Method, device, computer equipment and storage medium for early warning of earthquake loss of target object

Abstract

The embodiments of the present application belong to the field of artificial intelligence, and relate to a method, device, computer equipment and storage medium for early warning of earthquake losses of a target object, the method comprising: obtaining earthquake monitoring data of the target object according to a received earthquake occurrence notification signal; for each earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve; obtaining target characteristic information of the target object to determine a second loss assessment value according to the target characteristic information; inputting the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value; generating earthquake loss early warning information of the target object according to the earthquake loss assessment value. In addition, the present application also relates to blockchain technology, and earthquake monitoring data can be stored in a blockchain. The present application improves the efficiency of earthquake loss early warning.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, device, computer equipment and storage medium for early warning of earthquake losses of a target object. Background Art

[0002] Earthquakes, also known as ground movements or seismic vibrations, are vibrations caused by the rapid release of energy from the earth's crust. Earthquakes are an extremely common natural phenomenon. According to statistics, tens of thousands of earthquakes occur on Earth every day, most of which are too small or too far away for people to feel. However, when an earthquake reaches a certain level, it may cause damage to housing property and personal safety, causing losses to people.

[0003] Insuring personal property such as houses is a way to reduce earthquake losses. The insured house and building can be called the subject matter. Currently, earthquake loss assessment and early warning of the subject matter are usually carried out after the earthquake occurs. The insured actively reports that the subject matter has been destroyed, and then conducts manual inspections, which takes a long time. The insured may not even know whether the subject matter has been damaged in the earthquake in time, and cannot respond to the loss of the subject matter in time. Therefore, the current efficiency of early warning of earthquake losses of the subject matter is low. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, computer equipment and storage medium for warning of earthquake losses of target objects, so as to solve the problem of low efficiency of warning of earthquake losses of target objects.

[0005] In order to solve the above technical problems, the present application provides a method for early warning of earthquake losses of a target object, which adopts the following technical solution:

[0006] Acquiring earthquake monitoring data of the target object according to the received earthquake occurrence notification signal;

[0007] For each item of earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve;

[0008] Acquiring target characteristic information of the target object to determine a second loss assessment value according to the target characteristic information;

[0009] Inputting the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value;

[0010] Earthquake loss warning information of the target object is generated according to the earthquake loss assessment value.

[0011] In order to solve the above technical problems, the embodiment of the present application further provides an earthquake loss warning device for a target object, which adopts the following technical solution:

[0012] A monitoring acquisition module, used to acquire earthquake monitoring data of the target object according to the received earthquake occurrence notification signal;

[0013] A first determination module, configured to determine, for each earthquake monitoring sub-data in the earthquake monitoring data, a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve;

[0014] A second determination module, configured to obtain target feature information of the target object, so as to determine a second loss assessment value according to the target feature information;

[0015] A loss assessment module, used for inputting the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value;

[0016] An information generation module is used to generate earthquake loss warning information of the target object according to the earthquake loss assessment value.

[0017] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0018] Acquiring earthquake monitoring data of the target object according to the received earthquake occurrence notification signal;

[0019] For each item of earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve;

[0020] Acquiring target characteristic information of the target object to determine a second loss assessment value according to the target characteristic information;

[0021] Inputting the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value;

[0022] Earthquake loss warning information of the target object is generated according to the earthquake loss assessment value.

[0023] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0024] Acquiring earthquake monitoring data of the target object according to the received earthquake occurrence notification signal;

[0025] For each item of earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve;

[0026] Acquiring target characteristic information of the target object to determine a second loss assessment value according to the target characteristic information;

[0027] Inputting the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value;

[0028] Earthquake loss warning information of the target object is generated according to the earthquake loss assessment value.

[0029] Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects: after receiving an earthquake notification signal, earthquake monitoring data of the target object is obtained; the earthquake monitoring data may include multiple earthquake monitoring sub-data, and for each earthquake monitoring sub-data, a first loss assessment value is determined according to an earthquake loss assessment curve; the earthquake loss assessment curve is established in advance, and different earthquake loss assessment curves reflect the losses caused to the target object by different independent variables; then the characteristic information of the target object is obtained, and a second loss assessment value brought by the characteristic attributes of the target object itself is determined according to the characteristic information of the target object; then the total earthquake loss assessment value is calculated according to the earthquake loss assessment model, comprehensively considering the target object's own and external influencing factors, thereby improving the accuracy of the earthquake loss assessment, and generating earthquake loss warning information, thereby realizing automatic warning of earthquake losses of the target object and improving the efficiency of earthquake loss warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. 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.

