CN114442151B - Method, device, computer equipment and storage medium for early warning of earthquake loss of target object - Google Patents
Method, device, computer equipment and storage medium for early warning of earthquake loss of target object Download PDFInfo
- Publication number
- CN114442151B CN114442151B CN202210086260.XA CN202210086260A CN114442151B CN 114442151 B CN114442151 B CN 114442151B CN 202210086260 A CN202210086260 A CN 202210086260A CN 114442151 B CN114442151 B CN 114442151B
- Authority
- CN
- China
- Prior art keywords
- earthquake
- seismic
- loss evaluation
- loss
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000012544 monitoring process Methods 0.000 claims abstract description 157
- 238000011156 evaluation Methods 0.000 claims description 247
- 238000013210 evaluation model Methods 0.000 claims description 44
- 238000012417 linear regression Methods 0.000 claims description 17
- 238000012549 training Methods 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 7
- 238000005516 engineering process Methods 0.000 abstract description 13
- 238000013473 artificial intelligence Methods 0.000 abstract description 7
- 238000007726 management method Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/282—Application of seismic models, synthetic seismograms
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
本申请实施例属于人工智能领域，涉及一种标的物地震损失预警方法、装置、计算机设备及存储介质，方法包括：根据接收到的地震发生通知信号，获取标的物的地震监测数据；对于地震监测数据中的每项地震监测子数据，根据预先建立的地震损失评估曲线确定地震监测子数据所对应的第一损失评估值；获取标的物的标的特征信息，以根据标的特征信息确定第二损失评估值；将第一损失评估值和第二损失评估值输入地震损失评估模型，得到地震损失评估值；根据地震损失评估值生成标的物的地震损失预警信息。此外，本申请还涉及区块链技术，地震监测数据可存储于区块链中。本申请提高了地震损失预警效率。
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 the first loss assessment value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss assessment curve; obtaining the target characteristic information of the target object to determine the 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
The application relates to the technical field of artificial intelligence, in particular to a target earthquake loss early warning method, a target earthquake loss early warning device, computer equipment and a storage medium.
Background
Earthquake, also known as earth movement or earth vibration, is vibration caused in the process of rapidly releasing energy from the earth crust. Earthquake is an extremely common natural phenomenon, and is counted to occur tens of thousands of times daily on earth, most of which are too small or too far to be felt by people. However, when the earthquake reaches a certain degree, damage may be brought to property and personal safety of the house, and people suffer from loss.
The method is a way for reducing earthquake loss and ensuring personal property such as houses. The house building being covered may be referred to as a target. At present, the earthquake loss evaluation and early warning of the target object usually are carried out after the earthquake occurs, the target object is actively reported to be destroyed by an applicant, then the investigation is carried out manually, the time consumption is long, the applicant can not know whether the target object is damaged in the earthquake or not in time, and can not respond to the loss of the target object in time, so that the earthquake loss early warning efficiency of the current target object is low.
Disclosure of Invention
The embodiment of the application aims to provide a method, a device, computer equipment and a storage medium for early warning of the earthquake loss of a target object, so as to solve the problem of low early warning efficiency of the earthquake loss of the target object.
In order to solve the technical problems, the embodiment of the application provides a target earthquake loss early warning method, which adopts the following technical scheme:
acquiring seismic monitoring data of a target object according to the received seismic occurrence notification signal;
For each item of earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss evaluation value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss evaluation curve;
Acquiring target characteristic information of the target object to determine a second loss evaluation value according to the target characteristic information;
Inputting the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model to obtain a seismic loss evaluation value;
and generating earthquake loss early warning information of the target object according to the earthquake loss evaluation value.
In order to solve the technical problems, the embodiment of the application also provides a target earthquake loss early warning device, which adopts the following technical scheme:
The monitoring acquisition module is used for acquiring the earthquake monitoring data of the object according to the received earthquake occurrence notification signal;
The first determining module is used for determining a first loss evaluation value corresponding to each item of earthquake monitoring sub-data in the earthquake monitoring data according to a pre-established earthquake loss evaluation curve;
a second determining module, configured to obtain target feature information of the target object, so as to determine a second loss evaluation value according to the target feature information;
The loss evaluation module is used for inputting the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model to obtain a seismic loss evaluation value;
And the information generation module is used for generating earthquake loss early warning information of the target object according to the earthquake loss evaluation value.
In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:
acquiring seismic monitoring data of a target object according to the received seismic occurrence notification signal;
For each item of earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss evaluation value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss evaluation curve;
Acquiring target characteristic information of the target object to determine a second loss evaluation value according to the target characteristic information;
Inputting the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model to obtain a seismic loss evaluation value;
and generating earthquake loss early warning information of the target object according to the earthquake loss evaluation value.
In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:
acquiring seismic monitoring data of a target object according to the received seismic occurrence notification signal;
For each item of earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss evaluation value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss evaluation curve;
Acquiring target characteristic information of the target object to determine a second loss evaluation value according to the target characteristic information;
Inputting the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model to obtain a seismic loss evaluation value;
and generating earthquake loss early warning information of the target object according to the earthquake loss evaluation value.
