Intelligent matching method, device, equipment and storage medium for multi-scenario emergency measures

Abstract

The present invention provides an intelligent matching method, device, equipment and storage medium for multi-scenario emergency measures, and belongs to the field of emergency data processing technology. The method includes: obtaining the scene information of the current scene; wherein the scene information includes the scene purpose, scene scale, risk category, risk level and risk stage; matching the current scene with the historical scene in the emergency measures database based on the scene information of the current scene, and obtaining a matching scene similar to the current scene; wherein the emergency measures database stores scene information and emergency measures of multiple historical scenes; based on the difference in scene information between the current scene and the matching scene, adjusting the emergency measures corresponding to the matching scene, and obtaining the emergency measures corresponding to the current scene. The present invention can improve the implementation effect of the generated emergency measures, and has higher efficiency and more comprehensive and objective considerations than manually formulated emergency measures.

Description

Technical Field

[0001] The present invention relates to the field of emergency data processing technology, and in particular to an intelligent matching method, device, equipment and storage medium for multi-scenario emergency measures. Background Art

[0002] With the acceleration of urbanization, dense population and concentrated buildings, emergency measures should be able to be quickly initiated in the event of an emergency, such as fire, earthquake, traffic accident, etc. Perfect emergency measures can effectively control the accident at the early stage, protect people's lives and property, reduce the expansion of accidents and the occurrence of secondary disasters, thereby reducing casualties and economic losses, and maintaining social stability and normal order.

[0003] However, the types and scenarios of emergencies are complex, and the specific circumstances of each incident are unique, which leads to difficulties in the selection and implementation of emergency measures. They still need to rely on manual formulation, which is inefficient and has unstable implementation effects, making it difficult to effectively deal with all emergency situations. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, equipment and storage medium for intelligently matching emergency measures in multiple scenarios to solve the problem that manually formulated emergency measures are inefficient and have unstable implementation effects.

[0005] In a first aspect, an embodiment of the present invention provides an intelligent matching method for emergency measures in multiple scenarios, including:

[0006] Obtaining scenario information of the current scenario; wherein the scenario information includes scenario purpose, scenario scale, risk category, risk level and risk stage;

[0007] Based on the scene information of the current scene, the current scene is matched with the historical scenes in the emergency measures database to obtain a matching scene similar to the current scene; wherein the emergency measures database stores scene information and emergency measures of multiple historical scenes;

[0008] Based on the difference in scene information between the current scene and the matching scene, the emergency measures corresponding to the matching scene are adjusted to obtain the emergency measures corresponding to the current scene.

[0009] In a possible implementation, based on the scene information of the current scene, the current scene is matched with the historical scenes in the emergency measures database to obtain a matching scene similar to the current scene, including:

[0010] Select historical scenarios with the same scenario usage and risk category as the current scenario from the historical scenarios in the emergency measures database as candidate scenarios;

[0011] Based on the scenario information of each candidate scenario and the current scenario, the difficulty of adjusting the emergency measures of each candidate scenario relative to the current scenario is calculated;

[0012] The candidate scenario with the least difficulty in adjusting emergency measures is taken as the matching scenario corresponding to the current scenario.

[0013] In a possible implementation, based on the scene information of each candidate scene and the current scene, calculating the difficulty of emergency measure adjustment of each candidate scene relative to the current scene includes:

[0014] Determine the difference in scenario scale, risk level, and risk stage between the first candidate scenario and the current scenario; wherein the first candidate scenario is any candidate scenario;

[0015] Based on the scenario purpose and scenario category of the current scenario, determine the scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scenario in a preset difficulty coefficient comparison table;

[0016] The emergency measures adjustment difficulty score corresponding to the first candidate scenario is calculated based on the scenario scale difference, risk level difference, risk stage difference, scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scenario.

[0017] In a possible implementation, before determining the scene scale difference difficulty coefficient, risk level difference difficulty coefficient, and risk stage difference difficulty coefficient corresponding to the first candidate scene in a preset difficulty coefficient comparison table based on the scene purpose and scene category of the current scene, the method further includes:

[0018] Based on the hierarchical analysis method, a comparison table is constructed for scenario usage, risk category, difficulty coefficient of scenario scale difference, difficulty coefficient of risk level difference and difficulty coefficient of risk stage difference.

