Emergency management method and device for natural disasters, computer equipment and storage medium

Abstract

The invention relates to the technical field of data processing and the field of financial science and technology businesses, and discloses an emergency management method and device for natural disasters, computer equipment and a storage medium, and the method comprises the steps: generating a report amount monitoring result according to report data through ElasticSearch; determining a disaster type based on a case report amount monitoring result; and determining an emergency management mode corresponding to the disaster type according to the disaster type. Through the above mode, the change of the report amount is quickly captured through the real-time search and analysis capability of ElasticSearch, and the disaster type can be quickly determined through automatic data analysis, so that corresponding emergency management measures are taken, and the loss caused by the disaster is reduced. Based on the report amount monitoring result and disaster type analysis, rescue resources can be allocated more accurately for different disaster types, an emergency management strategy can be formulated more scientifically and reasonably, and the emergency management efficiency for coping with natural disasters is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology and the field of financial technology business, and in particular to a method, device, computer equipment and storage medium for emergency management of natural disasters. Background Art

[0002] In the process of company operation and management, unpredictable factors such as natural disasters may lead to a sudden increase in the number of reports, which is called a "major disaster". Major disasters usually include natural disasters such as lightning strikes, storms, tornadoes, heavy rains, floods, tsunamis, ground subsidence, ice subsidence, cliff collapse, hailstorms, mudslides, landslides, typhoons, blizzards, sandstorms, earthquakes, etc. These disasters pose a serious threat to the normal operation and brand image of the company.

[0003] With the development of data processing technology and financial technology, there has been some progress in the prediction technology of natural disasters, but there are still some defects and shortcomings. First, in the face of complex and severe extreme weather, multiple institutions need to cooperate in different disaster response links, but there are currently barriers to collaboration between institutions, resulting in poor coordination in the disaster response links. Secondly, existing emergency management methods often lack effective real-time monitoring and early warning mechanisms, and cannot quickly respond to the sudden increase in the number of reports, thus affecting the efficiency and service quality of disaster response. Therefore, how to improve the efficiency of emergency management in response to natural disasters has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present application provides a natural disaster emergency management method, apparatus, computer equipment and storage medium to improve the efficiency of emergency management in response to natural disasters.

[0005] In a first aspect, the present application provides a method for emergency management of natural disasters, the method comprising:

[0006] Through the preset distributed search analysis engine ElasticSearch, the number of reported cases is monitored according to the reported data of the target object, and the monitoring results of the number of reported cases are generated;

[0007] Determining the disaster type of the target object based on the monitoring result of the case report amount;

[0008] According to the disaster type, an emergency management method corresponding to the disaster type is determined.

[0009] In a second aspect, the present application further provides a natural disaster emergency management device, the device comprising:

[0010] The case reporting volume monitoring result generation module is used to monitor the case reporting volume according to the case reporting data of the target object through the preset distributed search analysis engine ElasticSearch and generate the case reporting volume monitoring result;

[0011] A disaster type determination module, used to determine the disaster type of the target object based on the case reporting monitoring result;

[0012] The emergency management method determination module is used to determine the emergency management method corresponding to the disaster type according to the disaster type.

[0013] In a third aspect, the present application also provides a computer device, comprising a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the emergency management method for natural disasters as described above when executing the computer program.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the emergency management method for natural disasters as described above.

[0015] The present application discloses a natural disaster emergency management method, device, computer equipment and storage medium. The natural disaster emergency management method includes monitoring the number of reports based on the report data of the target object through a preset distributed search and analysis engine ElasticSearch to generate a report monitoring result; based on the report monitoring result, determining the disaster type of the target object; and according to the disaster type, determining the emergency management method corresponding to the disaster type. Through the above-mentioned method, the present application quickly captures the changes in the number of reports through the real-time search and analysis capabilities of ElasticSearch, and through automated data analysis, it can quickly determine the type of disaster, so as to take corresponding emergency management measures to reduce the losses caused by the disaster. Based on the report monitoring results and disaster type analysis, rescue resources can be deployed more accurately for different disaster types, and emergency management strategies can be formulated more scientifically and reasonably, thereby improving the efficiency of emergency management in response to natural disasters. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 1 is a schematic flow chart of a natural disaster emergency management method provided by the first embodiment of the present application;