Compared with the prior art, the method and the device have the advantages that after receiving the earthquake occurrence notification signal, the method and the device acquire the earthquake monitoring data of the target object, the earthquake monitoring data can comprise a plurality of items of earthquake monitoring sub-data, the first loss evaluation value is determined according to the earthquake loss evaluation curve for each item of earthquake monitoring sub-data, the earthquake loss evaluation curve is pre-established, different earthquake loss evaluation curves reflect losses of different independent variables on the target object, then the target characteristic information is acquired, the second loss evaluation value brought by the characteristic attribute of the target object is determined according to the target characteristic information, then the total earthquake loss evaluation value is calculated according to the earthquake loss evaluation model, the self and external influence factors of the target object are comprehensively considered, the accuracy of earthquake loss evaluation is improved, the earthquake loss early warning information is generated, the automatic early warning of the earthquake loss of the target object is realized, and the earthquake loss early warning efficiency is improved.
Drawings
In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a subject seismic loss warning method according to the present disclosure;
FIG. 3 is a plot of seismic loss assessment under the influence of a seismic magnitude in accordance with the present application;
FIG. 4 is a plot of seismic loss assessment under the influence of a seismic intensity in accordance with the present application;
FIG. 5 is a plot of seismic loss assessment under the influence of one duration in accordance with the present application;
FIG. 6 is a schematic structural view of one embodiment of a subject seismic loss warning device according to the present disclosure;
FIG. 7 is a schematic diagram of an embodiment of a computer device in accordance with the application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs, the terms used in the description herein are used for the purpose of describing particular embodiments only and are not intended to limit the application, and the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the above description of the drawings are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide 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 fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the method for early warning the loss of the object seismic loss provided by the embodiment of the application is generally executed by a server, and correspondingly, the device for early warning the loss of the object seismic loss is generally arranged in the server.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of a subject seismic loss warning method according to the present application is shown. The method for early warning the earthquake loss of the target comprises the following steps:
step S201, according to the received earthquake occurrence notification signal, acquiring earthquake monitoring data of the object.
In this embodiment, the electronic device (for example, the server shown in fig. 1) on which the subject earthquake loss warning method operates may communicate with the terminal through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection 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 now known or later developed wireless connection.
The earthquake occurrence notification signal may be a notification signal after the occurrence of an earthquake. The seismic monitoring data may be monitored and seismic related data, which may include seismic monitoring sub-data of a variety of data types, which may include, for example, seismic magnitude, seismic intensity, seismic duration, and geologic information.
Specifically, when the earthquake occurrence notification signal is received, the earthquake monitoring data corresponding to the target object is acquired. The subject matter of the present application may be various types of buildings. The earthquake occurrence notification signal can be sent out by a server of the earthquake bureau, and the earthquake monitoring data can also be obtained from the server of the earthquake bureau.
It is emphasized that the seismic monitoring data may also be stored in a blockchain node in order to further ensure the privacy and security of the seismic monitoring data.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Step S202, for each item of earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss evaluation value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss evaluation curve.
In particular, the seismic monitoring data may include a plurality of items of seismic monitoring sub-data, which may include seismic magnitude, seismic intensity, duration, and geological information. The seismic loss evaluation curve is pre-established and can display the relation between the loss degree of the target object and the seismic monitoring sub-data. The loss degree is measured by a loss evaluation value, which can be a value between 0 and 1, and when the loss evaluation value is 0, the target object is completely damaged, and when the loss evaluation value is 1, the target object is completely damaged. The seismic loss evaluation curve may be a single dependent variable-single independent variable curve, i.e., in one seismic loss evaluation curve, the independent variable is a single type of seismic monitoring sub-data and the dependent variable is a loss evaluation value.
For each item of seismic monitoring sub-data in the seismic monitoring data, acquiring a seismic loss evaluation curve corresponding to the seismic monitoring sub-data, and then determining a first loss evaluation value in the seismic loss evaluation curve according to the data value of the seismic monitoring sub-data.
In step S203, target feature information of the target object is acquired to determine a second loss evaluation value according to the target feature information.
Specifically, the loss degree of the object in the earthquake is influenced by the external earthquake monitoring data and also has a relation with some attributes of the object, so that the object characteristic information of the object can be obtained. The target feature information is information recording the own feature attribute of the target object.
The seismic loss evaluation curve of the signature's characteristic information and loss evaluation value may be established in advance. And then determining a second loss evaluation value corresponding to the characteristic of the calibration according to the seismic loss evaluation curve.
In one embodiment, the subject feature in the subject feature information may include a time of presence of the subject matter. The target may be a building, which has problems with aging over time, the longer the building is, the higher the degree of aging, and the higher the likelihood of damage in an earthquake. Thus, the time of presence of the subject matter can be regarded as subject characteristic information. For a seismic loss evaluation curve, when the argument is the time of existence, the curve resembles the form of an exponential function.