[0019] In a possible implementation, based on the scene information of the current scene, the current scene is matched with the historical scenes in the emergency measures database to obtain a matching scene similar to the current scene, and further includes:

[0020] If there is no historical scenario with the same scenario purpose and risk category, determine the difference in scenario purpose and risk category between each historical scenario and the current scenario;

[0021] Based on the differences in scene usage and risk categories between each historical scene and the current scene, the scene usage difference effect reduction coefficient and the risk category difference effect reduction coefficient of each historical scene are determined in a preset effect reduction coefficient comparison table;

[0022] For each historical scenario, based on the emergency measures effect evaluation score of the historical scenario, the scenario usage difference effect reduction coefficient, and the risk category difference effect reduction coefficient, determine the estimated effect evaluation score of applying the emergency measures of the historical scenario to the current scenario;

[0023] Based on the estimated effect evaluation scores of each historical scene, a matching scene corresponding to the current scene is selected from each historical scene.

[0024] In a possible implementation, the scene information difference includes scene scale difference, risk level difference and risk stage difference; based on the scene information difference between the current scene and the matching scene, the emergency measures corresponding to the matching scene are adjusted to obtain the emergency measures corresponding to the current scene, including:

[0025] Based on the differences in risk levels and scenario scales between the current scenario and the matching scenario, the intensity of the emergency measures corresponding to the matching scenario is adjusted.

[0026] In a possible implementation, before matching the current scene with the historical scenes in the emergency measures database based on the scene information of the current scene to obtain a matching scene similar to the current scene, the method further includes:

[0027] Obtaining scene information, emergency measures, and effect evaluation of a first historical scene; wherein the first historical scene is any historical scene;

[0028] Based on the branches of each evaluation dimension in the effect evaluation, determine the optimization dimension and optimization range of the emergency measures corresponding to the first historical scenario;

[0029] The emergency measures corresponding to the first historical scenario are optimized based on the optimization dimension and optimization range of the emergency measures corresponding to the first historical scenario, and the optimized emergency measures and the optimized effect evaluation are obtained;

[0030] The scene information of the first historical scene, the optimized emergency measures and the optimized effect evaluation are stored in the emergency measures database.

[0031] In a second aspect, an embodiment of the present invention provides an intelligent matching device for emergency measures in multiple scenarios, including:

[0032] An acquisition module is used to acquire the scene information of the current scene; wherein the scene information includes scene purpose, scene scale, risk category, risk level and risk stage;

[0033] A matching module, used to match the current scene with the historical scenes in the emergency measures database based on the scene information of the current scene, to obtain a matching scene similar to the current scene; wherein the emergency measures database stores scene information and emergency measures of multiple historical scenes;

[0034] The adjustment module is used to adjust the emergency measures corresponding to the matching scene based on the difference in scene information between the current scene and the matching scene, so as to obtain the emergency measures corresponding to the current scene.

[0035] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements the steps of the method described in the first aspect or any possible implementation manner of the first aspect.

[0036] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation method of the first aspect are implemented.

[0037] The embodiments of the present invention provide an intelligent matching method, device, equipment and storage medium for emergency measures in multiple scenarios. The historical scenarios in the emergency measures database are matched by the scenario purpose, scenario scale, risk category, risk level and risk stage of the current scenario, and the matching scenarios similar to the current scenario are determined, so that the emergency measures of the matching scenarios are used as a reference basis for determining the emergency measures of the current scenario, and the information in the historical cases is effectively utilized. Then, based on the historical scenarios, the emergency measures of the matching scenarios are adjusted based on the scenario differences between the two, so that the specific implementation method of the emergency measures is closer to the current scenario, the implementation effect of the generated emergency measures is improved, and compared with manually formulated emergency measures, it has higher efficiency and more comprehensive and objective considerations. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art 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 labor.

[0039] Figure 1 It is a flowchart of an implementation method of an intelligent matching method for multi-scenario emergency measures provided by an embodiment of the present invention;

[0040] Figure 2 It is a structural schematic diagram of an intelligent matching device for multi-scenario emergency measures provided by an embodiment of the present invention;

[0041] Figure 3 It is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0043] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0044] See also Figure 1 , which shows a flowchart of the implementation of the intelligent matching method for multi-scenario emergency measures provided by an embodiment of the present invention, and is described in detail as follows:

[0045] Step 101, obtain the scene information of the current scene; wherein the scene information includes the scene purpose, scene scale, risk category, risk level and risk stage.