[0031] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0032] Figure 2 is a flow chart of an embodiment of a method for early warning of earthquake losses of a subject matter according to the present application;

[0033] Figure 3 It is an earthquake loss assessment curve under the influence of an earthquake magnitude according to the present application;

[0034] Figure 4 It is an earthquake loss assessment curve under the influence of an earthquake intensity according to this application;

[0035] Figure 5 It is an earthquake loss assessment curve under the influence of a duration according to this application;

[0036] Figure 6 is a schematic structural diagram of an embodiment of an earthquake loss warning device for a subject matter according to the present application;

[0037] Figure 7 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0039] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0040] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0041] like Figure 1 As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0042] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0043] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0044] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0045] It should be noted that the target object earthquake loss warning method provided in the embodiment of the present application is generally executed by a server, and accordingly, the target object earthquake loss warning device is generally set in the server.

[0046] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0047] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for early warning of earthquake losses of a target object according to the present application. The method for early warning of earthquake losses of a target object comprises the following steps:

[0048] Step S201, acquiring earthquake monitoring data of the target object according to the received earthquake occurrence notification signal.

[0049] In this embodiment, the target object earthquake loss early warning method is executed on the electronic device (eg Figure 1 The server shown in the figure) can communicate with the terminal through a wired connection or a wireless connection. It should be noted that the above wireless connection method may include but is not limited to 3G / 4G / 5G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0050] The earthquake occurrence notification signal may be a notification signal after an earthquake occurs. Earthquake monitoring data may be data related to earthquakes that are monitored, and the earthquake monitoring data may include earthquake monitoring sub-data of various data types, for example, earthquake magnitude, earthquake intensity, earthquake duration, and geological information.

[0051] Specifically, when an earthquake notification signal is received, earthquake monitoring data corresponding to the subject matter is obtained. The subject matter in this application can be various types of buildings. The earthquake notification signal can be sent by a server of the earthquake bureau, and the earthquake monitoring data can also be obtained from the server of the earthquake bureau.

[0052] It should be emphasized that in order to further ensure the privacy and security of the above-mentioned earthquake monitoring data, the above-mentioned earthquake monitoring data can also be stored in a blockchain node.

[0053] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0054] Step S202: for each earthquake monitoring sub-data in the earthquake monitoring data, determine a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve.

[0055] Specifically, earthquake monitoring data may include multiple earthquake monitoring sub-data, and earthquake monitoring sub-data may include earthquake magnitude, earthquake intensity, duration and geological information. The earthquake loss assessment curve is pre-established and can show the relationship between the degree of loss of the subject matter and the earthquake monitoring sub-data. The degree of loss is measured by the loss assessment value, which can be a value between [0,1]. When the loss assessment value is 0, it means that the subject matter has not suffered any loss or damage at all; when the loss assessment value is 1, it means that the subject matter is completely destroyed. The earthquake loss assessment curve can be a single dependent variable-single independent variable curve, that is, in an earthquake loss assessment curve, the independent variable is a single type of earthquake monitoring sub-data, and the dependent variable is the loss assessment value.

[0056] For each earthquake monitoring sub-data in the earthquake monitoring data, an earthquake loss assessment curve corresponding to the earthquake monitoring sub-data is obtained, and then a first loss assessment value is determined in the earthquake loss assessment curve according to the data value of the earthquake monitoring sub-data.

[0057] Step S203, obtaining target characteristic information of the target object to determine a second loss assessment value according to the target characteristic information.

[0058] Specifically, the extent of the loss of the target object in an earthquake is not only affected by external earthquake monitoring data, but also by some properties of the target object itself. Therefore, the target feature information of the target object can also be obtained. The target feature information is information that records the characteristic attributes of the target object itself.

[0059] An earthquake loss assessment curve of target characteristic information and loss assessment value may be established in advance, and then the second loss assessment value corresponding to the target characteristic is determined based on the earthquake loss assessment curve.