Step S204, inputting the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model to obtain a seismic loss evaluation value.
Specifically, the obtained first loss evaluation value and second loss evaluation value are input into a seismic loss evaluation model, which may be a model that predicts the degree of damage of the subject matter as a whole, based on the first loss evaluation value and second loss evaluation value. The earthquake loss evaluation model can calculate according to the first loss evaluation value and the second loss evaluation value to obtain an earthquake loss evaluation value, and the earthquake loss evaluation value can integrate various external factors and internal factors to measure the damage degree of the object.
In one embodiment, the seismic loss evaluation model may be built based on a neural network.
Step S205, generating earthquake loss early warning information of the target object according to the earthquake loss evaluation value.
Specifically, the seismic loss evaluation value reflects the damage degree of the object in numerical values, and in general, the larger the seismic loss evaluation value is, the more serious the damage of the object is. The standard evaluation values may be preset, and the standard evaluation values are plural to form plural evaluation sections, and the evaluation sections divide the seismic loss degree into a plurality of states. And comparing the seismic loss evaluation value with each standard evaluation value, determining an evaluation interval in which the seismic loss evaluation value is positioned, and taking the state corresponding to the evaluation interval and the seismic loss evaluation value as seismic loss early warning information.
In one embodiment, the loss amount of the target object may also be calculated based on the value of the target object and the seismic loss evaluation value. The value of the loss amount can be obtained by multiplying the target object value and the earthquake loss evaluation value, and the loss amount can also be used as one of earthquake loss early warning information.
In the embodiment, after receiving an earthquake occurrence notification signal, acquiring earthquake monitoring data of a target object, wherein the earthquake monitoring data can comprise a plurality of items of earthquake monitoring sub-data, a first loss evaluation value is determined according to an earthquake loss evaluation curve for each item of earthquake monitoring sub-data, the earthquake loss evaluation curve is pre-established, different earthquake loss evaluation curves reflect losses of different independent variables on the target object, target characteristic information is acquired, a second loss evaluation value brought by self characteristic attributes of the target object is determined according to the target characteristic information, and then a total earthquake loss evaluation value is calculated according to an earthquake loss evaluation model, self and external influence factors of the target object are comprehensively considered, accuracy of earthquake loss evaluation is improved, earthquake loss early warning information is generated, automatic early warning of the earthquake loss of the target object is realized, and earthquake loss early warning efficiency is improved.
Further, the step S201 may include receiving an earthquake occurrence notification signal, acquiring earthquake monitoring total data according to the earthquake occurrence notification signal, searching for a target object based on geographical area information in the earthquake monitoring total data, and acquiring the earthquake monitoring data of the target object from the earthquake monitoring total data.
Specifically, after receiving the earthquake occurrence notification signal, the server may acquire the earthquake monitoring total amount data. The seismic survey total data may be recorded seismic related total data. The seismic monitoring volume data may also include geographic area information, each set of geographic area information corresponding to a geographic area that is affected by the seismic event.
The aggregate seismic monitoring data may be a collection of seismic monitoring data for different geographic areas, the specific seismic monitoring data being different for different geographic areas. For example, seismic intensity may be different in two different geographic areas. And searching the target object in the geographic area corresponding to the geographic area information by the server, and then acquiring the seismic monitoring data of the target object from the seismic monitoring total data. If no target object is found in a geographic area, the seismic monitoring data corresponding to the geographic area may not be processed.
In one embodiment, the geographical area where the seismic impact reaches the preset condition may be screened based on the seismic monitoring throughput data, and then the target object may be searched for in the geographical area. For example, a geographical area with a seismic magnitude of 0.8, a seismic intensity of 0.6, and a duration of 20s is affected by a seismic event that is too small to have any effect, and calculation of targets within the geographical area may not be necessary.
The target object in the application can be a building, the geographic position information of the building is obtained in advance, then the geographic position information of the building is stored in a database, and then the target object is selected according to the geographic position information and the geographic area information.
In this embodiment, the seismic monitoring full data is acquired first, and then whether the target object exists is searched according to the geographical area information in the seismic monitoring full data, so that it is determined which target objects need to be processed, and which seismic monitoring data need to be calculated.
Further, the step of obtaining the seismic monitoring total data according to the seismic occurrence notification signal may include forwarding the seismic monitoring initial data to a message queue through a preset interface according to the seismic occurrence notification signal, and preprocessing the seismic monitoring initial data in the message queue through flink to obtain the seismic monitoring total data.
Specifically, after receiving the earthquake occurrence notification signal, the earthquake monitoring initial data can be obtained through a preset interface. The seismic monitoring initial data may be raw data collected by a seismic office, and a server of the seismic office may provide an interface through which the server interacts with the server of the seismic office.