[0046] In this embodiment, the scene information is used to describe various attributes of the area where the emergency occurs. By comparing the scene information of different scenes, the similarity between the scenes can be determined to determine the most similar scene, and the emergency measures for similar scenes can be reused.

[0047] Among them, scene usage refers to the purpose of an area or building, such as a commercial residential area, a chemical plant, a food foreman, a subway station, etc. If the purposes of two scenes are the same or similar, the internal structure, personnel distribution, and personnel behavior patterns of the two scenes will also be similar.

[0048] The scale of a scene refers to the size of an area or building. It is usually divided according to the purpose of the scene, such as small factory, medium factory, large factory, or small commercial area, large commercial area, etc. The scale of the scene can also be directly described by the usable area or land area. The specific division method can be adjusted according to the purpose of the scene.

[0049] Risk categories refer to the categories of emergency situations, including fire, traffic accidents, earthquakes, collapse, hazardous chemical leaks, etc. Different types of emergency situations correspond to different types of emergency measures.

[0050] The risk level refers to the severity of the emergency, and is usually divided into risk levels based on the type of emergency, such as earthquake level, fire level, traffic accident level, etc. Different levels of emergency situations correspond to emergency measures of different intensities, such as the number of emergency personnel called and the amount of fire-fighting materials used, which need to change with the level of the emergency.

[0051] The risk stage is usually divided into pre-event, mid-event and post-event, and the emergency measures to be taken at different risk stages have different focuses. In general, emergency measures should be implemented for pre-event prevention, mid-event control and post-event remediation. When an emergency develops very quickly, such as an earthquake, it may already be in the post-event stage when it is discovered. At this time, emergency treatment is still required, such as taking measures such as building maintenance and personnel rescue.

[0052] Step 102, based on the scene information of the current scene, the current scene is matched with the historical scenes in the emergency measures database to obtain a matching scene similar to the current scene; wherein the emergency measures database stores scene information and emergency measures of multiple historical scenes.

[0053] In this embodiment, the emergency measures of the scene are usually determined based on the scene information. After the historical scene is matched with a matching scene similar to the current scene in the emergency measures database based on the scene information, the emergency measures of the matching scene will also be adapted to the current scene, thereby enabling the reuse of the emergency measures of the historical scene.

[0054] Step 103: Based on the difference in scene information between the current scene and the matching scene, the emergency measures corresponding to the matching scene are adjusted to obtain the emergency measures corresponding to the current scene.

[0055] In this embodiment, due to the complexity of the categories and scenarios of emergency situations, it is almost impossible to find a historical scene that is exactly the same as the current scene. Therefore, the emergency measures of the historical scenes cannot be directly used in the current scene. For this purpose, adjustments can be made based on the emergency measures corresponding to the matching scene. The matching scene is relatively similar to the current scene, so the category of the emergency measures does not need to be changed. The dimension and magnitude of the specific adjustment of the emergency measures can be determined based on the difference in scene information between the current scene and the matching scene.

[0056] The embodiment of the present invention matches historical scenarios in the emergency measures database through the scenario purpose, scenario scale, risk category, risk level and risk stage of the current scenario, determines a matching scenario that is similar to the current scenario, and uses the emergency measures of the matching scenario as a reference basis for determining the emergency measures of the current scenario, effectively utilizing the information in historical cases, and then adjusting the emergency measures of the matching scenario based on the historical scenario and the scenario differences between the two, so that the specific implementation method of the emergency measures is closer to the current scenario, improves the implementation effect of the generated emergency measures, and has higher efficiency and more comprehensive and objective considerations than manually formulating emergency measures.

[0057] In a possible implementation, based on the scene information of the current scene, the current scene is matched with the historical scenes in the emergency measures database to obtain a matching scene similar to the current scene, including:

[0058] Select historical scenarios with the same scenario usage and risk category as the current scenario from the historical scenarios in the emergency measures database as candidate scenarios;

[0059] Based on the scenario information of each candidate scenario and the current scenario, the difficulty of adjusting the emergency measures of each candidate scenario relative to the current scenario is calculated;

[0060] The candidate scenario with the least difficulty in adjusting emergency measures is taken as the matching scenario corresponding to the current scenario.

[0061] In this embodiment, the scenario information that affects the category of emergency measures includes scenario purpose and risk category. When the scenario purpose and risk category of the historical scenario are the same as those of the current scene, the category of emergency measures applicable to the two scenarios is also the same, that is, the emergency measures of the historical scenario are applicable to the current scene.

