data to be processed, the accident type and cause of the safety emergency accident in the data to be processed are obtained, the accident type and cause of the accident are analyzed through the accident processing identification function, and the accident processing experience value JC corresponding to the safety emergency accident to be processed by the target safety emergency response team is output;
[0044] The accident type can be determined based on the processing requirement data corresponding to the existing safety emergency response plan, including but not limited to natural disaster types, industrial accident types, traffic accident types and construction accident types;
[0045] In addition, different accident types correspond to several accident causes, which can be determined based on all the existing accident data corresponding to different accident types;
[0046] The expression of the accident handling identification function is: In the formula, LS and LY are the accident types and accident causes corresponding to the safety emergency accidents in the data to be processed, respectively; U1 and U2 are the historical accident type sets and historical accident cause sets corresponding to the target safety emergency response team, respectively. The historical accident type sets and historical accident cause sets corresponding to the target safety emergency response team are determined by their historical safety emergency response data;
[0047] The incident handling experience value contains a value of 0, 1, or 2;
[0048] An incident handling experience value of 0 means that the target security emergency response team has never handled similar security emergency incidents in the past;
[0049] An incident handling experience value of 1 indicates that the target safety emergency response team has handled the same incident type in the past, but has not handled the same incident cause;
[0050] An incident handling experience value of 2 indicates that the target safety emergency response team has handled the same incident type and the same incident cause in the past;
[0051] In the embodiment of the present invention, by performing data processing and calculation between the safety emergency incident to be processed and the corresponding target safety emergency processing team, the accident processing experience value corresponding to the safety emergency incident to be processed by the target safety emergency processing team is obtained, which can not only realize the digital representation of the accident processing experience state corresponding to the safety emergency incident to be processed by the target safety emergency processing team, but also provide reliable data support for the subsequent collaborative sharing data synchronization push prompt of the target safety emergency processing team;
[0052] And, when the targeted safety emergency response team is screened and combined with collaborative shared data based on the accident handling experience value of 0 or 1;
[0053] like Figure 3 As shown, the accident type is input into the collaborative shared processing database according to the accident processing experience value of 0 or 1 for traversal matching, and a sample cause sharing set associated with the sample accident type having the same accident type is obtained;
[0054] Among them, a number of sample cause sharing sets are pre-stored in the collaborative shared processing database, and different sample cause sharing sets contain a number of sample accident causes, and different sample accident causes are associated with sample cause attention sets, and the number of sample cause attention sets are obtained by screening and classifying the summary data of different accident causes historically processed by several safety emergency response teams;
[0055] The accident cause is traversed and matched with the sample cause sharing set to obtain a sample cause attention set associated with the sample accident cause that is the same as the accident cause, and a number of attention points in the sample cause attention set are voiced in real time to all safety emergency response personnel in the target safety emergency response team;
[0056] Among them, different sample cause attention sets are associated with several precautions, which can be obtained through summary and shared screening by the safety emergency response team that has historically handled the same accident cause;
[0057] In an embodiment of the present invention, by matching and screening the collaboratively shared data for the data to be processed corresponding to the safety emergency accident, the collaboratively shared data corresponding to the safety emergency accident to which the data to be processed belongs is obtained, the collaboratively shared data is synchronously pushed to the safety emergency response team that handles the safety emergency accident, and preventive reminders of several precautions for targeted safety emergency response are implemented, thereby realizing intelligent matching of the collaboratively shared data of the safety emergency response team and active preventive reminders, and improving the autonomous collaborative sharing processing and utilization effect of historical safety emergency response data.
