CN114677061B - GIS-based emergency hierarchical pushing method, system and storage medium - Google Patents
GIS-based emergency hierarchical pushing method, system and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of GIS and geographic disasters, in particular to a GIS-based emergency classified pushing method, a GIS-based emergency classified pushing system and a GIS-based emergency classified pushing storage medium, which can obtain a plan classified pushing upgrading result by combining a rough screening result of a dynamic rescue plan in first emergency state information and the dynamic plan transformation possibility of each plan state node. Compared with the related technology for carrying out the plan grading promotion upgrading on the first emergency state information characteristic distribution through the emergency grading promotion decision tree, the method and the system improve the accuracy and timeliness of the plan grading promotion upgrading and effectively reduce the system resource consumption of the emergency grading promotion.
Description
Technical Field
The embodiment of the application relates to the technical field of GIS and emergency event processing, in particular to a GIS-based emergency event grading pushing method, a GIS-based emergency event grading pushing system and a storage medium.
Background
The Geographic Information System (GIS) is a specific and very important spatial Information System. The system is a technical system for collecting, storing, managing, operating, analyzing, displaying and describing relevant geographic distribution data in the whole or partial earth surface (including the atmosphere) space under the support of a computer hardware and software system.
GIS belongs to a class of information systems that differ in that it can manipulate and process geo-referenced data. At present, the GIS is widely applied to the fields of geological disaster analysis and emergency event processing, and can reduce personal and property losses of people caused by geological disasters to the greatest extent.
When dealing with emergency such as geographic disaster, it is necessary to promote emergency plans and emergency in a grading manner in order to deal with emergency and uncertain situation as much as possible, but the related plan grading promotion technology has a technical problem of low efficiency.
Disclosure of Invention
In view of this, embodiments of the present application provide a method, a system, and a storage medium for hierarchical promotion of emergency events based on a GIS.
The embodiment of the application provides an emergency hierarchical pushing method based on a GIS (geographic information system), which is applied to an emergency hierarchical pushing system and comprises the following steps:
transmitting the prior geographical disaster emergency plan into an emergency plan upgrading network;
acquiring a first dynamic rescue plan set in the prior geographical disaster emergency plan through the emergency plan upgrading network;
acquiring first emergency state information and first emergency state information characteristic distribution in the prior geographical disaster emergency plan based on the first dynamic rescue plan set through the emergency plan upgrading network, wherein the first emergency state information is corresponding state information of the first dynamic rescue plan set in the prior geographical disaster emergency plan;
the first emergency state information is disassembled into a plurality of plan state events through the emergency plan upgrading network, the plan transformation possibility corresponding to each plan state event in the first emergency state information is identified through the emergency plan upgrading network based on the first emergency state information characteristic distribution, and the dynamic plan transformation possibility of each plan state node in the first emergency state information is obtained through the emergency plan upgrading network, wherein the plan transformation possibility corresponding to the plan state event is the probability that a dynamic rescue plan exists in the plan state event;
and acquiring a plan grading promotion upgrading result based on the plan transformation possibility of each plan state event in the first emergency state information, the information of the first dynamic rescue plan set and the dynamic plan transformation possibility of each plan state node in the first emergency state information.
Optionally, the information of the first dynamic rescue plan set includes a category of the first dynamic rescue plan set, and the obtaining of the plan grading promotion upgrade result based on the plan transformation possibility of each plan state event in the first emergency state information, the information of the first dynamic rescue plan set, and the dynamic plan transformation possibility of each plan state node in the first emergency state information includes:
determining that the state information of the dynamic rescue plan exists in the first emergency state information based on a plan transformation possibility corresponding to each plan state event in the first emergency state information, and determining the category corresponding to the plan state node in the first emergency state information based on the dynamic plan transformation possibility of each plan state node in the first emergency state information, wherein the plan transformation possibility corresponding to the plan state event corresponding to the state information of the dynamic rescue plan exists is greater than a preset probability threshold;
and determining the plan state nodes belonging to the category of the first dynamic rescue plan set in the state information of the existing dynamic rescue plan as the plan grading promotion upgrading result based on the category of each plan state node in the first emergency state information.
Optionally, the obtaining, by the emergency plan upgrading network, the first dynamic rescue plan set in the prior geographic disaster emergency plan includes:
acquiring first static characteristic distribution in the prior geographical disaster emergency plan through the emergency plan upgrading network;
and acquiring a first dynamic rescue plan set in the prior geographical disaster emergency plan based on the first static characteristic distribution through the emergency plan upgrading network.
Optionally, the information of the first dynamic rescue plan set includes rescue plan elements of the first dynamic rescue plan set, and the obtaining, through the emergency plan upgrade network, a dynamic plan transformation possibility of each plan state node in the first emergency state information includes:
dynamically upgrading the first static characteristic distribution through the emergency plan upgrading network to obtain a first dynamic upgrading characteristic distribution corresponding to the plan state node in the prior geographical disaster emergency plan;
and determining a plan transformation possibility corresponding to each plan state node in the first emergency state information in the first dynamic upgrading characteristic distribution based on the rescue plan elements of the first dynamic rescue plan set, and taking the plan transformation possibility as a dynamic plan transformation possibility corresponding to the plan state node in the first emergency state information.