[0062] At the same time, when there are differences in scenario scale, risk level and risk stage between the historical scenario and the current scenario, the specific parameters of the emergency measures of the historical scenario are not applicable to the current scenario. For example, if the historical scenario is a leak in a small chemical plant and the current scenario is a leak in a large chemical plant, then the emergency measures required for the current scenario have a wider coverage than those for the historical scenario, that is, the intensity of the emergency measures required is higher.

[0063] On this basis, as the differences between historical scenarios and current scenarios vary, the difficulty of adjusting the emergency measures of historical scenarios to be applicable to current scenarios also varies. For example, when there are differences in the scene scale, risk level, and risk stage between historical scenarios and current scenarios, both the intensity and implementation time of emergency measures need to be adjusted, while when there is only a difference in the scene scale between historical scenarios and current scenarios, only the intensity of emergency measures needs to be adjusted. Obviously, the adjustment difficulty of the latter is lower than that of the former. Similarly, when the difference in scene information between historical scenarios and current scenarios is different, the adjustment range of the intensity of emergency measures is also different, and the corresponding adjustment difficulty is also different.

[0064] Therefore, the adjustment difficulty of emergency measures can be quantitatively evaluated, so that when there are multiple candidate matching scenarios, the matching scenario with the lowest adjustment difficulty can be selected, thereby reducing the workload of adjusting emergency measures and improving adjustment efficiency.

[0065] In a possible implementation, based on the scene information of each candidate scene and the current scene, calculating the difficulty of emergency measure adjustment of each candidate scene relative to the current scene includes:

[0066] Determine the difference in scenario scale, risk level, and risk stage between the first candidate scenario and the current scenario; wherein the first candidate scenario is any candidate scenario;

[0067] Based on the scenario purpose and scenario category of the current scenario, determine the scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scenario in a preset difficulty coefficient comparison table;

[0068] The emergency measures adjustment difficulty score corresponding to the first candidate scenario is calculated based on the scenario scale difference, risk level difference, risk stage difference, scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scenario.

[0069] In this embodiment, since the greater the difference in scenario scale, risk level and risk stage, the greater the difficulty of adjusting emergency measures, each difference can be quantified, and the difficulty score of adjusting emergency measures corresponding to the candidate scenario can be calculated based on the product of the difference value and the difficulty coefficient. For example, the risk level of the candidate scenario is level three, and the risk level of the current scenario is level one. The level one risk is two levels higher than the level three risk. Therefore, when other differences are not considered, the difficulty score of adjusting emergency measures corresponding to the candidate scenario = 2 × risk level difference difficulty coefficient.

[0070] Similarly, if the risk level of the candidate scenario is higher than that of the current scenario, for example, the risk level of the candidate scenario is level 1 and the risk level of the current scenario is level 3, the difference between the two is still two levels, and the difference value is 2. Without considering other differences, the emergency measures adjustment difficulty score corresponding to the candidate scenario = 2 × risk level difference difficulty coefficient. When considering multiple differences, the difference value is multiplied by the corresponding difference difficulty coefficient and then summed to obtain the emergency measures adjustment difficulty score corresponding to the candidate scenario.

[0071] In a possible implementation, before determining the scene scale difference difficulty coefficient, risk level difference difficulty coefficient, and risk stage difference difficulty coefficient corresponding to the first candidate scene in a preset difficulty coefficient comparison table based on the scene purpose and scene category of the current scene, the method further includes:

[0072] Based on the hierarchical analysis method, a comparison table is constructed for scenario usage, risk category, difficulty coefficient of scenario scale difference, difficulty coefficient of risk level difference and difficulty coefficient of risk stage difference.

[0073] In this embodiment, the difficulty coefficient of the difference in scene scale, the difficulty coefficient of the difference in risk level and the difficulty coefficient of the difference in risk stage are all affected by the scene purpose and risk category, so a comparison table can be constructed, specifically, a comparison table of the scene purpose, risk category and the difficulty coefficient of the difference in scene scale, a comparison table of the scene purpose, risk category and the difficulty coefficient of the difference in risk level, and a comparison table of the scene purpose, risk category and the difficulty coefficient of the difference in risk level can be constructed respectively. Based on the scene purpose, risk category and each comparison table, the difficulty coefficient of the difference in scene scale, the difficulty coefficient of the difference in risk level and the difficulty coefficient of the difference in risk stage can be found and determined.