[0060] In one embodiment, the target feature in the target feature information may include the existence time of the target object. The target object may be a building. Buildings have the problem of aging over time. The longer the building exists, the higher the degree of aging, and the higher the possibility of being destroyed in an earthquake. Therefore, the existence time of the target object can be used as the target feature information. For the earthquake loss assessment curve, when the independent variable is the existence time, the curve is similar to the form of an exponential function.

[0061] Step S204: input the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value.

[0062] Specifically, the first loss assessment value and the second loss assessment value are input into the earthquake loss assessment model, which can be a model that predicts the overall degree of damage to the subject matter based on the first loss assessment value and the second loss assessment value. The earthquake loss assessment model can be calculated based on the first loss assessment value and the second loss assessment value to obtain an earthquake loss assessment value, which can comprehensively consider various external and internal factors to measure the degree of damage to the subject matter.

[0063] In one embodiment, the earthquake loss assessment model can be built based on a neural network.

[0064] Step S205: generating earthquake loss warning information of the target object according to the earthquake loss assessment value.

[0065] Specifically, the earthquake loss assessment value reflects the degree of damage to the subject matter in numerical terms. Generally, the larger the earthquake loss assessment value, the more serious the damage to the subject matter. A standard assessment value can be set in advance. There are multiple standard assessment values, which form multiple assessment intervals. The assessment interval divides the earthquake loss degree into several states. The earthquake loss assessment value is compared with each standard assessment value to determine the assessment interval in which the earthquake loss assessment value is located. The state corresponding to the assessment interval and the earthquake loss assessment value are used as earthquake loss warning information.

[0066] In one embodiment, the loss amount of the subject matter can also be calculated based on the value of the subject matter and the earthquake loss assessment value. The value of the loss amount can be obtained by multiplying the value of the subject matter and the earthquake loss assessment value, and the loss amount can also be used as a kind of information in the earthquake loss warning information.

[0067] In this embodiment, after receiving an earthquake notification signal, earthquake monitoring data of the target object is obtained; the earthquake monitoring data may include multiple earthquake monitoring sub-data, and for each earthquake monitoring sub-data, a first loss assessment value is determined according to an earthquake loss assessment curve; the earthquake loss assessment curve is pre-established, and different earthquake loss assessment curves reflect the losses caused to the target object by different independent variables; then the characteristic information of the target object is obtained, and a second loss assessment value brought by the characteristic attributes of the target object itself is determined according to the characteristic information of the target object; then the total earthquake loss assessment value is calculated according to the earthquake loss assessment model, comprehensively considering the target object's own and external influencing factors, thereby improving the accuracy of the earthquake loss assessment, and generating earthquake loss warning information, thereby realizing automatic warning of earthquake losses of the target object and improving the efficiency of earthquake loss warning.

[0068] Furthermore, the above step S201 may include: receiving an earthquake notification signal; obtaining full earthquake monitoring data according to the earthquake notification signal; searching for a target object based on geographic area information in the full earthquake monitoring data; and obtaining earthquake monitoring data of the target object from the full earthquake monitoring data.

[0069] Specifically, after receiving the earthquake occurrence notification signal, the server can obtain the full amount of earthquake monitoring data. The full amount of earthquake monitoring data can be the full amount of data related to the recorded earthquake. The full amount of earthquake monitoring data can also include geographical area information, and each set of geographical area information corresponds to a geographical area that is affected by the earthquake.

[0070] The full amount of earthquake monitoring data can be a collection of earthquake monitoring data from different geographical areas. For different geographical areas, the specific earthquake monitoring data is also different. For example, the earthquake intensity may be different in two different geographical areas. The server searches for the target object in the geographical area corresponding to the geographical area information, and then obtains the earthquake monitoring data of the target object from the full amount of earthquake monitoring data. If the target object is not found in a geographical area, it is not necessary to process the earthquake monitoring data corresponding to the geographical area.

[0071] In one embodiment, the geographical area where the earthquake impact reaches the preset conditions can be screened based on the full amount of earthquake monitoring data, and then the target object can be searched in the geographical area. For example, if the magnitude of an earthquake in a certain geographical area is 0.8, the earthquake intensity is 0.6, and the duration is 20s, the geographical area is too small to be affected by the earthquake and has almost no impact, so it is not necessary to calculate the target object in the geographical area.

[0072] The subject matter in the present application may be a building, and the geographical location information of the building is obtained in advance, and then the geographical location information of the building is stored in a database, and then the subject matter is selected based on the geographical location information and geographical area information.