Optionally, the obtaining, by the emergency plan upgrading network, a first dynamic rescue plan set in the prior geographic disaster emergency plan based on the first static feature distribution includes:
acquiring a first to-be-determined rescue plan set in the prior geographical disaster emergency plan based on the first static feature distribution through the emergency plan upgrading network;
acquiring second emergency state information feature distribution in the prior geographical disaster emergency plan based on the first to-be-determined rescue plan set and the first static feature distribution through the emergency plan upgrading network;
and acquiring the first dynamic rescue plan set based on the second emergency state information characteristic distribution through the emergency plan upgrading network.
Optionally, the obtaining, by the emergency plan upgrading network, the first dynamic rescue plan set based on the second emergency state information feature distribution includes:
acquiring rescue plan clustering feature distribution and rescue plan commonality feature distribution corresponding to the second emergency state information feature distribution through the emergency plan upgrading network, wherein the rescue plan clustering feature distribution is used for representing the probability that the first to-be-determined rescue plan set belongs to each category, and the rescue plan commonality feature distribution is used for representing the difference condition of the first dynamic rescue plan set relative to the first to-be-determined rescue plan set;
acquiring information of the first dynamic rescue plan set based on rescue plan clustering feature distribution corresponding to the second emergency state information feature distribution and rescue plan commonality feature distribution corresponding to the second emergency state information feature distribution;
correspondingly, the information of the first dynamic rescue plan set further includes rescue plan elements of the first dynamic rescue plan set and categories of the first dynamic rescue plan set, and the obtaining of the information of the first dynamic rescue plan set based on the rescue plan cluster feature distribution corresponding to the second emergency state information feature distribution and the rescue plan commonality feature distribution corresponding to the second emergency state information feature distribution includes:
performing feature cleaning processing on the rescue plan clustering feature distribution corresponding to the second emergency state information feature distribution to obtain the category of the first dynamic rescue plan set;
and correcting the difference between the rescue plan commonality characteristic distribution corresponding to the second emergency state information characteristic distribution and the rescue plan elements of the first to-be-determined rescue plan set to obtain the rescue plan elements of the first dynamic rescue plan set.
Optionally, before the obtaining, by the emergency plan upgrading network, the first dynamic rescue plan set in the prior geographic disaster emergency plan, the method includes: and training and adjusting parameters of the emergency plan upgrading network.
Optionally, the training and parameter adjustment for the emergency plan upgrading network includes:
transmitting a sample geographical disaster emergency plan into the emergency plan upgrading network;
acquiring second static characteristic distribution of the sample geographical disaster emergency plan through the emergency plan upgrading network;
acquiring second emergency state information and third emergency state information characteristic distribution in the sample geographical disaster emergency plan based on the second static characteristic distribution through the emergency plan upgrading network;
decomposing the second emergency state information into a plurality of plan state events through the emergency plan upgrading network, identifying plan transformation possibility corresponding to each plan state event in the second emergency state information based on third emergency state information characteristic distribution through the emergency plan upgrading network, and dynamically upgrading the second static characteristic distribution through the emergency plan upgrading network to obtain second dynamic upgrading characteristic distribution;
acquiring a first network loss of the emergency plan upgrading network based on a comparison condition between a plan transformation possibility and a first reference transformation possibility corresponding to each plan state event in the second emergency state information, and acquiring a second network loss of the emergency plan upgrading network based on a comparison condition between the second dynamic upgrading feature distribution and a second reference transformation possibility;
improving network parameters of the emergency plan upgrade network based on the first network loss and the second network loss;
wherein the obtaining of the second emergency state information and the third emergency state information feature distribution in the prior geographical disaster emergency plan based on the second static feature distribution through the emergency plan upgrade network includes:
acquiring a second undetermined rescue plan set of the sample geographical disaster emergency plan through the emergency plan upgrading network based on the second static characteristic distribution, and taking state information corresponding to the second undetermined rescue plan set in the sample geographical disaster emergency plan as second emergency state information;
acquiring the third emergency state information feature distribution based on the second pending rescue plan set and the second static feature distribution through the emergency plan upgrading network;
correspondingly, after the third emergency state information feature distribution is acquired through the emergency plan upgrading network based on the second pending rescue plan set and the second static feature distribution, the method includes:
acquiring rescue plan clustering feature distribution and rescue plan commonality feature distribution corresponding to the third emergency state information feature distribution through the emergency plan upgrading network, wherein the rescue plan clustering feature distribution corresponding to the third emergency state information feature distribution is used for representing the probability that the second undetermined rescue plan set belongs to each category, and the rescue plan commonality feature distribution corresponding to the third emergency state information feature distribution is used for representing the difference condition of the second dynamic rescue plan set relative to the second undetermined rescue plan set;
acquiring a third network loss of the emergency plan upgrading network based on a comparison condition between a rescue plan clustering feature distribution corresponding to the third emergency state information feature distribution and a third reference transformation possibility, and acquiring a fourth network loss of the emergency plan upgrading network based on a comparison condition between a rescue plan commonality feature distribution corresponding to the third emergency state information feature distribution and a fourth reference transformation possibility;
improving network parameters of the emergency plan upgrade network based on the third network loss and the fourth network loss.
The embodiment of the application also provides an emergency hierarchical pushing system, which comprises a processor, a network module and a memory; the processor and the memory communicate through the network module, and the processor reads the computer program from the memory and operates to perform the above-described method.