The server may forward the seismic monitoring initial data collected by the seismic office to the message queue, for example, may forward the seismic monitoring initial data to ActiveMQ, activeMQ, which is a message middleware written in Java and runs in a Java virtual machine. And then, carrying out preprocessing on the seismic monitoring initial data in the message queue by flink clusters to obtain the seismic monitoring total data. The preprocessing can be to convert the initial data of the seismic monitoring into standard data in a standard data format, and check whether the standard data is complete and correct to obtain the full data of the seismic monitoring. In one embodiment, the seismic survey total data may also be persisted to a database.
In the embodiment, the earthquake monitoring initial data is processed through the big data technologies such as message queues, flink and the like, so that the timeliness of data processing is improved.
Further, the step S202 may include obtaining a target type of the target object, selecting a seismic loss evaluation curve from the pre-established seismic loss evaluation curves according to the data type and the target type of each item of the seismic monitoring sub-data in the seismic monitoring data, and determining a first loss evaluation value corresponding to the seismic monitoring sub-data according to the seismic loss evaluation curve.
Specifically, the targets may have multiple target types, and the damage degree of different target types may also have a large difference under the same seismic monitoring data. For example, the earthquake resistance of ordinary houses and large stadiums varies greatly. Therefore, for each item of the seismic monitoring sub-data in the seismic monitoring data, a corresponding seismic loss evaluation curve can be obtained according to the target type of the target object and the data type of the seismic monitoring sub-data. For example, according to the types of targets "town self-building" and "earthquake magnitude", a corresponding earthquake loss evaluation curve is obtained.
After the seismic loss evaluation curve is obtained, inquiring is carried out in the seismic loss evaluation curve according to the seismic monitoring sub-data, and then the loss evaluation value under the influence of the seismic monitoring sub-data, namely, the first loss evaluation value, can be obtained.
The seismic loss evaluation curve is obtained according to historical disaster-stricken data of targets of the same type. For example, when the target object is a building, according to disaster data of various types of buildings damaged by an earthquake, the loss degree of various types of buildings under different earthquake magnitudes, the loss degree under different earthquake intensity, the loss degree under different duration influences and the loss degree under different geology are obtained, so that independent variables are respectively the earthquake magnitude, the earthquake intensity, the duration and the earthquake loss evaluation curve of geology. Generally, the seismic loss evaluation curves with independent variables of seismic magnitude, seismic intensity and duration are generally "S" shaped curves, and specifically, referring to fig. 3,4 and 5, the seismic loss evaluation curves under the influence of seismic magnitude, the seismic loss evaluation curves under the influence of seismic intensity and the seismic loss evaluation curves under the influence of duration are respectively shown.
In this embodiment, the corresponding seismic loss evaluation curve is obtained according to the target type and the data type of the seismic monitoring sub-data, so that the accuracy of the seismic loss evaluation curve selection is ensured, and the accuracy of the first loss evaluation value is ensured.
Further, the step S204 may include inputting the first loss evaluation value and the second loss evaluation value into a trained seismic loss evaluation model, wherein the seismic loss evaluation model is a multiple linear regression model, obtaining an output result of the multiple linear regression model, and determining the output result as the seismic loss evaluation value.
Specifically, the first loss evaluation value and the second loss evaluation value are input into a seismic loss evaluation model, which may be a pretrained multiple linear regression model. The seismic loss evaluation model may add respective weights to the first loss evaluation value and the second loss evaluation value, and then perform a linear operation on the first loss evaluation value and the second loss evaluation value. The output result of the multiple linear regression model is used as the seismic loss evaluation value:
L=a1s1+a2s2+a3s3+Λ+ansn (1)
Where L is the seismic loss evaluation value, S i is the first loss evaluation value or the second loss evaluation value, and a i is the weight of S i.
In this embodiment, the seismic loss evaluation model may be a multiple linear regression model, and the multiple linear regression model may integrate the first loss evaluation value and the second loss evaluation value, so as to obtain the seismic loss evaluation value for measuring the damage degree overall.
Further, before the step S204, the method may further include obtaining historical seismic loss data, extracting a first loss evaluation value corresponding to the seismic monitoring sub-data, a second loss evaluation value corresponding to the target feature information and a seismic loss evaluation value from the historical seismic data, training an initial seismic loss evaluation model according to the first loss evaluation value, the second loss evaluation value and the seismic loss evaluation value to obtain a seismic loss evaluation model, wherein the initial seismic loss evaluation model is a multiple linear regression model.
Specifically, the seismic loss evaluation model needs to be obtained through model training. During training, historical seismic loss data are acquired, and the first loss evaluation value corresponding to various types of seismic monitoring sub-data, the second loss evaluation value corresponding to the target characteristic information and the seismic loss evaluation value are recorded in the historical seismic loss data. Wherein the seismic loss evaluation data may be determined manually.