[0073] In this embodiment, the full amount of earthquake monitoring data is first obtained, and then a search is performed to determine whether there are target objects based on the geographical area information in the full amount of earthquake monitoring data, so as to determine which target objects need to be processed and which earthquake monitoring data need to be calculated.

[0074] Furthermore, the above-mentioned step of obtaining the full amount of earthquake monitoring data according to the earthquake notification signal may include: forwarding the initial earthquake monitoring data to the message queue through a preset interface according to the earthquake notification signal; preprocessing the initial earthquake monitoring data in the message queue through flink to obtain the full amount of earthquake monitoring data.

[0075] Specifically, after receiving the earthquake notification signal, the initial earthquake monitoring data can be obtained through a preset interface. The initial earthquake monitoring data can be the original data collected by the earthquake bureau, and the server of the earthquake bureau can provide an interface through which the server interacts with the server of the earthquake bureau.

[0076] The server can first forward the initial earthquake monitoring data collected by the Earthquake Bureau to the message queue. For example, the initial earthquake monitoring data can be forwarded to ActiveMQ, which is a message middleware written in Java and runs on a Java virtual machine. The flink cluster then pre-processes the initial earthquake monitoring data in the message queue to obtain the full amount of earthquake monitoring data. Among them, the pre-processing can be to convert the initial earthquake monitoring data into standard data in a standard data format, and verify whether the standard data is complete and correct to obtain the full amount of earthquake monitoring data. In one embodiment, the full amount of earthquake monitoring data can also be persisted to the database.

[0077] In this embodiment, the initial earthquake monitoring data is processed through big data technologies such as message queues and flink, thereby improving the timeliness of data processing.

[0078] Furthermore, the above-mentioned step S202 may include: obtaining the target type of the target object; for each earthquake monitoring sub-data in the earthquake monitoring data, selecting an earthquake loss assessment curve from pre-established earthquake loss assessment curves according to the data type of the earthquake monitoring sub-data and the target type; and determining the first loss assessment value corresponding to the earthquake monitoring sub-data according to the earthquake loss assessment curve.

[0079] Specifically, the subject matter can have multiple subject types, and the degree of damage to the subject matter of different subject types may vary greatly under the same earthquake monitoring data. For example, the earthquake resistance of ordinary houses and large stadiums varies greatly. Therefore, for each earthquake monitoring sub-data in the earthquake monitoring data, the corresponding earthquake loss assessment curve can be obtained according to the subject type of the subject matter and the data type of the earthquake monitoring sub-data. For example, according to the subject types "self-built houses in towns" and "earthquake magnitude", the corresponding earthquake loss assessment curve is obtained.

[0080] After the earthquake loss assessment curve is obtained, a query is performed in the earthquake loss assessment curve according to the earthquake monitoring sub-data to obtain a loss assessment value under the influence of the earthquake monitoring sub-data, that is, a first loss assessment value.

[0081] The earthquake loss assessment curve is obtained based on the historical disaster data of the same type of subject matter as the subject matter. For example, when the subject matter is a building, based on the disaster data of various types of buildings damaged by earthquakes, the degree of loss of various types of buildings under different earthquake magnitudes, the degree of loss under different earthquake intensities, the degree of loss under the influence of different durations, and the degree of loss under different geology are obtained, thereby obtaining an earthquake loss assessment curve with the independent variables being earthquake magnitude, earthquake intensity, duration, and geology. Usually, an earthquake loss assessment curve with the independent variables being earthquake magnitude, earthquake intensity, and duration is usually an "S"-shaped curve. Specifically, refer to Figure 3 , Figure 4 and Figure 5 , which respectively show the earthquake loss assessment curve under the influence of earthquake magnitude, the earthquake loss assessment curve under the influence of earthquake intensity, and the earthquake loss assessment curve under the influence of duration.

[0082] In this embodiment, the corresponding earthquake loss assessment curve is obtained according to the target type and the data type of the earthquake monitoring sub-data, thereby ensuring the accuracy of the selection of the earthquake loss assessment curve and thus ensuring the accuracy of the first loss assessment value.

[0083] Furthermore, the above step S204 may include: inputting the first loss assessment value and the second loss assessment value into a trained earthquake loss assessment model, where the earthquake loss assessment model is a multiple linear regression model; obtaining an output result of the multiple linear regression model, and determining the output result as the earthquake loss assessment value.