An embodiment of the present application further provides a computer storage medium, where a computer program is stored, and the computer program implements the method when running.
Compared with the prior art, the emergency hierarchical pushing method, system and storage medium based on the GIS provided by the embodiment of the application have the following technical effects: through the mode, the first dynamic rescue plan set in the prior geographical disaster emergency plan is obtained through the emergency plan upgrading network, the first static characteristic distribution and the first emergency state information characteristic distribution of the prior geographical disaster emergency plan are obtained based on the first dynamic rescue plan set, the dynamic rescue plan of plan state evenings is selected for the first emergency state information, accordingly, the dynamic rescue plan in the first emergency state information can be rapidly screened preliminarily, the dynamic plan transformation possibility of each plan state node in the first emergency state information is obtained through the emergency plan upgrading network, and the plan grading promotion upgrading result can be obtained by combining the rough screening result of the dynamic rescue plan in the first emergency state information and the dynamic plan transformation possibility of each plan state node. Compared with the related technology for carrying out the plan grading promotion upgrading on the first emergency state information characteristic distribution through the emergency grading promotion decision tree, the method and the system improve the accuracy and timeliness of the plan grading promotion upgrading, and effectively reduce the system resource consumption of the emergency grading promotion system.
Moreover, because the related art emergency graded-pushing decision tree needs to acquire the probability that each plan transformation possibility in the corresponding first emergency state information feature distribution belongs to each category, the output feature distribution can occupy a large amount of system resources and overhead, and the emergency graded-pushing network only identifies whether a dynamic rescue plan exists in each plan state event, so that the occupation and the overhead of the system resources are effectively reduced, the consumption of the system resources occupied by the geographical disaster emergency plan upgrading can be reduced, the time consumed by the emergency graded-pushing upgrading is reduced, the rapid response degree of the emergency graded-pushing upgrading is improved, and the emergency graded-pushing can be timely applied to various geological disaster emergency events.
In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a block diagram illustrating an emergency pushing system according to an embodiment of the present application.
Fig. 2 is a flowchart of a method for pushing emergency based on GIS in a hierarchical manner according to an embodiment of the present disclosure.
Fig. 3 is a block diagram of a GIS-based emergency grading pushing device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Fig. 1 is a block diagram illustrating an emergency staging system 10 according to an embodiment of the present application. The emergency hierarchical pushing system 10 in the embodiment of the present application may be a server with data storage, transmission, and processing functions, as shown in fig. 1, the emergency hierarchical pushing system 10 includes: memory 11, processor 12, network module 13 and GIS-based emergency staging means 20.
The memory 11, the processor 12 and the network module 13 are electrically connected directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The storage 11 stores a GIS-based emergency hierarchical pushing device 20, the GIS-based emergency hierarchical pushing device 20 includes at least one software function module which can be stored in the storage 11 in the form of software or firmware (firmware), and the processor 12 executes various function applications and data processing by running software programs and modules stored in the storage 11, such as the GIS-based emergency hierarchical pushing device 20 in the embodiment of the present application, so as to implement the GIS-based emergency hierarchical pushing method in the embodiment of the present application.
The Memory 11 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 11 is used for storing a program, and the processor 12 executes the program after receiving an execution instruction.
The processor 12 may be an integrated circuit chip having data processing capabilities. The Processor 12 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 13 is used for establishing communication connection between the emergency hierarchical promotion system 10 and other communication terminal devices through a network, so as to implement transceiving operation of network signals and data. The network signal may include a wireless signal or a wired signal.
It will be appreciated that the configuration shown in fig. 1 is merely illustrative and that emergency staging push system 10 may include more or fewer components than shown in fig. 1 or may have a different sampling than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
An embodiment of the present application further provides a computer storage medium, where a computer program is stored, and the computer program implements the method when running.
Fig. 2 shows a flowchart of a GIS-based emergency hierarchical push provided by an embodiment of the present application. The method steps defined by the flow associated with the method, as applied to the emergency hierarchical push system 10, may be implemented by the processor 12, including the following S21-S25.
S21: the emergency grading promotion system transmits the prior geographical disaster emergency plan into an emergency plan upgrading network.
For example, the prior geographic disaster emergency plan may be a road emergency plan, a crowd transfer plan, or a material emergency plan, and the emergency plan upgrading network may be a relevant machine learning model/artificial intelligence neural network for performing a hierarchical promotion upgrade of the geographic disaster plan.
S22: and the emergency grading pushing system acquires a first dynamic rescue plan set in the prior geographical disaster emergency plans through the emergency plan upgrading network.
For example, the first set of dynamic rescue plans may be a set of emergency plans in a gradable driving state.
In some possible embodiments, the obtaining of the first set of dynamic rescue plans in the prior geographic disaster emergency plans through the emergency plan upgrading network described in S22 may be implemented by the schemes described in S221 and S222 below.
S221: and acquiring first static feature distribution in the prior geographical disaster emergency plan through the emergency plan upgrading network.
For example, the static feature distribution can be understood as a reference feature map or a reference feature vector set corresponding to a prior geographic disaster emergency plan.
S222: and acquiring a first dynamic rescue plan set in the prior geographical disaster emergency plan based on the first static characteristic distribution through the emergency plan upgrading network.