[0058] The local safety emergency response sharing assessment prompt module is used to supervise the whole process of the safety emergency response team that performs collaborative sharing data synchronous push prompts to handle safety emergency accidents, and to analyze the active prevention impact of the supervision data, so as to obtain the active prevention impact status of the safety emergency team in handling safety emergency accidents and give prompts; the specific steps include:
[0059] Monitor and count all accidents that occur during the handling process of the target safety emergency response team, set the total number of all accidents as active prevention impact indicators and analyze them; accidents include but are not limited to personal injuries and economic losses;
[0060] If the active prevention impact indicator is 0, it indicates that the active prevention impact corresponding to the target security emergency response team is normal;
[0061] If the active prevention impact flag is not 0, then the formula Calculate and obtain the active prevention impact abnormal value ω corresponding to the target safety emergency response team; where i is a different accident, i = 1, 2, 3, ..., N; N is a positive integer, which is the total number of all accidents; YYi is the accident impact factor corresponding to different accidents, and different accidents are pre-set with a corresponding accident impact factor. The accident impact factors associated with different accidents can be determined by professional and technical personnel in this field based on work experience, or based on the total number of historical occurrences of the corresponding accidents; A is the active prevention impact abnormal standard value, which is determined according to the design requirements of the collaborative sharing solution, or it can be determined by the median of all active prevention impact abnormal values obtained from historical processing;
[0062] It should be noted that the active prevention impact abnormal value is used to calculate the unexpected data when the target safety emergency response team handles the safety emergency incident, so as to digitally represent the active prevention impact abnormality of the target safety emergency response team;
[0063] If the active prevention impact abnormal value is less than 1, the active prevention impact corresponding to the target security emergency response team is slightly abnormal;
[0064] If the active prevention impact abnormal value is greater than or equal to 1, the active prevention impact severe abnormality corresponding to the target security emergency response team is prompted;
[0065] In an embodiment of the present invention, by supervising the entire process of handling a safety emergency accident by a safety emergency response team that performs collaborative shared data synchronous push notifications, and performing active prevention impact processing and analysis on the supervision data and prompting the active prevention impact status, it is possible to digitally process and represent the active prevention impact anomalies of a single safety emergency accident by different safety emergency response teams, and provide reliable local supervision data support for the dynamic optimization management of shared data for subsequent active prevention implementation corresponding to different accident causes.
[0066] The overall safety emergency response sharing evaluation management module is used to analyze the active prevention implementation effect of all safety emergency response teams that implement collaborative shared data synchronization push notifications on the active prevention impact supervision data of safety emergency accidents in different dimensions, and adaptively and dynamically optimize the active prevention implementation shared data associated with different accident causes based on the analysis results of different dimensions; the specific steps include:
[0067] When conducting supervision and analysis on the implementation effects of local active prevention for different accident causes, obtain all active prevention impact indicators corresponding to the accident causes and analyze the total number of active prevention normals with a statistical value of 0, the total number of active prevention mild anomalies and the total number of active prevention mild severe normals with a statistical value of non-0, and calculate the total number of active prevention mild abnormalities and the total number of active prevention mild severe normals through the formula Calculate and obtain the local active prevention implementation validity XJ corresponding to the cause of the accident; where NY1, NY2, and NZ are the total number of active prevention mild abnormalities, the total number of active prevention mild severe normals, and the total number of active prevention normals, respectively; NB is the standard value of the local active prevention implementation validity, which can be determined by the median value of all local active prevention implementation validity obtained from the historical processing of the cause of the accident; exp() represents an exponential function with the natural constant e as the base;
[0068] It should be noted that the local active prevention implementation effectiveness is used to calculate all the active prevention supervision data of the accident cause to digitally represent the local active prevention implementation effect corresponding to the accident cause;
[0069] like Figure 4 As shown, data analysis is performed on the effectiveness of local active prevention implementation, and the local active prevention implementation effects corresponding to the cause of the accident are dynamically classified and marked according to the analysis results;
[0070] If the local active prevention implementation effectiveness is less than 1, it indicates that the local active prevention implementation effect corresponding to the accident cause is normal, and the local active prevention implementation effect flag associated with the accident cause is set to 0;
[0071] If the local active prevention implementation validity is greater than 1, it is prompted that the local active prevention implementation effect corresponding to the accident cause is abnormal, and the local active prevention implementation effect flag associated with the accident cause is set to 1, and an optimization management prompt for the active prevention implementation shared data of the accident cause is performed;
[0072] In the implementation of the present invention, by calculating all the active prevention supervision data of the accident causes, the local active prevention implementation effects corresponding to the accident causes are digitally represented, thereby realizing the expanded utilization of all single collaborative shared processing data of the previous accident causes. It is possible to obtain the local active prevention implementation effects corresponding to different accident causes and implement targeted optimization management, and provide reliable data support for the data analysis of the active prevention implementation shared hidden danger status corresponding to the subsequent accident causes with normal active prevention implementation effects.