[0018] Figure 2is a schematic flow chart of a natural disaster emergency management method provided in the second embodiment of the present application;

[0019] Figure 3 is a schematic flow chart of a natural disaster emergency management method provided by the third embodiment of the present application;

[0020] Figure 4 A schematic block diagram of a natural disaster emergency management device provided in an embodiment of the present application;

[0021] Figure 5 A schematic block diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0024] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0025] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0026] The embodiments of the present application provide a method, device, computer equipment and storage medium for emergency management of natural disasters. Among them, the emergency management method for natural disasters can be applied to a server. Through the real-time search and analysis capabilities of ElasticSearch, the changes in the number of reports can be quickly captured. Through automated data analysis, the type of disaster can be quickly determined, so that corresponding emergency management measures can be taken to reduce the losses caused by the disaster. Based on the monitoring results of the number of reports and the analysis of disaster types, rescue resources can be deployed more accurately for different types of disasters, and emergency management strategies can be formulated more scientifically and reasonably, thereby improving the efficiency of emergency management in response to natural disasters. Among them, the server can be an independent server or a server cluster.

[0027] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0028] See also Figure 1 , Figure 1 This is a schematic flow chart of a natural disaster emergency management method provided by the first embodiment of the present application. The natural disaster emergency management method can be applied to the server to quickly capture changes in the number of reports through the real-time search and analysis capabilities of ElasticSearch. Through automated data analysis, the type of disaster can be quickly determined, so that corresponding emergency management measures can be taken to reduce the losses caused by the disaster. Based on the monitoring results of the number of reports and the analysis of disaster types, rescue resources can be deployed more accurately for different disaster types, and emergency management strategies can be formulated more scientifically and reasonably, thereby improving the efficiency of emergency management in response to natural disasters.

[0029] like Figure 1 As shown, the natural disaster emergency management method specifically includes steps S10 to S30.

[0030] Step S10: Using a preset distributed search analysis engine ElasticSearch to monitor the number of reported cases based on the reported case data of the target object, and generate a monitoring result of the number of reported cases;

[0031] Specifically, Elasticsearch is a distributed, highly scalable, and highly real-time search and data analysis engine. It can easily enable large amounts of data to be searched, analyzed, and explored. Taking full advantage of the horizontal scalability of Elasticsearch can make data more valuable in a production environment. The implementation principle of Elasticsearch is mainly divided into the following steps. First, the user submits the data to the Elasticsearch database, and then the corresponding sentence is segmented through the word segmentation controller, and its weight and word segmentation results are stored in the data. When the user searches for data, the results are ranked and scored according to the weight, and then the returned results are presented to the user.

[0032] Through the query interface of ElasticSearch, query parameters are set to retrieve the report data within a specific time range, and the aggregation function of ElasticSearch (such as Terms Aggregation, Date Histogram Aggregation, etc.) is used to group and count the report data to calculate the number of reports. The report volume monitoring results can include real-time report volume, time series analysis, trend prediction, etc.

[0033] Step S20: determining the disaster type of the target object based on the monitoring result of the number of reported cases;

[0034] Specifically, the report volume monitoring results are analyzed to identify abnormal patterns or pattern changes, which may indicate the occurrence of a disaster. Combined with other information in the report data (such as location, type, etc.), machine learning or statistical methods are used to further confirm the type of disaster.

[0035] Step S30: Determine the emergency management method corresponding to the disaster type according to the disaster type.

[0036] In one embodiment, according to the determined disaster type, the pre-set emergency management strategy library is queried to find the emergency management method corresponding to the disaster type, such as adjusting and optimizing the allocation of emergency resources, including personnel, materials and equipment, etc., and starting the emergency response process to execute relevant emergency management measures, such as evacuation, rescue, medical assistance, etc. Major natural disaster support dispatch will give priority to remote support, and remote overflow and unacceptable cases will be supported on-site. Continuously monitor the reporting data and the effectiveness of emergency management measures, collect feedback information, adjust the emergency management strategy based on the feedback information, and optimize the response process.