In a related embodiment, the step of obtaining the first dynamic rescue plan set in the prior geographic disaster emergency plan through the emergency plan upgrading network based on the first static feature distribution, which is described in S222, may be implemented by the following technical solutions described in S2221-S2223.
S2221: and acquiring a first to-be-determined rescue plan set in the prior geographical disaster emergency plan based on the first static characteristic distribution through the emergency plan upgrading network.
S2222: and acquiring second emergency state information characteristic distribution in the prior geographical disaster emergency plan based on the first to-be-determined rescue plan set and the first static characteristic distribution through the emergency plan upgrading network.
S2223: and acquiring the first dynamic rescue plan set based on the second emergency state information characteristic distribution through the emergency plan upgrading network.
For example, the emergency state information may be understood as an event state having a large influence range or a large disaster degree.
In some possible embodiments, the obtaining the first set of dynamic rescue plans based on the second emergency state information characteristic distribution through the emergency plan upgrading network described in the above S2223 may include the following S22231 and S22232.
S22231: and acquiring rescue plan clustering feature distribution and rescue plan commonality feature distribution corresponding to the second emergency state information feature distribution through the emergency plan upgrading network.
In the embodiment of the present application, the rescue plan clustering feature distribution is used to represent the probability that the first set of pending rescue plans belongs to each category, and the first rescue plan commonality feature distribution is used to represent the difference (error/deviation) of the first dynamic rescue plan set with respect to the first set of pending rescue plans.
S22232: and acquiring the information of the first dynamic rescue plan set based on the rescue plan clustering feature distribution corresponding to the second emergency state information feature distribution and the rescue plan commonality feature distribution corresponding to the second emergency state information feature distribution.
Further, the information of the first set of dynamic rescue plans further comprises rescue plan elements of the first set of dynamic rescue plans and categories of the first set of dynamic rescue plans. Based on this, the obtaining of the information of the first dynamic rescue plan set based on the rescue plan cluster feature distribution corresponding to the second emergency state information feature distribution and the rescue plan commonality feature distribution corresponding to the second emergency state information feature distribution, which is described in S22232, may include: performing feature cleaning processing on the rescue plan clustering feature distribution corresponding to the second emergency state information feature distribution to obtain the category of the first dynamic rescue plan set; and correcting the difference between the rescue plan commonality characteristic distribution corresponding to the second emergency state information characteristic distribution and the rescue plan elements of the first to-be-determined rescue plan set to obtain the rescue plan elements of the first dynamic rescue plan set. For example, the rescue plan elements may be various types of features of the rescue plan, so that a high correlation between the information of the first dynamic rescue plan set and the actual geographical disaster occurrence environment can be ensured.
Through the above S2221-S2223, the emergency state information can be taken into account, so that the first dynamic rescue plan set can be determined accurately and reliably.
It can be understood that, through the above S221 and S222, the characteristic reduction processing can be performed on the prior geographic disaster emergency plan, so as to reduce the time consumption for acquiring the first dynamic rescue plan set on the premise of ensuring that the first dynamic rescue plan set is accurately determined.
In some optional embodiments, before the obtaining the first set of dynamic rescue plans in the prior geographic disaster emergency plans through the emergency plan upgrading network described at S22, S30 may be further included: and training and adjusting parameters of the emergency plan upgrading network.
It is to be appreciated that training the emergency protocol upgrade network to tune parameters corresponds to training the associated network model. Accordingly, training and tuning the emergency protocol upgrade network as described in S30 may include S31-S36.
S31: and transmitting the sample geographical disaster emergency plan into the emergency plan upgrading network.
S32: and acquiring second static characteristic distribution of the sample geographical disaster emergency plan through the emergency plan upgrading network.
S33: and acquiring second emergency state information and third emergency state information characteristic distribution in the sample geographical disaster emergency plan based on the second static characteristic distribution through the emergency plan upgrading network.
S34: and decomposing the second emergency state information into a plurality of plan state events through the emergency plan upgrading network, identifying plan transformation possibility corresponding to each plan state event in the second emergency state information based on the third emergency state information characteristic distribution through the emergency plan upgrading network, and dynamically upgrading the second static characteristic distribution through the emergency plan upgrading network to obtain a second dynamic upgrading characteristic distribution.
S35: and acquiring a first network loss of the emergency plan upgrading network based on a comparison condition between a plan transformation possibility corresponding to each plan state event in the second emergency state information and a first reference transformation possibility, and acquiring a second network loss of the emergency plan upgrading network based on a comparison condition between the second dynamic upgrading characteristic distribution and a second reference transformation possibility.
For example, the network loss may be a loss function of the network model.
S36: improving network parameters of the emergency plan upgrade network based on the first network loss and the second network loss.
For example, the network parameters may be model parameters of a network model or related network layer parameters.
In this way, through S31-S36, global sampling can be performed on the emergency plan upgrade network, so that the stability and the operation quality of the emergency plan upgrade network in the subsequent use process are ensured.
In some related embodiments, the obtaining, by the emergency plan upgrading network, the second emergency state information and the third emergency state information feature distribution in the prior geographic disaster emergency plan based on the second static feature distribution may include: acquiring a second undetermined rescue plan set of the sample geographical disaster emergency plan through the emergency plan upgrading network based on the second static characteristic distribution, and taking state information corresponding to the second undetermined rescue plan set in the sample geographical disaster emergency plan as second emergency state information; and acquiring the third emergency state information feature distribution based on the second pending rescue plan set and the second static feature distribution through the emergency plan upgrading network.