[0084] Specifically, each first loss assessment value and second loss assessment value are input into an earthquake loss assessment model, which may be a pre-trained multiple linear regression model. The earthquake loss assessment model may add corresponding weights to the first loss assessment value and the second loss assessment value, and then perform linear operations on the first loss assessment value and the second loss assessment value. The output result of the multiple linear regression model will be used as the earthquake loss assessment value:

[0085] L=a1s1+a2s2+a3s3+Λ+a n s n (1)

[0086] Among them, L is the earthquake loss assessment value, S i is the first loss assessment value or the second loss assessment value, a i For S i The weight of .

[0087] In this embodiment, the earthquake loss assessment model may be a multiple linear regression model, which may integrate the first loss assessment value and the second loss assessment value to obtain an earthquake loss assessment value that comprehensively measures the degree of damage.

[0088] Furthermore, before the above step S204, it can also include: obtaining historical earthquake loss data; extracting a first loss assessment value corresponding to the earthquake monitoring sub-data, a second loss assessment value corresponding to the target characteristic information, and an earthquake loss assessment value from the historical earthquake data; training an initial earthquake loss assessment model according to the first loss assessment value, the second loss assessment value, and the earthquake loss assessment value to obtain an earthquake loss assessment model, wherein the initial earthquake loss assessment model is a multivariate linear regression model.

[0089] Specifically, it is necessary to first obtain an earthquake loss assessment model through model training. During training, historical earthquake loss data is obtained, and the historical earthquake loss data records the first loss assessment value corresponding to various earthquake monitoring sub-data, the second loss assessment value corresponding to the target feature information, and the earthquake loss assessment value. Among them, the earthquake loss assessment data can be determined manually.

[0090] Then, the first loss assessment value corresponding to each type of earthquake monitoring sub-data and the second loss assessment value corresponding to the target characteristic information are used as inputs of the initial earthquake loss assessment model, and the earthquake loss assessment value is used as a label to train the initial earthquake loss assessment model, thereby obtaining an earthquake loss assessment model. Among them, the initial earthquake loss assessment model can be a multivariate linear regression model. After training, the multivariate linear regression model can determine the weights of the loss assessment values ​​brought by various types of earthquake monitoring sub-data and target characteristic information.

[0091] In this embodiment, an initial earthquake loss assessment model is trained based on historical earthquake loss data, thereby determining the weights of loss assessment values ​​brought by various earthquake monitoring sub-data and target feature information, thereby ensuring the accuracy of earthquake loss assessment value calculation.

[0092] Furthermore, after the above step S205, the following may also be included: querying the account associated with the subject matter, the account including the customer account and the management account; and sending the earthquake loss warning information to the terminal logged in to the account.

[0093] Specifically, the server can query the account associated with the subject matter, which includes the customer account and the management account. The customer account can be the account of the insured of the subject matter, and the management account can be the account of the risk control staff of the insurance company. The earthquake loss warning information is sent to the terminal logged in by the account, so as to inform the customer and management personnel of the loss warning information of the subject matter in a timely manner so that they can prepare countermeasures.

[0094] In this embodiment, earthquake loss warning information is sent to the terminal logged in by the customer account and the management account, so as to promptly inform the damage of the subject matter and implement loss warning.

[0095] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0096] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0097] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0098] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0099] Further references Figure 6 , as a response to the above Figure 2 The present application provides an embodiment of an earthquake loss early warning device for a target object, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0100] like Figure 6 As shown, the target object earthquake loss warning device 300 described in this embodiment includes: a monitoring acquisition module 301, a first determination module 302, a second determination module 303, a loss assessment module 304 and an information generation module 305, wherein:

[0101] The monitoring acquisition module 301 is used to acquire earthquake monitoring data of the target object according to the received earthquake occurrence notification signal.

[0102] The first determination module 302 is used to determine, for each earthquake monitoring sub-data in the earthquake monitoring data, a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve.

[0103] The second determination module 303 is used to obtain target feature information of the target object to determine a second loss assessment value according to the target feature information.

[0104] The loss assessment module 304 is used to input the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value.

[0105] The information generation module 305 is used to generate earthquake loss warning information of the target object according to the earthquake loss assessment value.