[0037] The two support modes are as follows:

[0038] 1) Remote support mode: Priority will be given to the national remote centralized team, and the overflow will be supported by the remote teams of various institutions (with priority given to institutions with strong capabilities and no pressure). The tasks undertaken include major disaster case investigation, case follow-up, and new case investigation;

[0039] 2) On-site support mode: Support agencies are selected based on their pressure-bearing conditions, distance, and team capabilities, and the tasks undertaken are mainly disaster loss assessment and new loss assessment.

[0040] This embodiment discloses a natural disaster emergency management method, device, computer equipment and storage medium. The natural disaster emergency management method includes monitoring the number of reports based on the report data of the target object through a preset distributed search and analysis engine ElasticSearch to generate a report monitoring result; based on the report monitoring result, determining the disaster type of the target object; according to the disaster type, determining the emergency management method corresponding to the disaster type. Through the above method, this application uses the real-time search and analysis capabilities of ElasticSearch to quickly capture changes in the number of reports, and through automated data analysis, it can quickly determine the type of disaster, so as to take corresponding emergency management measures to reduce the losses caused by the disaster. Based on the report monitoring results and disaster type analysis, rescue resources can be deployed more accurately for different disaster types, and emergency management strategies can be formulated more scientifically and reasonably, thereby improving the efficiency of emergency management in response to natural disasters.

[0041] See also Figure 2 , Figure 2 This is a schematic flow chart of a natural disaster emergency management method provided by the second embodiment of the present application. The natural disaster emergency management method can be applied to a server, and can be used to quickly capture changes in report data and achieve real-time monitoring through the real-time search and analysis capabilities of ElasticSearch. This helps to quickly identify and respond to potential disasters and reduce losses caused by disasters. Based on the analysis of report data, emergency management strategies can be formulated and adjusted more scientifically and reasonably to improve the accuracy and efficiency of decision-making. According to different disaster types and scales, rescue resources, including personnel, materials and equipment, can be deployed more accurately to ensure the effectiveness of rescue work and improve the efficiency of emergency management in response to natural disasters.

[0042] based on Figure 1 The embodiment shown, this embodiment Figure 2 As shown, step S10 includes steps S01 to S02.

[0043] Step S01, configuring the query interface of the ElasticSearch;

[0044] Specifically, create a suitable index in ElasticSearch to store the report data, specify the field type and other attributes of the field, such as whether to store, whether to index, etc. For example, for report data, it may include fields such as timestamp, location, type, severity, etc. If multiple indexes need to be created according to the same pattern, you can create an index template, which can define default mapping, settings, aliases, and other information.

[0045] Step S02: Receive, index and store the report data of the target object from at least one source through the query interface of the ElasticSearch.

[0046] In one embodiment, a data import script or program is written according to the source and format of the reported data, and during this process, it is necessary to ensure that the data cleaning and conversion logic correctly handles abnormal values ​​and missing values. The data is inserted into ElasticSearch using the index interface of ElasticSearch, and for each data entry, a request body is constructed, which contains the document content to be indexed.

[0047] Adjust the Elasticsearch configuration file as needed and define an index mode, which determines how to store and query data. Set up a data receiving interface, which can be an API endpoint for receiving report data from different sources. After receiving the report data, use Elasticsearch's index API to index the data into the specified index. In addition, you also need to configure a query interface so that you can query the data stored in Elasticsearch.