Further, after the obtaining of the third emergency state information feature distribution based on the second pending rescue plan set and the second static feature distribution through the emergency plan upgrading network, the following may be included: acquiring rescue plan clustering feature distribution and rescue plan commonality feature distribution corresponding to the third emergency state information feature distribution through the emergency plan upgrading network, wherein the rescue plan clustering feature distribution corresponding to the third emergency state information feature distribution is used for representing the probability that the second undetermined rescue plan set belongs to each category, and the rescue plan commonality feature distribution corresponding to the third emergency state information feature distribution is used for representing the difference condition of the second dynamic rescue plan set relative to the second undetermined rescue plan set; acquiring a third network loss of the emergency plan upgrading network based on a comparison condition between a rescue plan clustering feature distribution corresponding to the third emergency state information feature distribution and a third reference transformation possibility, and acquiring a fourth network loss of the emergency plan upgrading network based on a comparison condition between a rescue plan commonality feature distribution corresponding to the third emergency state information feature distribution and a fourth reference transformation possibility; improving network parameters of the emergency plan upgrade network based on the third network loss and the fourth network loss.
The first network loss, the second network loss, the third network loss and the fourth network loss respectively reflect the performance of the emergency plan upgrading network from different angles, and by the design, the improvement of the network parameters of the emergency plan upgrading network can be realized by combining the network losses of more layers as far as possible, so that the anti-jamming capability of the emergency plan upgrading network is improved.
S23: and the emergency grading pushing system acquires first emergency state information and first emergency state information characteristic distribution in the prior geographical disaster emergency plan based on the first dynamic rescue plan set through the emergency plan upgrading network.
In an embodiment of the present application, the first emergency state information is state information corresponding to the first dynamic rescue plan set in the prior geographical disaster emergency plan.
S24: the emergency hierarchical pushing system disassembles the first emergency state information into a plurality of plan state events through the emergency plan upgrading network, identifies plan transformation possibility corresponding to each plan state event in the first emergency state information based on the first emergency state information characteristic distribution through the emergency plan upgrading network, and obtains dynamic plan transformation possibility of each plan state node in the first emergency state information through the emergency plan upgrading network.
In the embodiment of the application, the possibility of plan transformation corresponding to the plan state event is the probability of a dynamic rescue plan existing in the plan state event. The protocol change possibility can also be understood as characteristic information of the protocol state event.
In some examples, the information of the first dynamic set of rescue plans includes rescue plan elements of the first dynamic set of rescue plans. Based on this, the acquiring, through the emergency plan upgrading network, the dynamic plan transformation possibility of each plan status node in the first emergency status information described in S24 may include the following technical solutions described in S241 and S242.
S241: and dynamically upgrading the first static characteristic distribution through the emergency plan upgrading network to obtain a first dynamic upgrading characteristic distribution corresponding to the plan state node in the prior geographical disaster emergency plan.
For example, dynamic escalation may be understood as an improvement in feature distribution based on the dynamic state of a geo-disaster business.
S242: determining a plan transformation probability corresponding to each plan state node in the first emergency state information in the first dynamic upgrade feature distribution based on rescue plan elements of the first dynamic rescue plan set as a dynamic plan transformation probability corresponding to the plan state node in the first emergency state information.
It can be understood that, through S241 and S242, the dynamic status of the geographic disaster service can be taken into account, so as to ensure the integrity of the dynamic plan transformation possibility of each plan status node (which can be disassembled according to the time sequence characteristics) in the first emergency status information.
S25: and the emergency grading pushing system acquires a grading pushing upgrading result of the plan based on the plan transformation possibility of each plan state event in the first emergency state information, the information of the first dynamic rescue plan set and the dynamic plan transformation possibility of each plan state node in the first emergency state information.
For example, the plan grading promotion upgrading result may be an improved result or an updated result of the plan grading promotion, and is used for guiding the grading promotion of the subsequent geographical disaster emergency plan, so that the grading promotion efficiency of the geographical disaster emergency plan is improved, and the response enthusiasm of the geographical disaster emergency plan is improved.
In some examples, the information of the first set of dynamic rescue plans includes a category of the first set of dynamic rescue plans. Based on this, obtaining a plan grading promotion upgrade result based on the plan transformation likelihood of each plan status event in the first emergency status information, the information of the first dynamic rescue plan set, and the dynamic plan transformation likelihood of each plan status node in the first emergency status information, which is described in S25, may include S251 and S252.
S251: determining that the state information of the dynamic rescue plan exists in the first emergency state information based on the plan transformation possibility corresponding to each plan state event in the first emergency state information, and determining the category corresponding to the plan state node in the first emergency state information based on the dynamic plan transformation possibility of each plan state node in the first emergency state information.
In the embodiment of the application, the possibility of plan transformation corresponding to the plan state event corresponding to the state information with the dynamic rescue plan is greater than a preset probability threshold;
s252: and determining the plan state nodes belonging to the category of the first dynamic rescue plan set in the state information of the existing dynamic rescue plan as the plan grading promotion upgrading result based on the category of each plan state node in the first emergency state information.