[0074] The comparison table of each difference difficulty coefficient can be determined based on the analytic hierarchy process. Specifically, similar to the steps of determining weights based on the analytic hierarchy process, the influence of various scenarios and risk categories on a certain difference difficulty is compared, the importance is ranked and a judgment matrix is ​​constructed. After the consistency test, the difference difficulty coefficient corresponding to each scenario and risk category can be obtained, and the comparison table of the difference difficulty coefficient can be obtained. In this way, various difference difficulty coefficients can be objectively determined, and the difficulty of adjusting emergency measures can be accurately evaluated to select appropriate matching scenarios.

[0075] In a possible implementation, based on the scene information of the current scene, the current scene is matched with the historical scenes in the emergency measures database to obtain a matching scene similar to the current scene, and further includes:

[0076] If there is no historical scenario with the same scenario purpose and risk category, determine the difference in scenario purpose and risk category between each historical scenario and the current scenario;

[0077] Based on the differences in scene usage and risk categories between each historical scene and the current scene, the scene usage difference effect reduction coefficient and the risk category difference effect reduction coefficient of each historical scene are determined in a preset effect reduction coefficient comparison table;

[0078] For each historical scenario, based on the emergency measures effect evaluation score of the historical scenario, the scenario usage difference effect reduction coefficient, and the risk category difference effect reduction coefficient, determine the estimated effect evaluation score of applying the emergency measures of the historical scenario to the current scenario;

[0079] Based on the estimated effect evaluation scores of each historical scene, a matching scene corresponding to the current scene is selected from each historical scene.

[0080] In this embodiment, by evaluating the implementation effect of emergency measures, it is possible to identify whether the emergency measures are effective and optimize the emergency measures. Specifically, the evaluation can be performed in multiple dimensions according to actual needs, such as casualties, building damage, emergency material consumption, damage to items in the building, time to escape from the emergency, etc.

[0081] When there are differences in scenario usage and risk categories between the historical scenario and the current scenario, if the emergency measures of the same category in the historical scenario are applied to the current scenario, the implementation effect may be reduced. For example, if the historical scenario is a fire in a residential area and the current scenario is a fire in a commercial area, if the same fire-fighting and rescue methods are used, the implementation effect may be reduced.

[0082] For example, the historical scenario is that hazardous chemical A leaked in a factory, and the current scenario is that hazardous chemical B leaked in a factory. The properties of the two chemicals are different. If the means used for emergency containment and disposal of chemical A are directly applied to chemical B, the disposal effect may decrease.

[0083] Based on this, in this embodiment, various emergency measures, scenario uses and risk categories are analyzed, and the impact of scenario use differences and risk category differences on the effect of emergency measures conversion is quantified as a difference effect reduction coefficient. Based on the difference in scenario use and risk category between historical scenarios and current scenarios, the corresponding difference effect reduction coefficient is found, and then the emergency measures effect evaluation score of the historical scenarios is multiplied by each difference effect reduction coefficient respectively to determine the estimated effect evaluation score of the emergency measures of each historical scenario applied to the current scenario. When the historical scenario is not close enough to the current scene, emergency measures suitable for the current scene can be found as much as possible.

[0084] In a possible implementation, the scene information difference includes scene scale difference, risk level difference and risk stage difference; based on the scene information difference between the current scene and the matching scene, the emergency measures corresponding to the matching scene are adjusted to obtain the emergency measures corresponding to the current scene, including:

[0085] Based on the differences in risk levels and scenario scales between the current scenario and the matching scenario, the intensity of the emergency measures corresponding to the matching scenario is adjusted.

[0086] In this embodiment, the difference in risk level and the difference in scene scale will affect the intensity of the required emergency measures. For example, the higher the level of the fire, the larger the fire and the area covered, the more firefighting and rescue personnel are required, and the more firefighting materials are used; the larger the scale of the fire scene, the larger the number of people and area involved, the more firefighting and rescue personnel are required, and the more firefighting materials are used.

[0087] The specific adjustment method for the intensity of emergency measures may be to determine the specific category of the intensity of emergency measures according to the category of emergency measures. For example, when the emergency measure is fire extinguishing, the specific category of the intensity of emergency measures is the number of fire extinguishing and rescue personnel and the amount of fire extinguishing materials used. Then the difference value is converted into the intensity adjustment amount using the adjustment coefficient of the intensity category. For example, when the difference value of the fire level is 1, the adjustment coefficient of the intensity category is 2, then the adjusted number of fire extinguishing and rescue personnel = 2×the number of fire extinguishing and rescue personnel before adjustment, and the adjusted amount of fire extinguishing materials = 2×the amount of fire extinguishing materials before adjustment.