[0048] This embodiment discloses a natural disaster emergency management method, device, computer equipment and storage medium, the natural disaster emergency management method includes configuring the query interface of the ElasticSearch; receiving, indexing and storing the report data of the target object from at least one source through the query interface of the ElasticSearch; monitoring the report volume according to the report data of the target object through the preset distributed search analysis engine ElasticSearch, generating the report volume monitoring result; determining the disaster type of the target object based on the report volume monitoring result; and determining the emergency management method corresponding to the disaster type according to the disaster type. Through the above method, the application can quickly capture the changes in the report data and realize real-time monitoring through the real-time search and analysis capabilities of ElasticSearch. This helps to quickly identify and respond to potential disasters and reduce the losses caused by disasters. Based on the analysis of the report data, emergency management strategies can be formulated and adjusted more scientifically and reasonably to improve the accuracy and efficiency of decision-making. According to different disaster types and scales, rescue resources, including personnel, materials and equipment, can be deployed more accurately to ensure the effectiveness of rescue work and improve the efficiency of emergency management in response to natural disasters.

[0049] See also Figure 3 , Figure 3 This is a schematic flow chart of a natural disaster emergency management method provided by the third embodiment of the present application. The natural disaster emergency management method can be applied to the server, and can be used to flexibly set query parameters through the query interface of ElasticSearch, and filter the report data within a specific time range as needed to obtain accurate information. By using the aggregation function of ElasticSearch, the filtered data can be grouped and counted, and the report volume monitoring results can be generated to help users quickly understand the data distribution and trend. Based on the report volume monitoring results, the disaster type of the target object can be determined more accurately, while improving the efficiency of emergency management in response to natural disasters.

[0050] based on Figure 2 The embodiment shown, this embodiment Figure 3 As shown, step S10 includes steps S101 to S103.

[0051] Step S101, setting query parameters through the ElasticSearch query interface;

[0052] Specifically, the query parameters may include defining a time range for the query, such as a start time and an end time, and defining specific fields that need to be filtered, such as region, type, etc.

[0053] For example, use Elasticsearch's query DSL (Domain Specific Language) to construct a query statement, and add necessary query parameters according to the query requirements, such as match, range, term, bool, etc. Send the query statement to the Elasticsearch query interface through an HTTP request, and parse the returned query results in JSON format.

[0054] Step S102: Filter the reported data within a preset time range according to the query parameters;

[0055] Specifically, the constructed query request is sent to the ElasticSearch server, and the ElasticSearch server parses the response data to obtain the query result.

[0056] There is a field in the Elasticsearch index that is specifically used to store time information, such as the time of reporting a case, and the type of this field is date. Use Elasticsearch's query DSL (Domain Specific Language) to build a query request, use the range query to filter the data within the time range, and send the built query to Elasticsearch through an HTTP GET or POST request.

[0057] Step S103: group and count the filtered case report data through the aggregation function of ElasticSearch to generate the case report volume monitoring result.

[0058] Specifically, determine which fields to aggregate statistics on, which fields can be report type, report area, report time, etc. Use Elasticsearch's query DSL to build an aggregate query request, including filter conditions and aggregation instructions, and send the constructed aggregate query to Elasticsearch via HTTP GET or POST request. HTTP GET is an HTTP method used to request data from a server. It is part of the HTTP protocol and is used by clients (such as web browsers) to request information from a server.

[0059] Execute a query that includes an aggregation operation, obtain grouped statistical data, parse the response data based on the grouped statistical results, and extract the aggregation results.

[0060] In one embodiment, the specific steps are as follows:

[0061] 1. Prepare the ElasticSearch environment and ensure that the data has been correctly indexed into ElasticSearch;

[0062] 2. Define query parameters, including but not limited to time range, specific fields, etc.;

[0063] 3. Build a query request, which will contain all defined query parameters;

[0064] 4. Use the aggregation function of ElasticSearch to group and count the query results;

[0065] 5. Obtain and process the aggregated results to generate the required monitoring results;

[0066] 6. Optionally, the query and aggregation results are presented to the user in a visual form.

[0067] Send a query request to the ElasticSearch server and obtain response data. Parse the response data, extract the aggregated results, and obtain the natural disaster report data grouped by region. Process the aggregated results, such as sorting, formatting, etc., and display them to the user or further analyze them. Through the above steps, this embodiment can effectively query and aggregate the report data to generate the required monitoring results.