For example, the plan state nodes belonging to the category of the first dynamic rescue plan set in the state information of the existing dynamic rescue plan may include a common situation of related emergency plans, and the plan grading promotion requirement may be analyzed from a global level through the plan state nodes belonging to the category of the first dynamic rescue plan set in the state information of the existing dynamic rescue plan, so that a plan grading promotion upgrading result is accurately and reliably obtained.
In some alternative embodiments, the method may further include the following on the basis of the above-mentioned S21-S25.
S26: and pushing the upgrading result in a grading way according to the plan to carry out grading pushing on the emergency.
For example, the execution priority of the related plan can be adjusted according to the grading promotion and upgrading result of the plan, so that the grading promotion of the emergency is realized, and various emergency situations can be quickly and accurately dealt with.
In some alternative embodiments, the step of performing the emergency hierarchical pushing according to the upgrade result of the plan hierarchical pushing described in S26 may include the following steps S261 to S266.
S261: acquiring an emergency measure information set corresponding to the plan grading promotion upgrading result, wherein the emergency measure information set comprises i groups of associated emergency measure information, and i is an integer greater than or equal to 1;
s262: and acquiring an emergency interference information set according to the emergency measure information set, wherein the emergency interference information set comprises i groups of emergency interference information with correlation.
S263: and based on the emergency measure information set, a first information fine screening subnet included in the network is analyzed through emergency measures to obtain an emergency measure feature set, wherein the emergency measure feature set comprises i emergency measure features.
S264: and analyzing a second information fine screening subnet included in the network through the emergency measure to obtain an emergency interference feature set based on the emergency interference information set, wherein the emergency interference feature set comprises i emergency interference features.
S265: and acquiring a plan conflict label corresponding to the emergency measure information through a plan compatibility analysis subnet included in the emergency measure analysis network based on the emergency measure feature set and the emergency interference feature set.
S266: and determining a grading pushing strategy of the emergency measure information set according to the pre-arranged conflict tag, and carrying out grading pushing on the emergency events based on the grading pushing strategy.
In some optional embodiments, the obtaining, based on the emergency measure feature set and the emergency interference feature set, a pre-arranged plan compatibility analysis subnet included in the emergency measure analysis network to obtain a pre-arranged plan conflict tag corresponding to the emergency measure information set includes: acquiring i first feature expressions through a first latitude-concerned unit included in the emergency analysis network based on the emergency measure feature set, wherein each first feature expression corresponds to one emergency measure feature; based on the emergency interference feature set, acquiring i second feature expressions through a second longitude and latitude attention unit included in the emergency measure analysis network, wherein each second feature expression corresponds to one emergency interference feature; synthesizing the i first feature expressions and the i second feature expressions to obtain i target feature expressions, wherein each target feature expression comprises a first feature expression and a second feature expression; and acquiring a plan conflict label corresponding to the emergency measure information set through the plan compatibility analysis subnet included in the emergency measure analysis network based on the i target feature expressions.
In some alternative embodiments, the obtaining, by the first latitude-interested unit included in the emergency analysis network, i first feature expressions based on the emergency measure feature set includes: for each group of emergency measure features in the emergency measure feature set, obtaining a first global clustering feature through a global clustering layer included by the first latitude-focused unit, wherein the first latitude-focused unit belongs to the emergency measure analysis network; for each group of emergency measure features in the emergency measure feature set, obtaining a first local clustering feature through a local clustering layer included by the first latitude-longitude-related unit; for each group of emergency measure features in the emergency measure feature set, obtaining a first fusion feature through a feature compression layer included in the first latitude-interested unit based on the first global clustering feature and the first local clustering feature; for each set of emergency measures features in the set of emergency measures features, a first feature representation is obtained through a first local clustering layer included by the first latitudinal attention unit based on the first fusion feature and the emergency measures features.
It can be understood that by implementing the above S261-S266, compatibility and conflict between different plans can be considered, so as to ensure that global coordination can be considered during the pushing of emergency classification, and avoid conflict between different emergency plans.
Through the mode, the first dynamic rescue plan set in the prior geographical disaster emergency plan is obtained through the emergency plan upgrading network, the first static characteristic distribution and the first emergency state information characteristic distribution of the prior geographical disaster emergency plan are obtained based on the first dynamic rescue plan set, the dynamic rescue plan of plan state evenings is selected for the first emergency state information, accordingly, the dynamic rescue plan in the first emergency state information can be rapidly screened preliminarily, the dynamic plan transformation possibility of each plan state node in the first emergency state information is obtained through the emergency plan upgrading network, and the plan grading promotion upgrading result can be obtained by combining the rough screening result of the dynamic rescue plan in the first emergency state information and the dynamic plan transformation possibility of each plan state node. Compared with the related technology for carrying out the plan grading promotion upgrading on the first emergency state information characteristic distribution through the emergency grading promotion decision tree, the method and the system improve the accuracy and timeliness of the plan grading promotion upgrading, and effectively reduce the system resource consumption of the emergency grading promotion.
Moreover, because the decision tree is promoted by related technologies in a hierarchical manner according to the emergency events, the probability that each plan transformation possibility in the corresponding first emergency state information feature distribution belongs to each category needs to be obtained, the output feature distribution can occupy a large amount of system resources and cost, and the emergency plan upgrading network only identifies whether each plan state event has a dynamic rescue plan, so that the occupation and cost of the system resources are effectively reduced, the consumption of the system resources occupied by the emergency plan upgrading can be reduced, and the time consumed by promoting the upgrading by the emergency plans in a hierarchical manner is reduced.