[0088] In a possible implementation, before matching the current scene with the historical scenes in the emergency measures database based on the scene information of the current scene to obtain a matching scene similar to the current scene, the method further includes:

[0089] Obtaining scene information, emergency measures, and effect evaluation of a first historical scene; wherein the first historical scene is any historical scene;

[0090] Based on the branches of each evaluation dimension in the effect evaluation, determine the optimization dimension and optimization range of the emergency measures corresponding to the first historical scenario;

[0091] The emergency measures corresponding to the first historical scenario are optimized based on the optimization dimension and optimization range of the emergency measures corresponding to the first historical scenario, and the optimized emergency measures and the optimized effect evaluation are obtained;

[0092] The scene information of the first historical scene, the optimized emergency measures and the optimized effect evaluation are stored in the emergency measures database.

[0093] In this embodiment, before determining a matching scenario for the current scenario based on the emergency measures database, the emergency measures of the historical scenarios can be optimized from the perspective of post-event review based on the effect evaluation of the historical scenarios, so that the matched emergency measures have better implementation effects.

[0094] Specifically, since the implementation effect evaluation involves multiple dimensions of the implementation effect, and each evaluation dimension has a certain correspondence with the dimension of the emergency measures, the correspondence between the evaluation dimension and the optimization dimension can be pre-set to achieve automatic optimization. For example, if the historical scenario is a fire in a certain area, and the damage to items in the building in the emergency measures effect evaluation is too much damage to items, the data of the dimensions related to fire extinguishing efficiency and item protection in the emergency measures can be enhanced, and the specific increase can be determined based on the preset ratio.

[0095] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean 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 embodiment of the present invention.

[0096] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0097] Figure 2 The structure diagram of the intelligent matching device for multi-scenario emergency measures provided by an embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0098] like Figure 2 As shown, the intelligent matching device 2 for multi-scenario emergency measures includes:

[0099] The acquisition module 21 is used to acquire the scene information of the current scene; wherein the scene information includes the scene purpose, scene scale, risk category, risk level and risk stage;

[0100] A matching module 22 is used to match the current scene with the historical scenes in the emergency measures database based on the scene information of the current scene to obtain a matching scene similar to the current scene; wherein the emergency measures database stores scene information and emergency measures of multiple historical scenes;

[0101] The adjustment module 23 is used to adjust the emergency measures corresponding to the matching scene based on the difference in scene information between the current scene and the matching scene, so as to obtain the emergency measures corresponding to the current scene.

[0102] In a possible implementation, the matching module 22 is specifically configured to:

[0103] Select historical scenarios with the same scenario usage and risk category as the current scenario from the historical scenarios in the emergency measures database as candidate scenarios;

[0104] Based on the scenario information of each candidate scenario and the current scenario, calculating the difficulty of emergency measures adjustment for each candidate scenario relative to the current scenario;

[0105] The candidate scenario with the least difficulty in adjusting emergency measures is taken as the matching scenario corresponding to the current scenario.

[0106] In a possible implementation, the matching module 22 is specifically configured to:

[0107] Determine the difference in scenario scale, risk level, and risk stage between the first candidate scenario and the current scenario; wherein the first candidate scenario is any candidate scenario;

[0108] Based on the scenario purpose and scenario category of the current scenario, determine the scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scenario in a preset difficulty coefficient comparison table;

[0109] The emergency measures adjustment difficulty score corresponding to the first candidate scenario is calculated based on the scenario scale difference, risk level difference, risk stage difference, scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scenario.

[0110] In a possible implementation, the matching module 22 is further configured to:

[0111] Before determining the scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scenario in the preset difficulty coefficient comparison table based on the scenario purpose and scenario category of the current scenario, a comparison table of scenario purpose, risk category, scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient is constructed based on the hierarchical analysis method.

[0112] In a possible implementation, the matching module 22 is further configured to:

[0113] If there is no historical scenario with the same scenario purpose and risk category, determine the difference in scenario purpose and risk category between each historical scenario and the current scenario;

[0114] Based on the differences in scene usage and risk categories between each historical scene and the current scene, the scene usage difference effect reduction coefficient and the risk category difference effect reduction coefficient of each historical scene are determined in a preset effect reduction coefficient comparison table;

[0115] For each historical scenario, based on the emergency measures effect evaluation score of the historical scenario, the scenario usage difference effect reduction coefficient, and the risk category difference effect reduction coefficient, determine the estimated effect evaluation score of applying the emergency measures of the historical scenario to the current scenario;

[0116] Based on the estimated effect evaluation scores of each historical scene, a matching scene corresponding to the current scene is selected from each historical scene.