And then taking a first loss evaluation value corresponding to various types of seismic monitoring sub-data and a second loss evaluation value corresponding to target characteristic information as input of an initial seismic loss evaluation model, taking the seismic loss evaluation value as a label, and training the initial seismic loss evaluation model so as to obtain the seismic loss evaluation model. Wherein the initial seismic loss evaluation model may be a multiple linear regression model. The multiple linear regression model is trained to determine the weight of the loss evaluation value caused by various seismic monitoring sub-data and target characteristic information.
In this embodiment, an initial seismic loss evaluation model is trained according to historical seismic loss data, so that the weight of loss evaluation values caused by various types of seismic monitoring sub-data and target feature information is determined, and the accuracy of calculation of the seismic loss evaluation values is ensured.
Further, after the step S205, the method may further include querying an account associated with the target object, where the account includes a customer account and a management account, and sending the earthquake loss early warning information to a terminal logged in by the account.
Specifically, the server may query the account numbers associated with the subject matter, including the customer account number and the management account number. The customer account number may be an account number of a target applicant, and the management account number may be an account number of a worker such as a security company, a wind control, etc. And sending the earthquake loss early-warning information to a terminal registered by the account so as to inform customers and management personnel of the loss early-warning information of the target object in time, so that countermeasures are taken.
In this embodiment, the earthquake loss early warning information is sent to the terminal registered by the customer account and the management account, so as to inform the damage condition of the target object in time, and realize loss early warning.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored in a computer-readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
With further reference to fig. 6, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a target seismic loss early warning device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be specifically applied to various electronic devices.
As shown in fig. 6, the object earthquake loss early warning device 300 according to the present embodiment includes a monitoring acquisition module 301, a first determination module 302, a second determination module 303, a loss evaluation module 304, and an information generation module 305, wherein:
The monitoring acquisition module 301 is configured to acquire seismic monitoring data of the target object according to the received seismic occurrence notification signal.
The first determining module 302 is configured to determine, for each item of seismic monitoring sub-data in the seismic monitoring data, a first loss evaluation value corresponding to the seismic monitoring sub-data according to a pre-established seismic loss evaluation curve.
The second determining module 303 is configured to obtain target feature information of the target object, so as to determine a second loss evaluation value according to the target feature information.
The loss evaluation module 304 is configured to input the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model, and obtain a seismic loss evaluation value.
The information generating module 305 is configured to generate earthquake loss early warning information of the target object according to the earthquake loss evaluation value.
In the embodiment, after receiving an earthquake occurrence notification signal, acquiring earthquake monitoring data of a target object, wherein the earthquake monitoring data can comprise a plurality of items of earthquake monitoring sub-data, a first loss evaluation value is determined according to an earthquake loss evaluation curve for each item of earthquake monitoring sub-data, the earthquake loss evaluation curve is pre-established, different earthquake loss evaluation curves reflect losses of different independent variables on the target object, target characteristic information is acquired, a second loss evaluation value brought by self characteristic attributes of the target object is determined according to the target characteristic information, and then a total earthquake loss evaluation value is calculated according to an earthquake loss evaluation model, self and external influence factors of the target object are comprehensively considered, accuracy of earthquake loss evaluation is improved, earthquake loss early warning information is generated, automatic early warning of the earthquake loss of the target object is realized, and earthquake loss early warning efficiency is improved.
In some optional implementations of the present embodiment, the monitoring acquisition module 301 may include a signal receiving sub-module, a full-quantity acquisition sub-module, a target searching sub-module, and a monitoring acquisition sub-module, where:
And the signal receiving sub-module is used for receiving the earthquake occurrence notification signal.
And the total acquisition sub-module is used for acquiring the earthquake monitoring total data according to the earthquake occurrence notification signal.
And the target object searching sub-module is used for searching the target object based on the geographical area information in the seismic monitoring total data.
And the monitoring acquisition sub-module is used for acquiring the seismic monitoring data of the object from the seismic monitoring total data.
In this embodiment, the seismic monitoring full data is acquired first, and then whether the target object exists is searched according to the geographical area information in the seismic monitoring full data, so that it is determined which target objects need to be processed, and which seismic monitoring data need to be calculated.
In some optional implementations of the present embodiment, the full-quantity acquisition sub-module may include an initial forwarding unit and a preprocessing unit, where:
And the initial forwarding unit is used for forwarding the earthquake monitoring initial data to the message queue through a preset interface according to the earthquake occurrence notification signal.
The preprocessing unit is used for preprocessing the seismic monitoring initial data in the message queue through flink to obtain the seismic monitoring full data.
In the embodiment, the earthquake monitoring initial data is processed through the big data technologies such as message queues, flink and the like, so that the timeliness of data processing is improved.
In some alternative implementations of the present embodiment, the first determining module 302 may include a type obtaining sub-module, a curve selecting sub-module, and a first determining sub-module, where:
And the type acquisition sub-module is used for acquiring the target type of the target object.
The curve selection sub-module is used for selecting the earthquake loss evaluation curve from the pre-established earthquake loss evaluation curves according to the data type and the target type of the earthquake monitoring sub-data for each item of the earthquake monitoring sub-data.