[0068] The present embodiment discloses a natural disaster emergency management method, apparatus, computer equipment and storage medium, the natural disaster emergency management method comprising configuring the ElasticSearch query interface; receiving, indexing and storing the report data of the target object from at least one source through the ElasticSearch query interface; setting query parameters through the ElasticSearch query interface; filtering the report data within a preset time range according to the query parameters; grouping and counting the filtered report data through the aggregation function of the ElasticSearch to generate the report volume monitoring result; determining the disaster type of the target object based on the report volume monitoring result; and determining the emergency management method corresponding to the disaster type according to the disaster type. Through the above method, this application can flexibly set query parameters through the query interface of ElasticSearch, and filter the report data within a specific time range as needed to obtain accurate information. By using the aggregation function of ElasticSearch, the filtered data can be grouped and counted to generate report volume monitoring results, helping users to quickly understand data distribution and trends. Based on the report volume monitoring results, the disaster type of the target object can be determined more accurately, while improving the efficiency of emergency management in response to natural disasters.

[0069] based on Figure 1 In the embodiment shown, in this embodiment, step S20 includes:

[0070] Compare the case reporting amount monitoring result with a preset case reporting amount threshold to generate a comparison result;

[0071] The disaster type is determined according to the comparison result.

[0072] In a specific embodiment, a reasonable threshold for the number of reports is set for different types of disasters based on historical data, experience or expert advice. These thresholds should be able to reflect the severity and urgency of different disasters. Through the query and aggregation functions of ElasticSearch, the report data within a specific time range is obtained, and the number of reports for each disaster type is calculated.

[0073] Compare the number of reports for each disaster type with the corresponding preset threshold. This can be done by writing a script or calculating using the aggregation results of ElasticSearch. Based on the comparison results, generate a result set containing the comparison of each disaster type with the corresponding threshold. Determine which disaster types have reported cases exceeding the threshold to determine the current disaster type. If the number of reports for multiple disaster types exceeds the threshold, further analysis may be required to determine the most urgent disaster type. This result set can be a report or a dataset for subsequent analysis.

[0074] According to the determined disaster type, take appropriate emergency management measures, which may include activating emergency plans, deploying resources, issuing alerts, etc.

[0075] This embodiment discloses a method, device, computer equipment and storage medium for emergency management of natural disasters. The method for emergency management of natural disasters includes monitoring the number of reports based on the report data of the target object through a preset distributed search analysis engine ElasticSearch to generate a monitoring result of the number of reports; comparing the monitoring result of the number of reports with a preset threshold value of the number of reports to generate a comparison result; determining the type of disaster based on the comparison result; and determining the emergency management method corresponding to the type of disaster based on the type of disaster. Through the above-mentioned method, this application can quickly discover abnormal situations and improve the speed of emergency response by real-time monitoring and analysis of report data. Disaster type identification and emergency management method selection based on data analysis can provide decision makers with more scientific and accurate decision support. According to different types of disasters, resources can be allocated more specifically, and by analyzing historical data and real-time data, potential risks can be discovered in advance, and resource utilization efficiency can be improved to improve the efficiency of emergency management in response to natural disasters.

[0076] In a more preferred embodiment, determining the disaster type according to the comparison result includes:

[0077] When the monitoring result of the number of reports is greater than or equal to 3 times the preset report volume threshold, or the monitoring result of the number of reports for a preset number of consecutive days is greater than or equal to 1.5 times the preset report volume threshold, the disaster type is determined to be a major disaster type.

[0078] Specifically, the disaster warning and initiation is carried out by continuously monitoring the real-time report volume of the institution. If the report volume suddenly increases to 3 times or 1.5-3 times the normal daily average for more than three consecutive days, the institution is judged to have encountered a major disaster. The real-time monitoring of the report volume can be done through the distributed search and analysis engine Elasticsearch, and the report volume of the day or the previous three days can be queried by time period. If it is determined to be a major disaster, the relevant institution head will be notified by email and the major disaster support dispatch process will be initiated.