[0106] In this embodiment, after receiving an earthquake notification signal, earthquake monitoring data of the target object is obtained; the earthquake monitoring data may include multiple earthquake monitoring sub-data, and for each earthquake monitoring sub-data, a first loss assessment value is determined according to an earthquake loss assessment curve; the earthquake loss assessment curve is pre-established, and different earthquake loss assessment curves reflect the losses caused to the target object by different independent variables; then the characteristic information of the target object is obtained, and a second loss assessment value brought by the characteristic attributes of the target object itself is determined according to the characteristic information of the target object; then the total earthquake loss assessment value is calculated according to the earthquake loss assessment model, comprehensively considering the target object's own and external influencing factors, thereby improving the accuracy of the earthquake loss assessment, and generating earthquake loss warning information, thereby realizing automatic warning of earthquake losses of the target object and improving the efficiency of earthquake loss warning.

[0107] In some optional implementations of this embodiment, the monitoring acquisition module 301 may include: a signal receiving submodule, a full acquisition submodule, a target object search submodule and a monitoring acquisition submodule, wherein:

[0108] The signal receiving submodule is used to receive earthquake notification signals.

[0109] The full-volume acquisition submodule is used to obtain the full amount of earthquake monitoring data based on the earthquake notification signal.

[0110] The target object search submodule is used to search for the target object based on the geographical area information in the full amount of earthquake monitoring data.

[0111] The monitoring acquisition submodule is used to obtain the seismic monitoring data of the target object from the full amount of seismic monitoring data.

[0112] In this embodiment, the full amount of earthquake monitoring data is first obtained, and then a search is performed to determine whether there are target objects based on the geographical area information in the full amount of earthquake monitoring data, so as to determine which target objects need to be processed and which earthquake monitoring data need to be calculated.

[0113] In some optional implementations of this embodiment, the full amount acquisition submodule may include: an initial forwarding unit and a preprocessing unit, wherein:

[0114] The initial forwarding unit is used to forward the initial earthquake monitoring data to the message queue through a preset interface according to the earthquake occurrence notification signal.

[0115] The preprocessing unit is used to preprocess the initial earthquake monitoring data in the message queue through Flink to obtain the full amount of earthquake monitoring data.

[0116] In this embodiment, the initial earthquake monitoring data is processed through big data technologies such as message queues and flink, thereby improving the timeliness of data processing.

[0117] In some optional implementations of this embodiment, the first determination module 302 may include: a type acquisition submodule, a curve selection submodule and a first determination submodule, wherein:

[0118] The type acquisition submodule is used to obtain the target type of the target object.

[0119] The curve selection submodule is used to select an earthquake loss assessment curve from pre-established earthquake loss assessment curves for each earthquake monitoring sub-data in the earthquake monitoring data according to the data type and target type of the earthquake monitoring sub-data.

[0120] The first determination submodule is used to determine a first loss assessment value corresponding to the earthquake monitoring sub-data according to the earthquake loss assessment curve.

[0121] In this embodiment, the corresponding earthquake loss assessment curve is obtained according to the target type and the data type of the earthquake monitoring sub-data, thereby ensuring the accuracy of the selection of the earthquake loss assessment curve and thus ensuring the accuracy of the first loss assessment value.

[0122] In some optional implementations of this embodiment, the loss assessment module 304 may include: an assessment value input submodule and a result acquisition submodule, wherein:

[0123] The assessment value input submodule is used to input the first loss assessment value and the second loss assessment value into the trained earthquake loss assessment model, and the earthquake loss assessment model is a multivariate linear regression model.

[0124] The result acquisition submodule is used to obtain the output results of the multivariate linear regression model and determine the output results as earthquake loss assessment values.

[0125] In this embodiment, the earthquake loss assessment model may be a multiple linear regression model, which may integrate the first loss assessment value and the second loss assessment value to obtain an earthquake loss assessment value that comprehensively measures the degree of damage.

[0126] In some optional implementations of this embodiment, the target object earthquake loss warning device 300 may further include: a history acquisition module, an evaluation value extraction module and a model training module, wherein:

[0127] The historical acquisition module is used to obtain historical earthquake loss data.

[0128] The assessment value extraction module is used to extract the first loss assessment value corresponding to the earthquake monitoring sub-data, the second loss assessment value corresponding to the target characteristic information, and the earthquake loss assessment value from the historical earthquake data.