And the first determining sub-module is used for determining a first loss evaluation value corresponding to the earthquake monitoring sub-data according to the earthquake loss evaluation curve.
In this embodiment, the corresponding seismic loss evaluation curve is obtained according to the target type and the data type of the seismic monitoring sub-data, so that the accuracy of the seismic loss evaluation curve selection is ensured, and the accuracy of the first loss evaluation value is ensured.
In some alternative implementations of the present embodiment, the loss evaluation module 304 may include an evaluation value input sub-module and a result acquisition sub-module, where:
and the evaluation value input sub-module is used for inputting the first loss evaluation value and the second loss evaluation value into a trained seismic loss evaluation model, wherein the seismic loss evaluation model is a multiple linear regression model.
And the result acquisition sub-module is used for acquiring the output result of the multiple linear regression model and determining the output result as an earthquake loss evaluation value.
In this embodiment, the seismic loss evaluation model may be a multiple linear regression model, and the multiple linear regression model may integrate the first loss evaluation value and the second loss evaluation value, so as to obtain the seismic loss evaluation value for measuring the damage degree overall.
In some optional implementations of the present embodiment, the objective earthquake loss warning device 300 may further include a history acquisition module, an evaluation value extraction module, and a model training module, where:
and the history acquisition module is used for acquiring the history seismic loss data.
The evaluation value extraction module is used for extracting a first loss evaluation value corresponding to the earthquake monitoring sub-data, a second loss evaluation value corresponding to the target characteristic information and an earthquake loss evaluation value from the historical earthquake data.
The model training module is used for training an initial seismic loss evaluation model according to the first loss evaluation value, the second loss evaluation value and the seismic loss evaluation value to obtain a seismic loss evaluation model, wherein the initial seismic loss evaluation model is a multiple linear regression model.
In this embodiment, an initial seismic loss evaluation model is trained according to historical seismic loss data, so that the weight of loss evaluation values caused by various types of seismic monitoring sub-data and target feature information is determined, and the accuracy of calculation of the seismic loss evaluation values is ensured.
In some optional implementations of the present embodiment, the objective earthquake loss early-warning device 300 may further include an account query module and an information sending module, where:
And the account query module is used for querying accounts associated with the target objects, wherein the accounts comprise customer accounts and management accounts.
And the information sending module is used for sending the earthquake loss early warning information to the terminal logged in by the account.
In this embodiment, the earthquake loss early warning information is sent to the terminal registered by the customer account and the management account, so as to inform the damage condition of the target object in time, and realize loss early warning.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 7, fig. 7 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), a Programmable gate array (Field-Programmable GATE ARRAY, FPGA), a digital Processor (DIGITAL SIGNAL Processor, DSP), an embedded device, and the like.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., 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, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may 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 (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is generally used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of a method for warning of earthquake loss of a target object. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the method for warning of loss of the target object.
The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.
The computer device provided in this embodiment may execute the foregoing objective seismic loss warning method. The method for pre-warning the loss of the object in the object earthquake may be the method for pre-warning the loss of the object in the object earthquake in each of the above embodiments.
In the embodiment, after receiving an earthquake occurrence notification signal, acquiring earthquake monitoring data of a target object, wherein the earthquake monitoring data can comprise a plurality of items of earthquake monitoring sub-data, a first loss evaluation value is determined according to an earthquake loss evaluation curve for each item of earthquake monitoring sub-data, the earthquake loss evaluation curve is pre-established, different earthquake loss evaluation curves reflect losses of different independent variables on the target object, target characteristic information is acquired, a second loss evaluation value brought by self characteristic attributes of the target object is determined according to the target characteristic information, and then a total earthquake loss evaluation value is calculated according to an earthquake loss evaluation model, self and external influence factors of the target object are comprehensively considered, accuracy of earthquake loss evaluation is improved, earthquake loss early warning information is generated, automatic early warning of the earthquake loss of the target object is realized, and earthquake loss early warning efficiency is improved.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the subject matter earthquake loss warning method as described above.
In the embodiment, after receiving an earthquake occurrence notification signal, acquiring earthquake monitoring data of a target object, wherein the earthquake monitoring data can comprise a plurality of items of earthquake monitoring sub-data, a first loss evaluation value is determined according to an earthquake loss evaluation curve for each item of earthquake monitoring sub-data, the earthquake loss evaluation curve is pre-established, different earthquake loss evaluation curves reflect losses of different independent variables on the target object, target characteristic information is acquired, a second loss evaluation value brought by self characteristic attributes of the target object is determined according to the target characteristic information, and then a total earthquake loss evaluation value is calculated according to an earthquake loss evaluation model, self and external influence factors of the target object are comprehensively considered, accuracy of earthquake loss evaluation is improved, earthquake loss early warning information is generated, automatic early warning of the earthquake loss of the target object is realized, and earthquake loss early warning efficiency is improved.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.