[0079] It should be noted that the 3 times or 1.5-3 times in this embodiment are just examples and can be set by the user.

[0080] In a more preferred embodiment, step S30 includes:

[0081] When the disaster type is the major disaster type, the emergency management method is determined to be a remote support method and / or an on-site support method.

[0082] Specifically, disaster support dispatch will prioritize remote support, and provide on-site support for remote overflow and cases that cannot be undertaken. The two support methods are as follows:

[0083] 1) Remote support mode: Priority will be given to the national remote centralized team, and the overflow will be supported by the remote teams of various institutions (with priority given to institutions with strong capabilities and no pressure). The tasks undertaken include major disaster case investigation, case follow-up, and new case investigation;

[0084] 2) On-site support mode: Support agencies are selected based on their pressure-bearing conditions, distance, and team capabilities, and the tasks undertaken are mainly disaster loss assessment and new loss assessment.

[0085] In a specific embodiment, when the monitoring result of the number of reported cases is greater than or equal to 3 times the preset threshold of the number of reported cases, or the monitoring result of the number of reported cases for a preset number of consecutive days is greater than or equal to 1.5 times the preset threshold of the number of reported cases, before determining that the disaster type is a major disaster type, the method includes:

[0086] Collecting historical case reporting data, calculating the mean and standard deviation of the historical case reporting data, and determining an initial case reporting threshold value based on the mean and the standard deviation;

[0087] The initial case reporting volume threshold is adjusted according to the abnormal fluctuation of the historical case reporting volume data to determine the preset case reporting volume threshold.

[0088] In a specific embodiment, historical case reporting data over a period of time is collected from a database or data warehouse. These data should include the number of reports for each type of disaster. The mean and standard deviation of the historical case reporting data are calculated using statistical methods. The mean reflects the average level of the case reporting volume, while the standard deviation reflects the degree of fluctuation of the data. Based on the mean and standard deviation, an initial case reporting volume threshold can be determined. This threshold can be the mean plus a certain multiple of the standard deviation, for example, the mean plus twice the standard deviation.

[0089] After determining the initial threshold, it is necessary to analyze the abnormal fluctuations in the historical data. This may include an abnormal increase in the number of reports due to natural disasters, man-made events, or other factors. According to the specific circumstances of the abnormal fluctuation, the initial threshold is adjusted. If there are large abnormal fluctuations in the historical data, it may be necessary to set the threshold higher to accommodate this situation. After adjustment, the final preset report volume threshold is determined. This threshold should be able to reflect the actual situation while taking into account possible abnormal situations.

[0090] As time goes by and circumstances change, it may be necessary to regularly update the preset thresholds. This can be achieved by continually monitoring the report data and making adjustments based on new circumstances.

[0091] See also Figure 4 , Figure 4 The embodiment of the present application provides a schematic block diagram of a natural disaster emergency management device, which is used to execute the aforementioned natural disaster emergency management method. The natural disaster emergency management device can be configured on a server.

[0092] like Figure 4 As shown, the natural disaster emergency management device 400 includes:

[0093] The case reporting amount monitoring result generating module 410 is used to monitor the case reporting amount according to the case reporting data of the target object through the preset distributed search analysis engine ElasticSearch and generate the case reporting amount monitoring result;

[0094] A disaster type determination module 420, configured to determine the disaster type of the target object based on the case report monitoring result;

[0095] The emergency management method determination module 430 is used to determine the emergency management method corresponding to the disaster type according to the disaster type.

[0096] Furthermore, the natural disaster emergency management device 400 further includes:

[0097] A query interface configuration module, used to configure the query interface of the ElasticSearch;

[0098] A case report data acquisition module is used to receive, index and store the case report data of the target object from at least one source through the query interface of the ElasticSearch.