Based on the same inventive concept as above, as shown in fig. 3, the embodiment of the present application further provides a GIS-based emergency staged pushing device 20, applied to an emergency staged pushing system 10, the device including:
a plan transmission module 21 for transmitting the emergency plan of the prior geographic disaster to an emergency plan upgrading network;
a plan obtaining module 22, configured to obtain, through the emergency plan upgrading network, a first dynamic rescue plan set in the prior geographic disaster emergency plans;
a feature obtaining module 23, configured to obtain, through the emergency plan upgrading network, first emergency state information and first emergency state information feature distribution in the previous geographic disaster emergency plan based on the first dynamic rescue plan set, where the first emergency state information is state information of the first dynamic rescue plan set corresponding to the previous geographic disaster emergency plan;
a plan analysis module 24, configured to disassemble the first emergency state information into a plurality of plan state events through the emergency plan upgrade network, identify, through the emergency plan upgrade network, a plan transformation possibility corresponding to each plan state event in the first emergency state information based on feature distribution of the first emergency state information, and obtain, through the emergency plan upgrade network, a dynamic plan transformation possibility of each plan state node in the first emergency state information, where the plan transformation possibility corresponding to the plan state event is a probability that a dynamic rescue plan exists in the plan state event;
a grading pushing module 25, configured to obtain a grading pushing upgrade result of the plan based on the plan transformation possibility of each plan state event in the first emergency state information, the information of the first dynamic rescue plan set, and the dynamic plan transformation possibility of each plan state node in the first emergency state information.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. The storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A GIS-based emergency hierarchical pushing method is applied to an emergency hierarchical pushing system and comprises the following steps:
transmitting the prior geographical disaster emergency plan into an emergency plan upgrading network;
acquiring a first dynamic rescue plan set in the prior geographical disaster emergency plan through the emergency plan upgrading network;
acquiring first emergency state information and first emergency state information characteristic distribution in the prior geographical disaster emergency plan based on the first dynamic rescue plan set through the emergency plan upgrading network, wherein the first emergency state information is corresponding state information of the first dynamic rescue plan set in the prior geographical disaster emergency plan;
decomposing the first emergency state information into a plurality of plan state events through the emergency plan upgrading network, identifying plan transformation possibility corresponding to each plan state event in the first emergency state information based on the first emergency state information characteristic distribution through the emergency plan upgrading network, and acquiring dynamic plan transformation possibility of each plan state node in the first emergency state information through the emergency plan upgrading network, wherein the plan transformation possibility corresponding to the plan state event is the probability of a dynamic rescue plan existing in the plan state event;
and acquiring a plan grading promotion upgrading result based on the plan transformation possibility of each plan state event in the first emergency state information, the information of the first dynamic rescue plan set and the dynamic plan transformation possibility of each plan state node in the first emergency state information.
2. The method of claim 1, wherein the information of the first dynamic rescue plan set comprises a category of the first dynamic rescue plan set, and the obtaining a plan ranking promotion upgrade result based on a plan transformation likelihood of each plan status event in the first emergency status information, the information of the first dynamic rescue plan set, and a dynamic plan transformation likelihood of each plan status node in the first emergency status information comprises:
determining that the state information of the dynamic rescue plan exists in the first emergency state information based on a plan transformation possibility corresponding to each plan state event in the first emergency state information, and determining the category corresponding to the plan state node in the first emergency state information based on the dynamic plan transformation possibility of each plan state node in the first emergency state information, wherein the plan transformation possibility corresponding to the plan state event corresponding to the state information of the dynamic rescue plan exists is greater than a preset probability threshold;
and determining the plan state nodes belonging to the category of the first dynamic rescue plan set in the state information of the existing dynamic rescue plan as the plan grading promotion upgrading result based on the category of each plan state node in the first emergency state information.
3. The method of claim 1, wherein the obtaining, via the emergency protocol escalation network, a first set of dynamic rescue plans in the prior geographic disaster emergency protocols comprises:
acquiring first static characteristic distribution in the prior geographical disaster emergency plan through the emergency plan upgrading network;
and acquiring a first dynamic rescue plan set in the prior geographical disaster emergency plan based on the first static characteristic distribution through the emergency plan upgrading network.
4. The method of claim 3, wherein the information of the first dynamic rescue plan set comprises rescue plan elements of the first dynamic rescue plan set, and wherein the obtaining of the dynamic plan change probability of each plan state node in the first emergency state information through the emergency plan upgrade network comprises:
dynamically upgrading the first static characteristic distribution through the emergency plan upgrading network to obtain a first dynamic upgrading characteristic distribution corresponding to the plan state node in the prior geographical disaster emergency plan;
determining a plan transformation probability corresponding to each plan state node in the first emergency state information in the first dynamic upgrade feature distribution based on rescue plan elements of the first dynamic rescue plan set as a dynamic plan transformation probability corresponding to the plan state node in the first emergency state information.