Claims (9)
1. The earthquake loss early warning method for the target object is characterized by comprising the following steps of:
acquiring seismic monitoring data of a target object according to the received seismic occurrence notification signal;
For each item of earthquake monitoring sub-data in the earthquake monitoring data, determining a first loss evaluation value corresponding to the earthquake monitoring sub-data according to a pre-established earthquake loss evaluation curve;
Acquiring target characteristic information of the target object to determine a second loss evaluation value according to the target characteristic information;
Inputting the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model to obtain a seismic loss evaluation value;
generating earthquake loss early warning information of the target object according to the earthquake loss evaluation value;
The step of determining a first loss evaluation value corresponding to the seismic monitoring sub data according to a pre-established seismic loss evaluation curve for each item of the seismic monitoring sub data comprises the following steps:
Obtaining a target type of the target object, wherein the target object has a plurality of target types;
For each item of earthquake monitoring sub-data in the earthquake monitoring data, selecting an earthquake loss evaluation curve from the pre-established earthquake loss evaluation curves according to the data type of the earthquake monitoring sub-data and the target type, wherein the earthquake loss evaluation curve is obtained according to the historical disaster-stricken data of targets of the same type of the target;
determining a first loss evaluation value corresponding to the seismic monitoring sub-data according to the seismic loss evaluation curve;
wherein the step of acquiring the target feature information of the target object to determine the second loss evaluation value according to the target feature information includes:
acquiring target characteristic information of the target object, wherein the target characteristic information is information for recording the characteristic attribute of the target object;
and pre-establishing an earthquake loss evaluation curve of the target characteristic information and the loss evaluation value, and determining a second loss evaluation value corresponding to the target characteristic information according to the earthquake loss evaluation curve.
2. The method of claim 1, wherein the step of acquiring the seismic monitoring data of the subject based on the received seismic occurrence notification signal comprises:
receiving an earthquake occurrence notification signal;
acquiring seismic monitoring total data according to the seismic occurrence notification signal;
searching for a target object based on geographic area information in the seismic monitoring total data;
and acquiring the seismic monitoring data of the target object from the seismic monitoring total data.
3. The method for pre-warning of loss of seismic object according to claim 2, wherein the step of acquiring seismic monitoring full-scale data according to the seismic occurrence notification signal comprises:
according to the earthquake occurrence notification signal, the earthquake monitoring initial data is forwarded to a message queue through a preset interface;
And preprocessing the seismic monitoring initial data in the message queue through flink to obtain the seismic monitoring total data.
4. The method of claim 1, wherein the step of inputting the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model to obtain the seismic loss evaluation value comprises:
Inputting the first loss evaluation value and the second loss evaluation value into a trained seismic loss evaluation model, wherein the seismic loss evaluation model is a multiple linear regression model;
And obtaining an output result of the multiple linear regression model, and determining the output result as an earthquake loss evaluation value.
5. The method of claim 1, further comprising, prior to the step of inputting the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model to obtain a seismic loss evaluation value:
acquiring historical seismic loss data;
extracting a first loss evaluation value corresponding to the seismic monitoring sub-data, a second loss evaluation value corresponding to the target characteristic information and a seismic loss evaluation value from the historical seismic loss data;
And training an initial seismic loss evaluation model according to the first loss evaluation value, the second loss evaluation value and the seismic loss evaluation value to obtain a seismic loss evaluation model, wherein the initial seismic loss evaluation model is a multiple linear regression model.
6. The method of claim 1, further comprising, after the step of generating the seismic-loss warning information of the target object from the seismic-loss evaluation value:
Querying an account number associated with the subject matter, wherein the account number comprises a client account number and a management account number;
and sending the earthquake loss early warning information to the terminal logged in by the account.
7. The utility model provides a target thing earthquake loss early warning device which characterized in that includes:
The monitoring acquisition module is used for acquiring the earthquake monitoring data of the object according to the received earthquake occurrence notification signal;
The first determining module is used for determining a first loss evaluation value corresponding to each item of earthquake monitoring sub-data in the earthquake monitoring data according to a pre-established earthquake loss evaluation curve;
the first determining module comprises a type acquisition sub-module, a curve selection sub-module and a first determining sub-module;
the type acquisition sub-module is used for acquiring the target types of targets, wherein the targets have a plurality of target types;
The curve selection sub-module is used for selecting an earthquake loss evaluation curve from the pre-established earthquake loss evaluation curves according to the data type and the target type of the earthquake monitoring sub-data for each item of the earthquake monitoring sub-data, wherein the earthquake loss evaluation curve is obtained according to the historical disaster-stricken data of the target objects of the same type;
the first determining submodule is used for determining a first loss evaluation value corresponding to the earthquake monitoring sub-data according to the earthquake loss evaluation curve;
a second determining module, configured to obtain target feature information of the target object, so as to determine a second loss evaluation value according to the target feature information;
The method comprises the steps of recording characteristic information of a target object, establishing a seismic loss evaluation curve of the characteristic information and a loss evaluation value in advance, and determining a second loss evaluation value corresponding to the characteristic information of the target according to the seismic loss evaluation curve;
The loss evaluation module is used for inputting the first loss evaluation value and the second loss evaluation value into a seismic loss evaluation model to obtain a seismic loss evaluation value;
And the information generation module is used for generating earthquake loss early warning information of the target object according to the earthquake loss evaluation value.