[0073] It should be noted that even if the active prevention implementation effect analyzed for the cause of the accident is normal, the active prevention implementation shared hidden danger status corresponding to the active prevention implementation shared data associated with the cause of the accident cannot be obtained through supervision, and further extended analysis is required;
[0074] And, according to the local active prevention implementation effect identifier with a value of 0, all active prevention implementation shared data associated with the corresponding accident cause are obtained, and data processing of the active prevention implementation shared data and data analysis of the active prevention implementation shared hidden danger status are performed on the active prevention implementation shared data;
[0075] All accident causes belonging to the local active prevention implementation effect identifier with a value of 0 are marked as target accident causes and numbered, and the active prevention implementation shared data associated with different target accident causes are shared according to the numbers through the formula Calculate and obtain the active prevention content hidden danger degree FYj corresponding to different target accident causes; where j is a different target accident cause, j=1, 2, 3, ..., M; M is a positive integer, which is the total number of all target accident causes; NNj is the total number of active prevention implementation shared prompts in the active prevention implementation shared data associated with different target accident causes; NN′ and NN″ are the first active prevention content hidden danger standard value and the second active prevention content hidden danger standard value, respectively, which can be determined according to the maximum value and median value of the total number of active prevention implementation shared prompts in the active prevention implementation shared data corresponding to all accident causes with abnormal local active prevention implementation effects; min() means obtaining the minimum value of two real numbers; [*] is a rounding function, which means obtaining the maximum integer not exceeding the real number *;
[0076] It should be noted that the active prevention content hidden danger degree is used to calculate the active prevention implementation shared data associated with the target accident cause to digitally represent its active prevention implementation shared hidden danger status;
[0077] If the hidden danger degree of the active prevention content is 1, it indicates that the shared hidden danger status of the active prevention implementation of the target accident cause is normal;
[0078] If the hidden danger degree of the active prevention content is 0, it will be prompted that the shared hidden danger status of the active prevention implementation of the target accident cause is abnormal, and targeted prevention optimization management will be implemented on the shared data of the active prevention implementation of the accident cause; for example, the shared data of active prevention implementation will be actively supplemented and improved.
[0079] In an embodiment of the present invention, by analyzing the active prevention implementation effect of different dimensions of the active prevention impact supervision data of safety emergency accidents handled by all safety emergency response teams that implement collaborative shared data synchronous push notifications, and adaptively optimizing the active prevention implementation shared data associated with different accident causes according to the analysis results of different dimensions, it is realized that the existing safety emergency response collaborative sharing scheme is actively processed in multiple dimensions and the analysis optimization management is improved, thereby improving the autonomous supervision, analysis and optimization management effect of the safety emergency response data.
[0080] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.
[0081] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.
[0082] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0083] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.
[0084] It is obvious to a person skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.
[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.
Claims
1. A security emergency processing data intelligent evaluation and management system, characterized in that: include: The safety emergency processing matching sharing prompt module is used to obtain the pending data corresponding to different safety emergency incidents to be processed by different safety emergency processing teams, and to match and screen the pending data for collaborative sharing data, obtain the collaborative sharing data corresponding to the safety emergency incident to which the pending data belongs, and synchronously push the collaborative sharing data to the safety emergency processing team that handles the safety emergency incident; The local safety emergency response sharing assessment prompt module is used to supervise the entire process of the safety emergency response team that performs collaborative sharing data synchronous push prompts to handle safety emergency accidents, and to analyze the active prevention impact of the supervision data, obtain the active prevention impact status of the safety emergency team in handling safety emergency accidents and provide prompts; The overall safety emergency response shared evaluation management module is used to analyze the active prevention implementation effects in different dimensions of the active prevention impact supervision data of safety emergency accidents handled by all safety emergency response teams that implement collaborative shared data synchronous push notifications, and dynamically optimize the active prevention implementation shared data related to different accident causes based on the analysis results of different dimensions.
2. According to claim 1, the intelligent evaluation and management system for security emergency processing data is characterized in that: Obtain the pending data corresponding to the pending security emergency incidents and the target security emergency response team to which they belong; When matching and screening the collaborative shared data of the target safety emergency response team for the data to be processed, the accident type and cause of the safety emergency accident in the data to be processed are obtained, the accident type and cause of the accident are analyzed through the accident processing identification function, and the accident processing experience value corresponding to the safety emergency accident to be processed by the target safety emergency response team is output; Incident handling experience contains a value of 0, 1, or 2.
3. The intelligent evaluation and management system for security emergency processing data according to claim 2 is characterized in that: The expression of the accident handling identification function is: Where LS and LY are the accident types and accident causes corresponding to the safety emergency accidents in the data to be processed, respectively; U1 and U2 are the historical accident type sets and historical accident cause sets corresponding to the target safety emergency response team, respectively.