[0129] The model training module is used to train an initial earthquake loss assessment model according to the first loss assessment value, the second loss assessment value and the earthquake loss assessment value to obtain an earthquake loss assessment model, wherein the initial earthquake loss assessment model is a multivariate linear regression model.

[0130] In this embodiment, an initial earthquake loss assessment model is trained based on historical earthquake loss data, thereby determining the weights of loss assessment values ​​brought by various earthquake monitoring sub-data and target feature information, thereby ensuring the accuracy of earthquake loss assessment value calculation.

[0131] In some optional implementations of this embodiment, the target object earthquake loss warning device 300 may further include: an account query module and an information sending module, wherein:

[0132] The account query module is used to query the account associated with the subject matter, including customer accounts and management accounts.

[0133] The information sending module is used to send earthquake loss warning information to the terminal where the account is logged in.

[0134] In this embodiment, earthquake loss warning information is sent to the terminal logged in by the customer account and the management account, so as to promptly inform the damage of the subject matter and implement loss warning.

[0135] To solve the above technical problems, the present application also provides a computer device. Figure 7 , Figure 7 This is a basic structural block diagram of the computer device in this embodiment.

[0136] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 4 with components 41-43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0137] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0138] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 4. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the target earthquake loss early warning method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0139] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run the computer-readable instructions stored in the memory 41 or process data, such as running the computer-readable instructions of the target object earthquake loss early warning method.

[0140] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0141] The computer device provided in this embodiment can execute the above-mentioned target object earthquake loss early warning method. Here, the target object earthquake loss early warning method can be the target object earthquake loss early warning method of each of the above-mentioned embodiments.

[0142] In this embodiment, after receiving an earthquake notification signal, earthquake monitoring data of the target object is obtained; the earthquake monitoring data may include multiple earthquake monitoring sub-data, and for each earthquake monitoring sub-data, a first loss assessment value is determined according to an earthquake loss assessment curve; the earthquake loss assessment curve is pre-established, and different earthquake loss assessment curves reflect the losses caused to the target object by different independent variables; then the characteristic information of the target object is obtained, and a second loss assessment value brought by the characteristic attributes of the target object itself is determined according to the characteristic information of the target object; then the total earthquake loss assessment value is calculated according to the earthquake loss assessment model, comprehensively considering the target object's own and external influencing factors, thereby improving the accuracy of the earthquake loss assessment, and generating earthquake loss warning information, thereby realizing automatic warning of earthquake losses of the target object and improving the efficiency of earthquake loss warning.

[0143] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the target object earthquake loss warning method as described above.

[0144] In this embodiment, after receiving an earthquake notification signal, earthquake monitoring data of the target object is obtained; the earthquake monitoring data may include multiple earthquake monitoring sub-data, and for each earthquake monitoring sub-data, a first loss assessment value is determined according to an earthquake loss assessment curve; the earthquake loss assessment curve is pre-established, and different earthquake loss assessment curves reflect the losses caused to the target object by different independent variables; then the characteristic information of the target object is obtained, and a second loss assessment value brought by the characteristic attributes of the target object itself is determined according to the characteristic information of the target object; then the total earthquake loss assessment value is calculated according to the earthquake loss assessment model, comprehensively considering the target object's own and external influencing factors, thereby improving the accuracy of the earthquake loss assessment, and generating earthquake loss warning information, thereby realizing automatic warning of earthquake losses of the target object and improving the efficiency of earthquake loss warning.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0146] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A method for early warning of earthquake losses of a target object, characterized in that: The steps include: Acquiring earthquake monitoring data of the target object according to the received earthquake occurrence notification signal; For each item of earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve; Acquiring target characteristic information of the target object to determine a second loss assessment value according to the target characteristic information; Inputting the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value; generating earthquake loss warning information of the target object according to the earthquake loss assessment value; The step of determining, for each earthquake monitoring sub-data in the earthquake monitoring data, a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve comprises: Obtaining the subject type of the subject matter, where the subject matter has multiple subject types; For each earthquake monitoring sub-data in the earthquake monitoring data, according to the data type of the earthquake monitoring sub-data and the type of the target, an earthquake loss assessment curve is selected from pre-established earthquake loss assessment curves, wherein the earthquake loss assessment curve is obtained based on historical disaster data of targets of the same type as the target; Determining a first loss assessment value corresponding to the earthquake monitoring sub-data according to the earthquake loss assessment curve; The step of obtaining the target characteristic information of the target object to determine the second loss assessment value according to the target characteristic information includes: Acquire characteristic information of the subject matter, wherein the characteristic information is information recording characteristic attributes of the subject matter itself; An earthquake loss assessment curve of the target characteristic information and the loss assessment value is pre-established, and a second loss assessment value corresponding to the target characteristic information is determined based on the earthquake loss assessment curve.