8. A computer device comprising a memory having stored therein computer readable instructions which when executed by the processor implement the steps of the subject matter earthquake loss warning method of any of claims 1 to 6.
9. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the subject matter earthquake loss warning method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210086260.XA CN114442151B (en) | 2022-01-25 | 2022-01-25 | Method, device, computer equipment and storage medium for early warning of earthquake loss of target object |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210086260.XA CN114442151B (en) | 2022-01-25 | 2022-01-25 | Method, device, computer equipment and storage medium for early warning of earthquake loss of target object |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114442151A CN114442151A (en) | 2022-05-06 |
| CN114442151B true CN114442151B (en) | 2025-02-28 |
Family
ID=81370635
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210086260.XA Active CN114442151B (en) | 2022-01-25 | 2022-01-25 | Method, device, computer equipment and storage medium for early warning of earthquake loss of target object |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114442151B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434951A (en) * | 2020-11-25 | 2021-03-02 | 中国海洋大学 | Earthquake disaster grade assessment method and system |
| CN113609202A (en) * | 2021-08-11 | 2021-11-05 | 湖南快乐阳光互动娱乐传媒有限公司 | Data processing method and device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3909840B2 (en) * | 2002-10-30 | 2007-04-25 | 株式会社フジタ | Building loss assessment program |
| KR20160116400A (en) * | 2015-03-30 | 2016-10-10 | 케이아이티밸리(주) | The analysis method for loss estimation of buildings collapse by earthquake |
| CN109543616B (en) * | 2018-11-23 | 2022-05-03 | 北京师范大学 | Method, device, equipment and medium for evaluating damage of target object after earthquake |
-
2022
- 2022-01-25 CN CN202210086260.XA patent/CN114442151B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434951A (en) * | 2020-11-25 | 2021-03-02 | 中国海洋大学 | Earthquake disaster grade assessment method and system |
| CN113609202A (en) * | 2021-08-11 | 2021-11-05 | 湖南快乐阳光互动娱乐传媒有限公司 | Data processing method and device |
Non-Patent Citations (1)
| Title |
|---|
| 地震灾害经济损失的宏观估算方法;李平等;《中国地质灾害与防治学报》;19941231;第5卷(第4期);第86页倒数第1段-第90页倒数第1段 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114442151A (en) | 2022-05-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112863683B (en) | Medical record quality control method and device based on artificial intelligence, computer equipment and storage medium | |
| CN117093477A (en) | Software quality assessment method and device, computer equipment and storage medium | |
| CN117234844A (en) | Cloud server abnormality management method and device, computer equipment and storage medium | |
| CN115237724A (en) | Data monitoring method, device, equipment and storage medium based on artificial intelligence | |
| CN112417886B (en) | Method, device, computer equipment and storage medium for extracting intention entity information | |
| CN117522538A (en) | Bid information processing method, device, computer equipment and storage medium | |
| CN115757075A (en) | Task abnormity detection method and device, computer equipment and storage medium | |
| CN119806937A (en) | Anomaly detection method and related equipment for application service reliability | |
| CN114442151B (en) | Method, device, computer equipment and storage medium for early warning of earthquake loss of target object | |
| CN110348669B (en) | Intelligent rule generation method, intelligent rule generation device, computer equipment and storage medium | |
| CN116611941A (en) | Fraud identification method, device, equipment and storage medium based on artificial intelligence | |
| CN115941712B (en) | Method and device for processing report data, computer equipment and storage medium | |
| CN116843395A (en) | Alarm classification method, device, equipment and storage medium of service system | |
| CN116843483A (en) | Vehicle insurance claim settlement method, device, computer equipment and storage medium | |
| CN117217684A (en) | Index data processing method and device, computer equipment and storage medium | |
| CN116777639A (en) | Case risk rating method, device, computer equipment and storage medium | |
| CN115099710B (en) | Method and system for rapidly evaluating business association influence | |
| CN119690772A (en) | Index monitoring method and device, computer equipment and storage medium | |
| CN119179957A (en) | Abnormal user detection method and device, computer equipment and storage medium | |
| CN118041977A (en) | Method and device for processing micro-service component, computer equipment and storage medium | |
| CN115237736A (en) | Data mart anomaly detection method and device, computer equipment and storage medium | |
| CN116611432A (en) | Drunk driving risk identification method and device, computer equipment and storage medium | |
| CN117132409A (en) | Nuclear protection data processing method, device, equipment and medium based on artificial intelligence | |
| CN119477557A (en) | A risk identification method, device, equipment and storage medium thereof | |
| CN120216315A (en) | Data analysis method, device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |