A Big Data Intelligent Decision Analysis Method and System Based on Machine Learning

Abstract

The present invention relates to the technical field of data analysis, and particularly relates to a big data intelligent decision-making analysis method and system based on machine learning, including an interaction layer, an analysis layer and a decision-making layer; the real-time location information of the user and the building parameters in the area where the user is located are captured through the interaction layer, and the earthquake information is synchronously collected in real time based on the interaction layer. The analysis layer receives the earthquake information collected in real time by the interaction layer. The present invention can combine the real-time location information of the user and the earthquake information to bring better earthquake avoidance and maintenance effects to the user, ensure that unorganized user groups can use the data provided by the system to evacuate from the house more orderly when an earthquake occurs, effectively guarantee the escape probability of the users in the house when an earthquake occurs, and when the users in the house cannot escape, at the same time, provide data support for rescue personnel in the form of location information feedback, so as to facilitate the more efficient carrying out of rescue work and further provide safety guarantee for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular, to a big data intelligent decision-making analysis method and system based on machine learning. Background Art

[0002] An earthquake, also known as ground motion or ground vibration, is a natural phenomenon in which vibrations are caused during the rapid release of energy in the earth's crust, and seismic waves are generated during this period.

[0003] Earthquakes are often accompanied by the collapse of houses, which poses a great threat to the lives of the residents inside the houses. At present, with the development of technology, there are already earthquake prediction technologies to assist the residents inside the houses to evacuate and escape in advance, but:

[0004] (1) When some residents are escaping, they may lose or reduce their ability to distinguish directions and correctly determine the escape target area due to nervousness and emotional excitement. As a result, the escape probability of the residents is affected to a certain extent during an earthquake;

[0005] (2) Residents who do not have the conditions to escape are buried by the collapsed house walls caused by the earthquake. When waiting for rescue, the vast majority rely on search and rescue dogs or scientific and technological equipment for passive search and rescue, and the trapped residents do not have the conditions for active rescue. Summary of the Invention

[0006] In view of the above-mentioned drawbacks of the prior art, the present invention provides a big data intelligent decision-making analysis method and system based on machine learning, which solves the technical problems proposed in the above background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0008] In a first aspect, a big data intelligent decision-making analysis system based on machine learning includes an interaction layer, an analysis layer, and a decision-making layer;

[0009] The real-time location information of the user and the building parameters of the area where the user is located are captured through the interaction layer, and the earthquake information is synchronously collected in real time based on the interaction layer. The analysis layer receives the earthquake information collected in real time by the interaction layer, and triggers an operation to analyze the safe area based on the earthquake information in the building parameters of the area where the user is located. The decision-making layer receives in real time the safe area analyzed in the analysis layer, further configures the safe area based on the real-time location information of the user captured in the interaction layer, and then feeds back the coordinates of the configured safe area to the interaction layer;

[0010] The analysis layer includes a receiving module, a setting module, and an analysis module. The receiving module is used to receive the real-time location information of the user, the building parameters of the area where the user is located, and the earthquake information collected in real time in the interaction layer. The setting module is used to set a trigger threshold, compare the trigger threshold with the earthquake information received by the receiving module, and trigger the operation of the analysis module based on the comparison result. The analysis module is used to obtain the building parameters of the area where the user is located received by the receiving module, and analyze the safe area based on the building parameters of the area where the user is located;

[0011] In the analysis module, a safe area analysis logic is set. During the operation stage of the analysis module, based on the regional building parameters, open areas are identified, and then the safe area analysis logic is further applied to analyze each identified open area, and then the analysis results are used to confirm the safe areas in the identified open areas;

[0012] Among them, the safe areas confirmed by the analysis module are fed back to the decision-making layer in real time, and when feeding back to the decision-making layer, the specification parameters of the safe areas are synchronously fed back to the decision-making layer in mutual configuration with the corresponding safe areas.

[0013] Furthermore, the interaction layer includes a capturing module, a collecting module, and a storage module. The capturing module is used to capture the real-time location information of the user and the building parameters of the area where the user is located. The collecting module is used to collect earthquake information in real time. The storage module is used to receive the real-time location information of the user captured by the capturing module and the building parameters of the area where the user is located, construct a regional building model based on the building parameters of the area where the user is located, and store the captured real-time location information of the user and the regional building model;

[0014] Among them, the capturing module and the collecting module are installed in the mobile devices held by each user of the system service in the format of a software APP. The collecting module collects earthquake information in real time through the network in the mobile device held by the user. The earthquake information includes: the earthquake source, the earthquake source magnitude, the estimated magnitude, the earthquake arrival time, and the earthquake end time. The estimated magnitude is the magnitude when the vibration generated by the earthquake source propagates to the location where the user is located.

[0015] Furthermore, after obtaining the user's real-time location information, the capture module confirms the area range where the user is located based on the location information of all users, and further captures the building parameters within the area where the user is located. The building parameters include: the coordinates of the building's location, the coordinates of the roads connecting the buildings, the length, width, and height of the building's exterior facade, and the material of the building's load-bearing structure. When constructing the regional building model, the storage module uses the coordinates of the building's location, the coordinates of the roads connecting the buildings, and the length, width, and height of the building's exterior facade to complete the construction of the regional building model. The acquisition module runs continuously to obtain the user's real-time location information, and further expands the regional building model based on the user's real-time location information. Before the decision layer runs, only the latest captured user real-time location information is stored in the storage module. After the decision layer runs, the user's real-time location information continuously captured by the capture module is continuously stored.

[0016] Furthermore, among the user's real-time location information, the building parameters of the area where the user is located, and the earthquake information received by the receiving module, the user's real-time location information is sourced from the storage module, the building parameters of the area where the user is located are replaced by the regional building model stored in the storage module, and the earthquake information is sourced from the acquisition module in the interaction layer. The trigger threshold set in the setting module is the safety magnitude. When comparing the trigger threshold with the earthquake information received by the receiving module in the setting module, the earthquake information received by the receiving module used is the estimated magnitude;

[0017] Among them, the trigger threshold is expressed as: , where x is the safety magnitude. If the estimated magnitude is not within the trigger threshold, the analysis module is triggered to run; otherwise, the receiving module is jumped to run.

[0018] Furthermore, during the operation stage of the analysis module, when obtaining the building parameters of the area where the user is located and analyzing the safe area based on the building parameters of the area where the user is located, the regional building model is synchronously used instead of the building parameters of the area where the user is located. The safe area analysis logic set in the analysis module is expressed as: ;

[0019] In the formula: is the safety score for the open area; is the set of adjacent buildings in the open area; is a constant; is the area of the similar exterior facade between the i-th adjacent building in the open area and the open area; is the ground contact side length of the similar exterior facade between the i-th adjacent building in the open area and the open area; is the relative in the open area is the maximum span in the parallel direction; is the maximum span of the open area; is the total number of adjacent buildings in the open area; is the total number of roads adjacent to the open area; is the safety factor of the i-th building adjacent to the open area;

[0020] Among them, the constant The value range is: , and it follows the setting logic that the higher the building adjacent to the open area, the larger the value of the constant , and vice versa, the smaller the value of the constant . The building safety factor is set based on the material of the building's load-bearing structure. The better the seismic performance of the building's load-bearing structure material, the larger the value of the building safety factor, and vice versa, the smaller the value of the building safety factor. The value range of the building safety factor is: . The higher the safety score of the open area, the less safe the open area is, and vice versa, the safer the open area is.

[0021] Furthermore, in the stage when the analysis module identifies the open area, it traverses the regional building model, identifies the areas where all roads and buildings are located in the regional building model, and further divides and discards the areas where all roads and buildings are located in the regional building model. The remaining area is the open area identified by the analysis module;

[0022] Among them, a safety area determination threshold is set in the analysis module. After obtaining the safety score of the open area, the safety area determination threshold is further applied to compare with the safety score of the open area. Based on the safety area determination threshold, it is determined whether each open area is a safe area. The specification parameters of the safe area are obtained from the regional building model. The specification parameters of the safe area include: the boundary coordinates of the safe area and the area of the safe area.

[0023] Furthermore, the decision-making layer includes a configuration module, a transmission module, and a positioning module. The configuration module is used to receive the safe area and the specification parameters of the safe area analyzed in the analysis layer, and the real-time position information of the user stored in the storage module in the interaction layer, set the configuration ratio of the safe area and the user, and complete the configuration of the user and the safe area based on the configuration ratio of the safe area and the user. The transmission module is used to receive the configuration result of the user and the safe area in the configuration module and feedback the configuration result to the mobile device held by the user. The positioning module is used to obtain the position information of the user captured in the interaction layer in real time;

[0024] Among them, the configuration ratio of the safe area and the user set in the configuration module is expressed as: , It represents q users placed per y square meters. When the configuration module performs the configuration of users and safety areas based on the safety area and the user configuration ratio, the application safety area specification parameters and the safety area and user configuration ratio are used to obtain the total number of configurable users in the safety area. Further, based on the user's real-time location information and the total number of configurable users in the safety area, users are configured for the safety area. And when configuring users for the safety area based on the user's real-time location information, the closer the user is to the safety area, the higher the priority for configuration.

[0025] Furthermore, the positioning module runs to receive the earthquake information collected in the interaction layer in real time, obtains the earthquake end time in the earthquake information, refreshes the system operation after the earthquake end time arrives, and controls the operation of the capture module in the interaction layer of the system to compare the user's real-time location information captured by the operation of the capture module with the configuration result of users and safety areas in the configuration module;

[0026] For the users corresponding to the real-time location information of users not in the safety area, the real-time location information is obtained and stored in real time by the positioning module in the capture module; for the users corresponding to the real-time location information of users in the safety area, after the real-time location information is captured by the positioning module controlling the operation of the capture module, the real-time location information is obtained and stored by the positioning module in the capture module, and then it ends.

[0027] Furthermore, the receiving module is connected to a storage module through wireless network interaction, the storage module is electrically connected to a collection module and a capture module through a medium, the receiving module is electrically connected to a setting module and an analysis module through a medium, the analysis module is connected to a configuration module through wireless network interaction, and the configuration module is electrically connected to a transmission module and a positioning module through a medium.

[0028] In a second aspect, a big data intelligent decision-making analysis method based on machine learning includes the following steps:

[0029] S1: Obtain the user's real-time location information, set the area where the user is located based on the user's real-time location information, capture the building parameters in the area where the user is located, and construct a regional building model based on the building parameters in the area where the user is located;

[0030] S2: Apply the regional building model to identify the safety area in the area where the user is located;

[0031] S21: The setting stage of the safety area identification logic in the area where the user is located;

[0032] S3: Collect earthquake information, and configure a safety area for the user based on the user's real-time location information when an earthquake occurs;

[0033] S31: The configuration logic setting stage for the mutual configuration of users and safety areas;

[0034] S4: Determine whether the user has reached the safe area based on the user's real-time location information;

[0035] S5: If the result of S4 is yes, obtain and store the user's real-time location information;

[0036] S6: If the result of S4 is no, continuously obtain and store the user's real-time location information.

[0037] Adopting the technical solution provided by the present invention, compared with the known public technologies, it has the following beneficial effects:

[0038] 1. The present invention provides a big data intelligent decision-making analysis system based on machine learning. During the operation of the system, it can combine the user's real-time location information and earthquake information to bring better earthquake avoidance and maintenance effects to users, ensuring that unorganized user groups can withdraw from the house more orderly with the data provided by the system when an earthquake occurs, effectively guaranteeing the escape probability of users in the house when an earthquake occurs. And when the users in the house cannot escape, at the same time, in the form of feedback of location information, it provides data support for rescue personnel to facilitate more efficient rescue work and further provides safety guarantee for users.

[0039] 2. During the operation of the system in the present invention, it is less affected by the user group and can continuously expand the functional operation of the system based on the expansion of the user group, ensuring that the system can bring centralized earthquake response strategies and safety maintenance to more users. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 It is a schematic structural diagram of a big data intelligent decision-making analysis system based on machine learning;

[0042] Figure 2 It is a schematic flowchart of a big data intelligent decision-making analysis method based on machine learning;

[0043] Figure 3 It is a schematic diagram of the parameter value source in the safety area analysis logic applied in the present invention;

[0044] The reference numerals in the figure respectively represent: 1. Adjacent buildings in the open area; 2. Open area; 3. Exterior facade of the building close to the open area; 4. Ground contact side length; 5. Maximum span in the direction parallel to the corresponding line of the ground contact side length in the open area; 6. Maximum span of the open area. Detailed implementation mode

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] The present invention will be further described below with reference to the embodiments. Embodiment 1

[0047] A big data intelligent decision analysis system based on machine learning in this embodiment, as Figure 1 shown, includes an interaction layer, an analysis layer, and a decision layer;

[0048] The real-time location information of the user and the building parameters of the area where the user is located are captured through the interaction layer, and the earthquake information is synchronously collected in real time based on the interaction layer. The analysis layer receives the earthquake information collected in real time by the interaction layer, triggers the operation of analyzing the safe area based on the earthquake information in the building parameters of the area where the user is located, the decision layer receives in real time the safe area analyzed in the analysis layer, further configures the safe area based on the real-time location information of the user captured in the interaction layer, and then feeds back to the interaction layer with the coordinates of the configured safe area;

[0049] The analysis layer includes a receiving module, a setting module, and an analysis module. The receiving module is used to receive the real-time location information of the user, the building parameters of the area where the user is located, and the earthquake information collected in real time by the interaction layer. The setting module is used to set a trigger threshold, compare the trigger threshold with the earthquake information received by the receiving module, and trigger the operation of the analysis module based on the comparison result. The analysis module is used to obtain the building parameters of the area where the user is located received by the receiving module, and analyze the safe area based on the building parameters of the area where the user is located;

[0050] A safe area analysis logic is set in the analysis module. During the operation stage of the analysis module, the open area is identified based on the area building parameters, and then the safe area analysis logic is further applied to analyze each identified open area, and then the safe area in the identified open area is confirmed with the analysis result;

[0051] Among them, the safety area confirmed in the analysis module feeds back to the decision-making layer in real time. When feeding back to the decision-making layer, the specification parameters of the safety area are synchronously fed back to the decision-making layer in mutual configuration with the corresponding safety area.

[0052] The interaction layer includes a capture module, a collection module, and a storage module. The capture module is used to capture the real-time location information of the user and the building parameters of the area where the user is located. The collection module is used to collect earthquake information in real time. The storage module is used to receive the real-time location information of the user captured by the capture module and the building parameters of the area where the user is located, construct a regional building model based on the building parameters of the area where the user is located, and store the captured real-time location information of the user and the regional building model.

[0053] Among them, the capture module and the collection module are installed in the mobile devices held by each user of the system service in the format of a software APP. The collection module collects earthquake information in real time through the network in the mobile device held by the user. The earthquake information includes: the earthquake source, the earthquake source magnitude, the estimated magnitude, the earthquake arrival time, and the earthquake end time. The estimated magnitude is the magnitude when the vibration generated by the earthquake source propagates to the location where the user is located.

[0054] During the operation stage of the analysis module, when obtaining the building parameters of the area where the user is located and analyzing the safety area based on the building parameters of the area where the user is located, the regional building model is synchronously applied to replace the building parameters of the area where the user is located. The safety area analysis logic set in the analysis module is expressed as: ;

[0055] In the formula: is the safety score of the open area; is the set of adjacent buildings in the open area; is a constant; is the area of the similar outer facade between the i-th adjacent building in the open area and the open area; is the ground contact length of the similar outer facade between the i-th adjacent building in the open area and the open area; is relative in the open area is the maximum span in the parallel direction; is the maximum span of the open area; is the total number of adjacent buildings in the open area; is the total number of adjacent roads in the open area; is the safety factor of the i-th adjacent building in the open area;

[0056] Among them, the constant The value range is: , and it follows that the higher the adjacent building in the open area, the larger the value of the constant , and vice versa, then the constant The smaller the value of the setting logic, the building safety factor is set based on the material of the building's load-bearing structure. The better the seismic performance of the building's load-bearing structure material, the larger the value of the building safety factor; conversely, the smaller the value of the building safety factor. The value range of the building safety factor is: , the safety score of the open area The higher it is, the less safe the open area is; conversely, the safer the open area is.

[0057] The decision-making layer includes a configuration module, a transmission module, and a positioning module. The configuration module is used to receive the analyzed safe areas and the specification parameters of the safe areas in the analysis layer, and the real-time location information of the user stored in the storage module in the interaction layer, set the configuration ratio of the safe area and the user, and complete the configuration of the user and the safe area based on the configuration ratio of the safe area and the user. The transmission module is used to receive the configuration result of the user and the safe area in the configuration module and feedback the configuration result to the mobile device held by the user. The positioning module is used to obtain the real-time location information of the user captured in the interaction layer in real time.

[0058] Among them, the configuration ratio of the safe area and the user set in the configuration module is expressed as: , It represents q users placed per y square meters. When the configuration module executes the configuration of the user and the safe area based on the configuration ratio of the safe area and the user, it applies the specification parameters of the safe area and the configuration ratio of the safe area and the user to obtain the total number of users that can be configured in the safe area. Further, based on the real-time location information of the user and the total number of users that can be configured in the safe area, it configures users for the safe area. And when configuring users for the safe area based on the real-time location information of the user, it follows that the closer the user is to the safe area, the more preferentially it is configured.

[0059] The receiving module is connected to the storage module through wireless network interaction. The storage module is electrically connected to the acquisition module and the capture module through a medium. The receiving module is electrically connected to the setting module and the analysis module through a medium. The analysis module is connected to the configuration module through wireless network interaction. The configuration module is electrically connected to the transmission module and the positioning module through a medium.

[0060] In this embodiment, the capture module runs to capture the real-time location information of the user and the building parameters of the area where the user is located. The acquisition module synchronously acquires the seismic information in real time. The storage module runs later to receive the real-time location information of the user captured by the capture module and the building parameters of the area where the user is located, constructs a regional building model based on the building parameters of the area where the user is located, stores the captured real-time location information of the user and the regional building model, and then the receiving module receives the real-time location information of the user, the building parameters of the area where the user is located, and the seismic information collected in real time in the interaction layer. The setting module synchronously sets a trigger threshold, compares the trigger threshold with the seismic information received by the receiving module, triggers the operation of the analysis module based on the comparison result. The analysis module further obtains the building parameters of the area where the user is located received by the receiving module, analyzes the safe area based on the building parameters of the area where the user is located, and finally the configuration module receives the safe area and the safe area specification parameters analyzed in the analysis layer, and the real-time location information of the user stored in the storage module in the interaction layer, sets the ratio of the safe area to the user configuration, and completes the configuration of the user and the safe area based on the ratio of the safe area to the user configuration. The transmission module receives the configuration result of the user and the safe area in the configuration module in real time, feeds back the configuration result to the mobile device held by the user, and the positioning module obtains the location information of the user captured in the interaction layer in real time;

[0061] Based on the above system, it effectively guarantees the life safety of the users of the house residents during an earthquake, and provides a designated escape target area for the users of the house residents, greatly reducing the safety threat to the users of the house residents caused by the earthquake;

[0062] See Figure 3 As shown, this figure further shows the safety area analysis logic set in the analysis module. In the specific application stage, the source of the parameters applied in the logic further ensures the stable implementation of the analysis logic in the system. Embodiment 2

[0063] At the specific implementation level, on the basis of Embodiment 1, this embodiment further specifically describes a big data intelligent decision-making analysis system based on machine learning in Embodiment 1 with reference to Figure 1 As shown:

[0064] After the capture module obtains the user's real-time location information, it confirms the area range where the user is located based on the location information of all users, and further captures the building parameters within the area where the user is located. The building parameters include: the coordinates of the building's location, the coordinates of the roads connecting the buildings, the length, width, and height of the building's exterior facade, and the material of the building's load-bearing structure. When the storage module constructs the regional building model, it applies the coordinates of the building's location, the coordinates of the roads connecting the buildings, and the length, width, and height of the building's exterior facade to complete the construction of the regional building model. The acquisition module runs continuously to obtain the user's real-time location information, and further expands the regional building model based on the user's real-time location information. The user's real-time location information stored in the storage module only stores the latest captured user's real-time location information before the decision layer runs, and continuously stores the user's real-time location information continuously captured by the capture module after the decision layer runs.

[0065] Through the above settings, the interaction layer in the system is further provided to run according to the specified operation logic, ensuring the stable operation of the interaction layer in the system and providing the first data support for the operation of the analysis layer and the decision layer in the system.

[0066] Such as Figure 1 As shown, among the user's real-time location information, the building parameters of the area where the user is located, and the earthquake information received by the receiving module, the user's real-time location information comes from the storage module, the building parameters of the area where the user is located are replaced by the regional building model stored in the storage module, and the earthquake information comes from the acquisition module in the interaction layer. The trigger threshold set in the setting module is the safety magnitude. When the setting module compares the trigger threshold with the earthquake information received by the receiving module, the earthquake information received by the receiving module applied is the estimated magnitude;

[0067] Among them, the trigger threshold is expressed as: , where x is the safety magnitude. If the estimated magnitude is not within the trigger threshold, the analysis module is triggered to run; otherwise, the receiving module is jumped to run;

[0068] In the stage when the analysis module identifies the open area, it traverses the regional building model, identifies all the areas where the roads are located and the areas where the buildings are located in the regional building model, and further divides and discards all the areas where the roads are located and the areas where the buildings are located in the regional building model. The remaining area is the open area identified by the analysis module;

[0069] Among them, a safety area determination threshold is set in the analysis module, and the safety score of the open area After obtaining, the safety area determination threshold is further applied to compare with the safety score of the open area Based on the safety area determination threshold, it is determined whether each open area is a safe area. The specification parameters of the safe area are obtained from the regional building model. The specification parameters of the safe area include: the boundary coordinates of the safe area and the area of the safe area.

[0070] Through the above settings, setting logic limitations are provided for the trigger thresholds set by the setting module in the analysis layer, ensuring that the analysis layer in the system provides linkage conditions for the upper and lower operation layers with specified trigger logic, making the system operation more stable.

[0071] As Figure 1 shown, the positioning module runs to receive the seismic information collected in the interaction layer in real time, obtains the earthquake end time in the seismic information, refreshes the system operation after the earthquake end time arrives, controls the operation of the capture module in the interaction layer of the system, and compares the real-time position information of the user captured by the operation of the capture module with the configuration result of the user and the safe area in the configuration module;

[0072] For the user corresponding to the real-time position information of the user not in the safe area, the real-time position information is obtained and stored in real time by the positioning module in the capture module; for the user corresponding to the real-time position information of the user in the safe area, after the real-time position information is captured by the positioning module controlling the operation of the capture module, the real-time position information is obtained and stored by the positioning module in the capture module, and then it ends.

[0073] By limiting the operation logic of the positioning module, further support for user data at the system end is provided, enabling users who have not completed earthquake evacuation based on this system to still provide efficient rescue operation conditions for rescue personnel with the position information fed back by the system operation. Embodiment 3

[0074] At the specific implementation level, on the basis of Embodiment 1, this embodiment further specifically describes a big data intelligent decision-making analysis system based on machine learning in Embodiment 1 with reference to Figure 2 shown as follows:

[0075] A big data intelligent decision-making analysis method based on machine learning includes the following steps:

[0076] S1: Obtain the real-time position information of the user, set the area where the user is located based on the real-time position information of the user, capture the building parameters in the area where the user is located, and construct a regional building model based on the building parameters in the area where the user is located;

[0077] S2: Apply the regional building model to identify the safe area in the area where the user is located;

[0078] S21: The setting stage of the safe area identification logic in the area where the user is located;

[0079] S3: Collect seismic information, and configure a safe area for the user based on the real-time position information of the user when an earthquake occurs;

[0080] S31: The configuration logic setting stage for the mutual configuration of the user and the safe area;

[0081] S4: Determine whether the user has reached the safe area based on the user's real-time location information;

[0082] S5: If the determination result of S4 is yes, obtain and store the user's real-time location information;

[0083] S6: If the determination result of S4 is no, continuously obtain and store the user's real-time location information.

[0084] In summary, in the above embodiments, the system can combine the user's real-time location information and earthquake information during operation, bringing a better earthquake avoidance and maintenance effect to the user, ensuring that an unorganized user group can withdraw from the house more orderly with the data provided by the system when an earthquake occurs, effectively guaranteeing the escape probability of the users in the house when an earthquake occurs. And when the users in the house cannot escape, at the same time, in the form of location information feedback, provide data support for rescue personnel to facilitate more efficient rescue work and further provide safety guarantee for the users; and during the operation of this system, it is less affected by the user group and can continuously expand the functionality of the system operation based on the expansion of the user group to ensure that the system can bring centralized earthquake response strategies and safety maintenance to more users.

[0085] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A big data intelligent decision-making analysis system based on machine learning, characterized in that, It includes an interaction layer, an analysis layer, and a decision-making layer; The interaction layer includes a capture module, a collection module, and a storage module. The capture module is used to capture the real-time location information of the user and the building parameters of the area where the user is located. The collection module is used to collect earthquake information in real time. The storage module is used to receive the real-time location information of the user captured by the capture module and the building parameters of the area where the user is located, construct a regional building model based on the building parameters of the area where the user is located, and store the captured real-time location information of the user and the regional building model; The analysis layer includes a receiving module, a setting module, and an analysis module. The receiving module is used to receive the real-time location information of the user, the building parameters of the area where the user is located, and the earthquake information collected in real time in the interaction layer. The setting module is used to set a trigger threshold, compare the trigger threshold with the earthquake information received by the receiving module, and trigger the operation of the analysis module based on the comparison result. The analysis module is used to obtain the building parameters of the area where the user is located received by the receiving module. When analyzing the safe area based on the building parameters of the area where the user is located, the regional building model is synchronously applied to replace the building parameters of the area where the user is located; During the operation stage of the analysis module, the open areas are identified based on the building parameters of the area where the user is located, and each identified open area is analyzed using the safe area analysis logic, and then the safe area in the identified open areas is confirmed based on the analysis results. The safe area analysis logic is expressed as: Where: g safe is the safety score of the open area; n is the set of adjacent buildings in the open area; s i is the area of the i-th building adjacent to the open area that is close to the outer facade of the open area; l is the length of the ground contact side of the i-th building adjacent to the open area that is close to the outer facade of the open area; k max is the maximum span in the direction parallel to l in the open area; d is the maximum span of the open area; n0 is the total number of adjacent buildings in the open area; m0 is the total number of adjacent roads in the open area; the value range of the constant γ is: 0.5 ≤ γ ≤ 1, and it follows the setting logic that the higher the adjacent buildings in the open area, the larger the value of the constant γ, and vice versa, the smaller the value of the constant γ; δ i is the safety factor of the i-th building adjacent to the open area. The better the seismic performance of the building load-bearing structure material, the larger the value of the building safety factor, and vice versa, the smaller the value of the building safety factor. The value range of the building safety factor is: 0.75 < δ ≤ 1. The higher the safety score g safe of the open area, the less safe the open area is, and vice versa, the safer the open area is. After obtaining the safety score g safe of the open area, it is determined whether each open area is a safe area based on the safety area determination threshold; The safe area confirmed by the analysis module in real time is fed back to the decision-making layer, and when feeding back to the decision-making layer, the specification parameters of the safe area are synchronously fed back to the decision-making layer; The decision-making layer receives the safe area analyzed in the analysis layer in real time, configures the safe area based on the real-time location information of the user captured in the interaction layer, and then feeds back the coordinates of the configured safe area to the interaction layer.

2. The big data intelligent decision-making analysis system based on machine learning according to claim 1, characterized in that The capture module and the collection module are installed in the mobile devices held by each user of the system service in the format of a software APP. The collection module collects earthquake information in real time through the network in the mobile device held by the user. The earthquake information includes: the earthquake source, the earthquake source magnitude, the estimated magnitude, the earthquake arrival time, and the earthquake end time. The estimated magnitude is the magnitude when the vibration generated by the earthquake source propagates to the location where the user is located.

3. A big data intelligent decision-making analysis system based on machine learning according to claim 2, characterized in that, After obtaining the real-time location information of the user, the capture module confirms the range of the area where the user is located based on the location information of all users, and further captures the building parameters within the range of the area where the user is located. The building parameters include: the coordinates of the location where the building is located, the coordinates of the roads connecting the buildings, the length, width, and height of the building facade, and the material of the building load-bearing structure. When constructing the regional building model, the storage module uses the coordinates of the location where the building is located, the coordinates of the roads connecting the buildings, and the length, width, and height of the building facade to complete the construction of the regional building model. The collection module continuously obtains the real-time location information of the user and further expands the regional building model based on the real-time location information of the user. Before the decision-making layer operates, only the latest captured real-time location information of the user is stored in the storage module. After the decision-making layer operates, the real-time location information of the user continuously captured by the capture module is continuously stored.

4. A big data intelligent decision-making analysis system based on machine learning according to claim 1, characterized in that, The analysis layer further includes a receiving module and a setting module. Among the user's real-time location information, the building parameters of the area where the user is located, and the earthquake information received by the receiving module, the user's real-time location information is sourced from the storage module, the building parameters of the area where the user is located are replaced by the regional building model stored in the storage module, the earthquake information is sourced from the acquisition module in the interaction layer, and the trigger threshold set in the setting module is the safety magnitude. When the trigger threshold set in the setting module is compared with the earthquake information received by the receiving module, the earthquake information received by the receiving module used is the estimated magnitude; Among them, the trigger threshold is expressed as: (0, x), where x is the safety magnitude. If the estimated magnitude is not within the trigger threshold, the analysis module is triggered to run; otherwise, the receiving module is jumped to run.

5. A big data intelligent decision-making analysis system based on machine learning according to claim 1, characterized in that, In the stage where the analysis module identifies the open area, it traverses the regional building model, identifies all the areas where roads are located and the areas where buildings are located in the regional building model, and further divides and discards all the areas where roads are located and the areas where buildings are located in the regional building model. The remaining area is the open area identified by the analysis module; The specification parameters of the safe area are obtained from the regional building model, and the specification parameters of the safe area include: the boundary coordinates of the safe area and the area of the safe area.

6. The big data intelligent decision-making analysis system based on machine learning according to claim 1, characterized in that, The decision-making layer includes a configuration module, a transmission module, and a positioning module. The configuration module is used to receive the safe area and the specification parameters of the safe area analyzed in the analysis layer, as well as the user's real-time location information stored in the storage module in the interaction layer, set the configuration ratio between the safe area and the user, and complete the configuration of the user and the safe area based on the configuration ratio between the safe area and the user. The transmission module is used to receive the configuration result of the user and the safe area in the configuration module and feedback the configuration result to the mobile device held by the user. The positioning module is used to obtain the user's location information captured in the interaction layer in real time; Among them, the safety area set in the configuration module and the user configuration ratio are expressed as: It represents q users placed per y square meters. When the configuration module executes the configuration of users and the safety area based on the safety area and the user configuration ratio, it applies the safety area specification parameters and the safety area and the user configuration ratio to obtain the total number of users that can be configured in the safety area. Further, based on the real-time location information of the users and the total number of users that can be configured in the safety area, it configures users for the safety area. When configuring users for the safety area based on the real-time location information of the users, it follows the principle that the closer the user is to the safety area, the higher the priority of configuration.

7. An intelligent decision-making analysis system for big data based on machine learning according to claim 6, characterized in that The positioning module runs to receive the earthquake information collected in the interaction layer in real time, obtains the earthquake end time in the earthquake information, refreshes the system operation after the earthquake end time arrives, and controls the operation of the capture module in the interaction layer of the control system to compare the user's real-time location information captured by the capture module with the configuration result of the user and the safe area in the configuration module; For the user corresponding to the user's real-time location information not in the safe area, the real-time location information is obtained and stored in real time by the positioning module in the capture module; for the user corresponding to the user's real-time location information in the safe area, after the real-time location information is captured by the capture module controlled by the positioning module, the real-time location information is obtained and stored by the positioning module in the capture module, and then it ends.

8. A big data intelligent decision-making analysis system based on machine learning according to claim 1, characterized in that The receiving module is connected to the storage module through wireless network interaction. The storage module is electrically connected to the acquisition module and the capture module through a medium. The receiving module is electrically connected to the setting module and the analysis module through a medium. The analysis module is connected to the configuration module through wireless network interaction. The configuration module is electrically connected to the transmission module and the positioning module through a medium.

9. A big data intelligent decision-making analysis method based on machine learning, which is an implementation method of a big data intelligent decision-making analysis system based on machine learning as described in any one of claims 1-8, characterized in that, It includes the following steps: S1: Obtain the user's real-time location information, set the area where the user is located based on the user's real-time location information, capture the building parameters in the area where the user is located, and construct a regional building model based on the building parameters in the area where the user is located; S2: Apply the regional building model to identify the safe area in the area where the user is located; S21: The setting stage of the safe area identification logic in the area where the user is located; S3: Collect earthquake information, and configure a safe area for the user based on the user's real-time location information when an earthquake occurs; S31: The configuration logic setting stage for the mutual configuration of the user and the safe area; S4: Determine whether the user has reached the safe area based on the user's real-time location information; S5: If the determination result of S4 is yes, obtain and store the user's real-time location information; S6: If the determination result of S4 is no, continuously obtain and store the user's real-time location information.

Images