[0099] Furthermore, the case reporting amount monitoring result generating module 410 includes:

[0100] A query parameter setting unit, used to set query parameters through the query interface of ElasticSearch;

[0101] A case report data screening unit, used to screen the case report data within a preset time range according to the query parameter;

[0102] The case report quantity monitoring result generating unit is used to group and count the filtered case report data through the aggregation function of the ElasticSearch to generate the case report quantity monitoring result.

[0103] Furthermore, the disaster type determination module 420 includes:

[0104] A comparison result generating unit, used for comparing the case reporting amount monitoring result with a preset case reporting amount threshold value to generate a comparison result;

[0105] The disaster type determination unit is used to determine the disaster type according to the comparison result.

[0106] Furthermore, the disaster type determination unit includes:

[0107] The disaster type determination subunit is used to determine that the disaster type is a major disaster type when the result of the report volume monitoring is greater than or equal to 3 times the preset report volume threshold, or the result of the report volume monitoring for a preset number of consecutive days is greater than or equal to 1.5 times the preset report volume threshold.

[0108] Furthermore, the emergency management mode determination module 430 includes:

[0109] The emergency management mode determination unit is used to determine the emergency management mode as a remote support mode and / or an on-site support mode when the disaster type is the major disaster type.

[0110] Furthermore, the natural disaster emergency management device 400 further includes:

[0111] An initial case reporting volume threshold determination module is used to collect historical case reporting volume data, calculate the mean and standard deviation of the historical case reporting volume data, and determine the initial case reporting volume threshold according to the mean and the standard deviation;

[0112] The preset case reporting volume threshold determination module is used to adjust the initial case reporting volume threshold according to the abnormal fluctuation of the historical case reporting volume data to determine the preset case reporting volume threshold.

[0113] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0114] The above-mentioned device can be implemented in the form of a computer program. Figure 5 Runs on the computer device shown.

[0115] See also Figure 5 , Figure 5 1 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0116] See also Figure 5 The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0117] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any natural disaster emergency management method.

[0118] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0119] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any emergency management method for natural disasters.

[0120] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0121] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0122] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0123] Through the preset distributed search analysis engine ElasticSearch, the number of reported cases is monitored according to the reported data of the target object, and the monitoring results of the number of reported cases are generated;

[0124] Determining the disaster type of the target object based on the monitoring result of the case report amount;

[0125] According to the disaster type, an emergency management method corresponding to the disaster type is determined.

[0126] In one embodiment, the preset distributed search analysis engine ElasticSearch is used to monitor the number of reported cases based on the reported case data of the target object, and before generating the monitoring result of the number of reported cases, it is used to achieve:

[0127] Configure the query interface of the ElasticSearch;

[0128] The report data of the target object from at least one source is received, indexed and stored through the query interface of the ElasticSearch.

[0129] In one embodiment, a preset distributed search analysis engine ElasticSearch is used to monitor the number of reported cases based on the reported case data of the target object, and to generate a monitoring result of the number of reported cases, which is used to achieve:

[0130] Set query parameters through the ElasticSearch query interface;

[0131] Filter the reported data within a preset time range according to the query parameters;

[0132] The aggregation function of ElasticSearch is used to group and count the filtered case report data to generate the case report volume monitoring result.

[0133] In one embodiment, based on the case reporting monitoring result, the disaster type of the target object is determined to achieve:

[0134] Compare the case reporting amount monitoring result with a preset case reporting amount threshold to generate a comparison result;

[0135] The disaster type is determined according to the comparison result.

[0136] In one embodiment, the disaster type is determined according to the comparison result to achieve:

[0137] When the monitoring result of the number of reports is greater than or equal to 3 times the preset report volume threshold, or the monitoring result of the number of reports for a preset number of consecutive days is greater than or equal to 1.5 times the preset report volume threshold, the disaster type is determined to be a major disaster type.

[0138] In one embodiment, according to the disaster type, an emergency management method corresponding to the disaster type is determined to achieve:

[0139] When the disaster type is the major disaster type, the emergency management method is determined to be a remote support method and / or an on-site support method.