[0117] In a possible implementation, the scenario information difference includes scenario scale difference, risk level difference and risk stage difference; the adjustment module 23 is specifically used for:

[0118] Based on the differences in risk levels and scenario scales between the current scenario and the matching scenario, the intensity of the emergency measures corresponding to the matching scenario is adjusted.

[0119] In a possible implementation, the matching module 22 is further configured to:

[0120] Before matching the current scene with the historical scenes in the emergency measures database based on the scene information of the current scene to obtain a matching scene similar to the current scene, obtaining scene information, emergency measures and effect evaluation of a first historical scene; wherein the first historical scene is any historical scene;

[0121] Based on the branches of each evaluation dimension in the effect evaluation, determine the optimization dimension and optimization range of the emergency measures corresponding to the first historical scenario;

[0122] The emergency measures corresponding to the first historical scenario are optimized based on the optimization dimension and optimization range of the emergency measures corresponding to the first historical scenario, and the optimized emergency measures and the optimized effect evaluation are obtained;

[0123] The scene information of the first historical scene, the optimized emergency measures and the optimized effect evaluation are stored in the emergency measures database.

[0124] The embodiment of the present invention matches historical scenarios in the emergency measures database through the scenario purpose, scenario scale, risk category, risk level and risk stage of the current scenario, determines a matching scenario that is similar to the current scenario, and uses the emergency measures of the matching scenario as a reference basis for determining the emergency measures of the current scenario, effectively utilizing the information in historical cases, and then adjusting the emergency measures of the matching scenario based on the historical scenario and the scenario differences between the two, so that the specific implementation method of the emergency measures is closer to the current scenario, improves the implementation effect of the generated emergency measures, and has higher efficiency and more comprehensive and objective considerations than manually formulating emergency measures.

[0125] Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, the electronic device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above-mentioned intelligent matching method embodiments of the multi-scenario emergency measures are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are realized, for example, Figure 2 Functions of modules / units 21 to 23 are shown.

[0126] Exemplarily, the computer program 32 may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 32 in the electronic device 3. For example, the computer program 32 may be divided into Figure 2 Modules / units 21 to 23 are shown.

[0127] The electronic device 3 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of the electronic device 3 and does not constitute a limitation of the electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0128] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0129] The memory 31 may be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 31 may also be an external storage device of the electronic device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the electronic device 3. The memory 31 is used to store the computer program and other programs and data required by the electronic device. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0130] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0131] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0132] Those skilled in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0133] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0136] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned intelligent matching method embodiments of each multi-scenario emergency measure can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0137] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An intelligent matching method for emergency measures in multiple scenarios, characterized in that: include: Obtaining scene information of the current scene; wherein the scene information includes scene purpose, scene scale, risk category, risk level and risk stage; Based on the scene information of the current scene, the current scene is matched with the historical scene in the emergency measures database to obtain a matching scene similar to the current scene; wherein the emergency measures database stores scene information and emergency measures of multiple historical scenes; Based on the difference in scene information between the current scene and the matching scene, the emergency measures corresponding to the matching scene are adjusted to obtain the emergency measures corresponding to the current scene; The method of matching the current scene with the historical scenes in the emergency measures database based on the scene information of the current scene to obtain a matching scene similar to the current scene includes: Select historical scenarios with the same scenario usage and risk category as the current scenario from the historical scenarios in the emergency measures database as candidate scenarios; Based on the scenario information of each candidate scenario and the current scenario, calculating the difficulty of emergency measures adjustment for each candidate scenario relative to the current scenario; The candidate scenario with the least difficulty in adjusting emergency measures is used as the matching scenario corresponding to the current scenario; The calculating the difficulty of adjusting the emergency measures of each candidate scenario relative to the current scenario based on the scenario information of each candidate scenario and the current scenario includes: Determine the difference in scenario scale, risk level, and risk stage between a first candidate scenario and the current scenario; wherein the first candidate scenario is any candidate scenario; Based on the scene purpose and scene category of the current scene, determine the scene scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scene in a preset difficulty coefficient comparison table; The emergency measures adjustment difficulty score corresponding to the first candidate scenario is calculated based on the scenario scale difference, risk level difference, risk stage difference, scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scenario.