4. The intelligent evaluation and management system for security emergency processing data according to claim 2 is characterized in that: According to the accident processing experience value of 0 or 1, the accident type is input into the collaborative shared processing database for traversal matching to obtain a sample cause sharing set associated with the sample accident type that is the same as the accident type; The accident cause is traversed and matched with the sample cause shared set to obtain a sample cause attention set associated with the sample accident cause that is the same as the accident cause, and several precautions in the sample cause attention set are given real-time voice prompts to all safety emergency response personnel in the target safety emergency response team.
5. The intelligent evaluation and management system for security emergency processing data according to claim 4 is characterized in that: Monitor and count all accidents that occur during the handling process of the target safety emergency response team, set the total number of all accidents as active prevention impact indicators and analyze them; If the active prevention impact indicator is 0, it indicates that the active prevention impact corresponding to the target security emergency response team is normal.
6. The intelligent evaluation and management system for security emergency processing data according to claim 5 is characterized in that: If the active prevention impact flag is not 0, then the formula Calculate and obtain the active prevention impact abnormal value ω corresponding to the target safety emergency response team; where i is a different accident, i = 1, 2, 3, ..., N; N is a positive integer, which is the total number of all accidents; YYi is the accident impact factor corresponding to different accidents; A is the active prevention impact abnormal standard value; If the active prevention impact abnormal value is less than 1, the active prevention impact corresponding to the target security emergency response team is slightly abnormal; If the active prevention impact abnormal value is greater than or equal to 1, the active prevention impact corresponding to the target security emergency response team is prompted to be severely abnormal.
7. The intelligent evaluation and management system for security emergency processing data according to claim 6 is characterized in that: When conducting supervision and analysis on the local active prevention implementation effects corresponding to different accident causes of accident types, obtain all active prevention impact identifiers corresponding to the accident causes and analyze the total number of active prevention normals with a statistical value of 0, as well as the total number of active prevention mild abnormalities and active prevention mild severe normals with a statistical value of non-0, and calculate the total number of active prevention mild abnormalities and the total number of active prevention mild severe normals through the formula Calculate and obtain the local active prevention implementation validity XJ corresponding to the cause of the accident; where NY1, NY2, and NZ are the total number of active prevention mild abnormalities, the total number of active prevention mild severe normals, and the total number of active prevention normals, respectively; NB is the standard value of the local active prevention implementation validity; exp() represents an exponential function with the natural constant e as the base; Conduct data analysis on the effectiveness of local active prevention implementation, and dynamically classify and mark the local active prevention implementation effects corresponding to the causes of the accidents based on the analysis results.
8. The intelligent evaluation and management system for security emergency processing data according to claim 7 is characterized in that: If the local active prevention implementation effectiveness is less than 1, it indicates that the local active prevention implementation effect corresponding to the accident cause is normal, and the local active prevention implementation effect flag associated with the accident cause is set to 0; If the local active prevention implementation effectiveness is greater than 1, it is prompted that the local active prevention implementation effect corresponding to the accident cause is abnormal, and the local active prevention implementation effect flag associated with the accident cause is set to 1, and an optimization management prompt is given for the active prevention implementation shared data of the accident cause.
9. The intelligent evaluation and management system for security emergency processing data according to claim 8, characterized in that: According to the local active prevention implementation effect identifier with a value of 0, all active prevention implementation shared data associated with the corresponding accident cause are obtained, and data processing of the active prevention implementation shared data and data analysis of the active prevention implementation shared hidden danger status are performed; All accident causes belonging to the local active prevention implementation effect identifier with a value of 0 are marked as target accident causes and numbered, and the active prevention implementation shared data associated with different target accident causes are shared according to the numbers through the formula Calculate and obtain the active prevention content hidden danger degree FYj corresponding to different target accident causes; where j is a different target accident cause, j=1, 2, 3, ..., M; M is a positive integer, which is the total number of all target accident causes; NNj is the total number of active prevention implementation shared prompts in the active prevention implementation shared data associated with different target accident causes; NN′ and NN″ are respectively the first active prevention content hidden danger standard value and the second active prevention content hidden danger standard value; min() means obtaining the minimum value of two real numbers; [*] is the rounding function, which means obtaining the maximum integer not exceeding the real number *.
10. The intelligent evaluation and management system for security emergency processing data according to claim 9, characterized in that: If the hidden danger degree of the active prevention content is 1, it indicates that the shared hidden danger status of the active prevention implementation of the target accident cause is normal; If the hidden danger degree of the active prevention content is 0, it will be prompted that the shared hidden danger status of the active prevention implementation of the target accident cause is abnormal, and targeted prevention optimization management will be implemented on the shared data of the active prevention implementation of the accident cause.