5. The method of claim 3, wherein the obtaining, by the emergency protocol upgrade network, a first set of dynamic rescue plans in the prior geo-disaster emergency protocol based on the first static feature distribution comprises:
acquiring a first to-be-determined rescue plan set in the prior geographical disaster emergency plan based on the first static feature distribution through the emergency plan upgrading network;
acquiring second emergency state information feature distribution in the prior geographical disaster emergency plan based on the first to-be-determined rescue plan set and the first static feature distribution through the emergency plan upgrading network;
and acquiring the first dynamic rescue plan set based on the second emergency state information characteristic distribution through the emergency plan upgrading network.
6. The method of claim 5, wherein the obtaining, by the emergency protocol upgrade network, the first set of dynamic rescue plans based on the second emergency state information feature distribution comprises:
acquiring rescue plan clustering feature distribution and rescue plan commonality feature distribution corresponding to the second emergency state information feature distribution through the emergency plan upgrading network, wherein the rescue plan clustering feature distribution is used for representing the probability that the first to-be-determined rescue plan set belongs to each category, and the rescue plan commonality feature distribution is used for representing the difference condition of the first dynamic rescue plan set relative to the first to-be-determined rescue plan set;
acquiring information of the first dynamic rescue plan set based on rescue plan clustering feature distribution corresponding to the second emergency state information feature distribution and rescue plan commonality feature distribution corresponding to the second emergency state information feature distribution;
correspondingly, the information of the first dynamic rescue plan set further includes rescue plan elements of the first dynamic rescue plan set and categories of the first dynamic rescue plan set, and the obtaining of the information of the first dynamic rescue plan set based on the rescue plan cluster feature distribution corresponding to the second emergency state information feature distribution and the rescue plan commonality feature distribution corresponding to the second emergency state information feature distribution includes:
performing feature cleaning processing on the rescue plan clustering feature distribution corresponding to the second emergency state information feature distribution to obtain the category of the first dynamic rescue plan set;
and correcting the difference between the rescue plan commonality characteristic distribution corresponding to the second emergency state information characteristic distribution and the rescue plan elements of the first to-be-determined rescue plan set to obtain the rescue plan elements of the first dynamic rescue plan set.
7. The method of claim 1, wherein prior to said obtaining a first set of dynamic rescue plans in said prior geo-disaster emergency protocol over said emergency protocol upgrade network, comprising: and training and adjusting parameters of the emergency plan upgrading network.
8. The method of claim 7, wherein the training and tuning the emergency protocol upgrade network comprises:
transmitting a sample geographical disaster emergency plan into the emergency plan upgrading network;
acquiring second static characteristic distribution of the sample geographical disaster emergency plan through the emergency plan upgrading network;
acquiring second emergency state information and third emergency state information characteristic distribution in the sample geographical disaster emergency plan based on the second static characteristic distribution through the emergency plan upgrading network;
decomposing the second emergency state information into a plurality of plan state events through the emergency plan upgrading network, identifying plan transformation possibility corresponding to each plan state event in the second emergency state information based on third emergency state information characteristic distribution through the emergency plan upgrading network, and dynamically upgrading the second static characteristic distribution through the emergency plan upgrading network to obtain second dynamic upgrading characteristic distribution;
acquiring a first network loss of the emergency plan upgrading network based on a comparison condition between a plan transformation possibility corresponding to each plan state event in the second emergency state information and a first reference transformation possibility, and acquiring a second network loss of the emergency plan upgrading network based on a comparison condition between the second dynamic upgrading feature distribution and a second reference transformation possibility;
improving network parameters of the emergency plan upgrade network based on the first network loss and the second network loss;
wherein the obtaining of the second emergency state information and the third emergency state information feature distribution in the prior geographical disaster emergency plan based on the second static feature distribution through the emergency plan upgrade network includes:
acquiring a second undetermined rescue plan set of the sample geographical disaster emergency plan through the emergency plan upgrading network based on the second static characteristic distribution, and taking state information corresponding to the second undetermined rescue plan set in the sample geographical disaster emergency plan as second emergency state information;
acquiring the third emergency state information feature distribution based on the second pending rescue plan set and the second static feature distribution through the emergency plan upgrading network;
correspondingly, after the third emergency state information feature distribution is acquired through the emergency plan upgrading network based on the second pending rescue plan set and the second static feature distribution, the method includes:
acquiring rescue plan clustering feature distribution and rescue plan commonality feature distribution corresponding to the third emergency state information feature distribution through the emergency plan upgrading network, wherein the rescue plan clustering feature distribution corresponding to the third emergency state information feature distribution is used for representing the probability that the second undetermined rescue plan set belongs to each category, and the rescue plan commonality feature distribution corresponding to the third emergency state information feature distribution is used for representing the difference condition of the second dynamic rescue plan set relative to the second undetermined rescue plan set;
acquiring a third network loss of the emergency plan upgrading network based on a comparison condition between a rescue plan clustering feature distribution corresponding to the third emergency state information feature distribution and a third reference transformation possibility, and acquiring a fourth network loss of the emergency plan upgrading network based on a comparison condition between a rescue plan commonality feature distribution corresponding to the third emergency state information feature distribution and a fourth reference transformation possibility;
improving network parameters of the emergency plan upgrade network based on the third network loss and the fourth network loss.
9. An emergency hierarchical promotion system comprising a processor, a network module, and a memory; the processor and the memory communicate through the network module, the processor reading the computer program from the memory and operating to perform the method of any one of claims 1-8.
10. A computer storage medium, characterized in that it stores a computer program which, when executed, implements the method of any one of claims 1-8.
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