2. The intelligent matching method for multi-scenario emergency measures according to claim 1 is characterized in that: Before determining the scene scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scene in a preset difficulty coefficient comparison table based on the scene purpose and scene category of the current scene, the method further includes: Based on the hierarchical analysis method, a comparison table is constructed for scenario usage, risk category, difficulty coefficient of scenario scale difference, difficulty coefficient of risk level difference and difficulty coefficient of risk stage difference.

3. The intelligent matching method for multi-scenario emergency measures according to claim 1 is characterized in that: The method of matching the current scene with the historical scenes in the emergency measures database based on the scene information of the current scene to obtain a matching scene similar to the current scene also includes: If there is no historical scenario with the same scenario purpose and risk category, determine the difference in scenario purpose and risk category between each historical scenario and the current scenario; Based on the differences in scene usage and risk categories between each historical scene and the current scene, the scene usage difference effect reduction coefficient and the risk category difference effect reduction coefficient of each historical scene are determined in a preset effect reduction coefficient comparison table; For each historical scenario, based on the emergency measures effect evaluation score of the historical scenario, the scenario usage difference effect reduction coefficient, and the risk category difference effect reduction coefficient, determine the estimated effect evaluation score of applying the emergency measures of the historical scenario to the current scenario; Based on the estimated effect evaluation scores of each historical scene, a matching scene corresponding to the current scene is selected from each historical scene.

4. The intelligent matching method for multi-scenario emergency measures according to claim 1 is characterized in that: The scene information difference includes scene scale difference, risk level difference and risk stage difference; the emergency measures corresponding to the matching scene are adjusted based on the scene information difference between the current scene and the matching scene to obtain the emergency measures corresponding to the current scene, including: Based on the difference in risk level and scenario scale between the current scenario and the matching scenario, the intensity of the emergency measures corresponding to the matching scenario is adjusted.

5. The intelligent matching method for multi-scenario emergency measures according to claim 1 is characterized in that: Before matching the current scene with the historical scenes in the emergency measures database based on the scene information of the current scene to obtain a matching scene similar to the current scene, the method further includes: Obtaining scene information, emergency measures, and effect evaluation of a first historical scene; wherein the first historical scene is any historical scene; Determining, based on the branches of each evaluation dimension in the effect evaluation, an optimization dimension and an optimization range of the emergency measures corresponding to the first historical scenario; Optimizing the emergency measures corresponding to the first historical scenario based on the optimization dimension and optimization range of the emergency measures corresponding to the first historical scenario to obtain optimized emergency measures and optimized effect evaluation; The scene information of the first historical scene, the optimized emergency measures and the optimized effect evaluation are stored in the emergency measures database.

6. An intelligent matching device for multi-scenario emergency measures, characterized in that: include: An acquisition module, used to acquire scene information of the current scene; wherein the scene information includes scene purpose, scene scale, risk category, risk level and risk stage; A matching module, used to match the current scene with the historical scenes in the emergency measures database based on the scene information of the current scene, to obtain a matching scene similar to the current scene; wherein the emergency measures database stores scene information and emergency measures of multiple historical scenes; An adjustment module, configured to adjust the emergency measures corresponding to the matching scene based on the difference in scene information between the current scene and the matching scene, so as to obtain the emergency measures corresponding to the current scene; The matching module is specifically used for: Select historical scenarios with the same scenario usage and risk category as the current scenario from the historical scenarios in the emergency measures database as candidate scenarios; Based on the scenario information of each candidate scenario and the current scenario, calculating the difficulty of emergency measures adjustment for each candidate scenario relative to the current scenario; The candidate scenario with the least difficulty in adjusting emergency measures is used as the matching scenario corresponding to the current scenario; The matching module is specifically used for: Determine the difference in scenario scale, risk level, and risk stage between a first candidate scenario and the current scenario; wherein the first candidate scenario is any candidate scenario; Based on the scene purpose and scene category of the current scene, determine the scene scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scene in a preset difficulty coefficient comparison table; The emergency measures adjustment difficulty score corresponding to the first candidate scenario is calculated based on the scenario scale difference, risk level difference, risk stage difference, scenario scale difference difficulty coefficient, risk level difference difficulty coefficient and risk stage difference difficulty coefficient corresponding to the first candidate scenario.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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