[0140] In one embodiment, when the monitoring result of the number of case reports is greater than or equal to 3 times the preset number of case reports threshold, or the monitoring result of the number of case reports for a preset number of consecutive days is greater than or equal to 1.5 times the preset number of case reports threshold, before determining that the disaster type is a major disaster type, it is used to achieve:

[0141] Collecting historical case reporting data, calculating the mean and standard deviation of the historical case reporting data, and determining an initial case reporting threshold value based on the mean and the standard deviation;

[0142] The initial case reporting volume threshold is adjusted according to the abnormal fluctuation of the historical case reporting volume data to determine the preset case reporting volume threshold.

[0143] A computer-readable storage medium is also provided in an embodiment of the present application, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and the processor executes the program instructions to implement any natural disaster emergency management method provided in the embodiment of the present application.

[0144] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0145] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A natural disaster emergency management method, It is characterized in that include: The preset distributed search and analysis engine ElasticSearch is used to monitor the number of reported cases based on the reported data of the target object and generate the monitoring results of the number of reported cases; Determining the disaster type of the target object based on the monitoring result of the case report amount; According to the disaster type, an emergency management method corresponding to the disaster type is determined.

2. The natural disaster emergency management method according to claim 1, It is characterized in that The method of monitoring the number of reported cases according to the reported case data of the target object by using the preset distributed search analysis engine ElasticSearch and generating the monitoring result of the number of reported cases includes: Configure the query interface of the ElasticSearch; The report data of the target object from at least one source is received, indexed and stored through the query interface of the ElasticSearch.

3. The natural disaster emergency management method according to claim 2, It is characterized in that The method of monitoring the number of reported cases according to the reported case data of the target object by using the preset distributed search analysis engine ElasticSearch to generate the monitoring result of the number of reported cases includes: Set query parameters through the ElasticSearch query interface; According to the query parameters, filter the reported data within a preset time range; The aggregation function of ElasticSearch is used to group and count the filtered case report data to generate the case report volume monitoring result.

4. The natural disaster emergency management method according to claim 1, It is characterized in that The determining the disaster type of the target object based on the case reporting amount monitoring result includes: Compare the case reporting amount monitoring result with a preset case reporting amount threshold to generate a comparison result; The disaster type is determined according to the comparison result.

5. The natural disaster emergency management method according to claim 4, It is characterized in that Determining the disaster type according to the comparison result includes: When the monitoring result of the number of reports is greater than or equal to 3 times the preset report volume threshold, or the monitoring result of the number of reports for a preset number of consecutive days is greater than or equal to 1.5 times the preset report volume threshold, the disaster type is determined to be a major disaster type.

6. The method for emergency management of natural disasters according to claim 5, It is characterized in that Determining the emergency management method corresponding to the disaster type according to the disaster type includes: When the disaster type is the major disaster type, the emergency management method is determined to be a remote support method and / or an on-site support method.

7. The natural disaster emergency management method according to claim 5, It is characterized in that When the monitoring result of the case report amount is greater than or equal to 3 times the preset case report amount threshold, or the monitoring result of the case report amount for a preset number of consecutive days is greater than or equal to 1.5 times the preset case report amount threshold, before determining that the disaster type is a major disaster type, the method includes: Collecting historical case reporting data, calculating the mean and standard deviation of the historical case reporting data, and determining an initial case reporting threshold value based on the mean and the standard deviation; The initial case reporting volume threshold is adjusted according to the abnormal fluctuation of the historical case reporting volume data to determine the preset case reporting volume threshold.

8. A natural disaster emergency management device, It is characterized in that include: The case reporting volume monitoring result generation module is used to monitor the case reporting volume according to the case reporting data of the target object through the preset distributed search analysis engine ElasticSearch and generate the case reporting volume monitoring result; A disaster type determination module, used to determine the disaster type of the target object based on the case reporting monitoring result; The emergency management method determination module is used to determine the emergency management method corresponding to the disaster type according to the disaster type.

9. A computer device, It is characterized in that The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and implement the natural disaster emergency management method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the natural disaster emergency management method according to any one of claims 1 to 7.

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