2. The target object earthquake loss early warning method according to claim 1, characterized in that: The step of acquiring earthquake monitoring data of the target object according to the received earthquake occurrence notification signal comprises: Receive earthquake notification signals; Acquiring full earthquake monitoring data according to the earthquake occurrence notification signal; Searching for a target object based on the geographical area information in the full amount of earthquake monitoring data; The seismic monitoring data of the target object is obtained from the full amount of seismic monitoring data.

3. The target object earthquake loss early warning method according to claim 2, characterized in that: The step of obtaining the full amount of earthquake monitoring data according to the earthquake occurrence notification signal comprises: According to the earthquake occurrence notification signal, the initial earthquake monitoring data is forwarded to the message queue through a preset interface; The initial earthquake monitoring data in the message queue is preprocessed through Flink to obtain the full amount of earthquake monitoring data.

4. The target object earthquake loss early warning method according to claim 1, characterized in that: The step of inputting the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value comprises: Inputting the first loss assessment value and the second loss assessment value into a trained earthquake loss assessment model, wherein the earthquake loss assessment model is a multivariate linear regression model; The output result of the multivariate linear regression model is obtained, and the output result is determined as an earthquake loss assessment value.

5. The target object earthquake loss early warning method according to claim 1, characterized in that: Before the step of inputting the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value, the method further includes: Obtain historical earthquake loss data; Extracting a first loss assessment value corresponding to the earthquake monitoring sub-data, a second loss assessment value corresponding to the target characteristic information, and an earthquake loss assessment value from the historical earthquake loss data; An initial earthquake loss assessment model is trained according to the first loss assessment value, the second loss assessment value and the earthquake loss assessment value to obtain an earthquake loss assessment model, wherein the initial earthquake loss assessment model is a multivariate linear regression model.

6. The target object earthquake loss early warning method according to claim 1, characterized in that: After the step of generating earthquake loss warning information of the target object according to the earthquake loss assessment value, the method further includes: Querying the account number associated with the subject matter, the account number including the customer account number and the management account number; The earthquake loss warning information is sent to the terminal logged in to the account.

7. A target object earthquake loss warning device, characterized in that: include: A monitoring acquisition module, used to acquire earthquake monitoring data of the target object according to the received earthquake occurrence notification signal; A first determination module, configured to determine, for each earthquake monitoring sub-data in the earthquake monitoring data, a first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve; Wherein, the first determination module includes: a type acquisition submodule, a curve selection submodule and a first determination submodule; The type acquisition submodule is used to acquire the subject type of the subject matter, and the subject matter has multiple subject types; The curve selection submodule is used to select an earthquake loss assessment curve from pre-established earthquake loss assessment curves for each earthquake monitoring sub-data in the earthquake monitoring data according to the data type of the earthquake monitoring sub-data and the target type, wherein the earthquake loss assessment curve is obtained according to the historical disaster data of the target object of the same type as the target object; The first determination submodule is used to determine the first loss assessment value corresponding to the earthquake monitoring sub-data according to the earthquake loss assessment curve; A second determination module, configured to obtain target feature information of the target object, so as to determine a second loss assessment value according to the target feature information; The target characteristic information is information recording the characteristic attributes of the target object itself; an earthquake loss assessment curve of the target characteristic information and the loss assessment value is pre-established, and a second loss assessment value corresponding to the target characteristic information is determined based on the earthquake loss assessment curve; A loss assessment module, used for inputting the first loss assessment value and the second loss assessment value into an earthquake loss assessment model to obtain an earthquake loss assessment value; An information generation module is used to generate earthquake loss warning information of the target object according to the earthquake loss assessment value.

8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the target object earthquake loss warning method as described in any one of claims 1 to 6 when executing the computer-readable instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the target object earthquake loss warning method according to any one of claims 1 to 6.

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