CN110544013A

CN110544013A - disaster risk early warning method and device, computer equipment and storage medium

Info

Publication number: CN110544013A
Application number: CN201910702054.5A
Authority: CN
Inventors: 赵素群
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2019-07-31
Filing date: 2019-07-31
Publication date: 2019-12-06
Anticipated expiration: 2039-07-31
Also published as: CN110544013B; WO2021017607A1

Abstract

The invention discloses a disaster risk early warning method, a disaster risk early warning device, computer equipment and a storage medium. The method comprises the following steps: crawling information matched with preset risk keywords in a webpage corresponding to a preset network address to obtain event information containing a plurality of risk events, merging and recombining the risk events according to time factors to obtain risk event recombination information, obtaining target risk events matched with a risk early warning request in the risk event recombination information, calculating the target risk events according to a preset risk coefficient calculation model to obtain risk coefficients, and obtaining early warning prompt information corresponding to the risk coefficients and the risk early warning request. The invention is based on big data processing technology, can early warn the risk of the risk event at any time, integrates the factors such as geographical position information, time information and the like in the early warning process, and improves the accuracy of early warning the risk of the risk event.

Description

Disaster risk early warning method and device, computer equipment and storage medium

Technical Field

The invention relates to the technical field of computers, in particular to a disaster risk early warning method and device, computer equipment and a storage medium.

Background

In order to avoid damage to personnel or materials due to potential risks such as extreme weather, the risk of a future risk event can be pre-warned through the existing information. Because the occurrence probability of the risk event is related to the time interval between the occurrence of the last risk event, the risk of the risk event is early warned by the calculation model in the prior art, however, the reference factor of time is not included in the calculation model, so that the accuracy is not sufficient when the risk of the risk event is early warned, and the risk of the risk event occurring at a certain time in the future can only be early warned. Therefore, the existing early warning method has the problem that the risk of the risk event cannot be accurately early warned.

Disclosure of Invention

the embodiment of the invention provides a disaster risk early warning method, a disaster risk early warning device, computer equipment and a storage medium, and aims to solve the problem that a disaster risk early warning method in the prior art has large deviation when risk early warning is carried out.

In a first aspect, an embodiment of the present invention provides a disaster risk early warning method, including:

Crawling information matched with preset risk keywords in a webpage corresponding to a preset network address to obtain event information containing a plurality of risk events;

merging and recombining the risk events contained in the event information according to preset event factors to obtain risk event recombination information, wherein the event factors comprise names, time, places, risk types and degrees;

If a risk early warning request input by a user is received, acquiring a target risk event matched with the risk early warning request in the risk event recombination information, wherein the risk early warning request comprises geographical position information, risk type information and time information;

Judging whether the risk type information of the risk early warning request contains a plurality of risk types to obtain a judgment result;

calculating the target risk event according to a preset risk coefficient calculation model and the judgment result to obtain a risk coefficient corresponding to the risk early warning request;

And generating early warning prompt information corresponding to the risk early warning request according to a preset early warning prompt model and the risk coefficient.

In a second aspect, an embodiment of the present invention provides a disaster risk early warning apparatus, including:

The system comprises an event information acquisition unit, a risk analysis unit and a risk analysis unit, wherein the event information acquisition unit is used for crawling information matched with preset risk keywords in a webpage corresponding to a preset network address to obtain event information containing a plurality of risk events;

The event information merging and recombining unit is used for merging and recombining the risk events contained in the event information according to preset event factors to obtain risk event recombining information, wherein the event factors comprise names, time, places, risk types and degrees;

The target risk event acquiring unit is used for acquiring a target risk event matched with the risk early warning request in the risk event recombination information if the risk early warning request input by a user is received, wherein the risk early warning request comprises geographical position information, risk type information and time information;

A risk type information judging unit, configured to judge whether the risk type information of the risk early warning request includes multiple risk types;

A risk coefficient calculation unit, configured to calculate the target risk event according to a preset risk coefficient calculation model and the determination result to obtain a risk coefficient corresponding to the risk early warning request;

and the early warning prompt information generating unit is used for generating early warning prompt information corresponding to the risk early warning request according to a preset early warning prompt model and the risk coefficient.

in a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the disaster risk early warning method according to the first aspect when executing the computer program.

In a fourth aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the disaster risk early warning method according to the first aspect.

The embodiment of the invention provides a disaster risk early warning method and device, computer equipment and a storage medium. Executing an information crawling program to obtain risk events, merging and recombining the risk events according to time factors to obtain risk event recombination information, obtaining target risk events matched with the risk early warning request in the risk event recombination information, calculating the target risk events according to a preset risk coefficient calculation model to obtain risk coefficients, and obtaining early warning prompt information corresponding to the risk coefficients and the risk early warning request. By the method, the risk of the risk event occurring at any time can be pre-warned, factors such as geographical position information and time information are integrated in the pre-warning process, and the accuracy of pre-warning the risk of the risk event occurring is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flow chart of a disaster risk early warning method according to an embodiment of the present invention;

Fig. 2 is a schematic sub-flow diagram of a disaster risk early warning method according to an embodiment of the present invention;

Fig. 3 is a schematic sub-flow diagram of a disaster risk early warning method according to an embodiment of the present invention;

Fig. 4 is a schematic view of another sub-flow of the disaster risk early warning method according to the embodiment of the present invention;

Fig. 5 is a schematic view of another sub-flow of the disaster risk early warning method according to the embodiment of the present invention;

Fig. 6 is a schematic block diagram of a disaster risk early warning device provided in an embodiment of the present invention;

Fig. 7 is a schematic block diagram of sub-units of a disaster risk early warning device provided in an embodiment of the present invention;

Fig. 8 is a schematic block diagram of another sub-unit of the disaster risk early warning device provided in the embodiment of the present invention;

Fig. 9 is a schematic block diagram of another sub-unit of the disaster risk early warning device provided in the embodiment of the present invention;

fig. 10 is a schematic block diagram of another sub-unit of the disaster risk early warning device provided in the embodiment of the present invention;

FIG. 11 is a schematic block diagram of a computer device provided by an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

referring to fig. 1, fig. 1 is a schematic flow chart of a disaster risk early warning method according to an embodiment of the present invention. The disaster risk early warning method is applied to a user terminal, and the method is executed through application software installed in the user terminal, wherein the user terminal is terminal equipment, such as a desktop computer, a notebook computer, a tablet computer or a mobile phone, for executing the disaster risk early warning method to early warn risks of disaster occurrence.

As shown in fig. 1, the method includes steps S110 to S160.

S110, crawling information matched with preset risk keywords in a webpage corresponding to a preset network address to obtain event information containing a plurality of risk events.

Crawling information matched with preset risk keywords in a webpage corresponding to a preset network address to obtain event information containing a plurality of risk events, wherein the event information comprises event basic information and event description information, the basic information is information such as types and names of the risk events, and the event description information is detailed information for specifically describing the events. And executing an information crawling program with configured parameter values, wherein the parameter values comprise preset network addresses and preset risk keywords, and crawling information matched with the risk keywords in a webpage corresponding to the network addresses to obtain event information containing a plurality of risk events. Specifically, in order to calculate and analyze the possibility of a risk event occurring in a certain area, the historical risk event occurring in the area needs to be crawled from the internet, and parameter values in the information crawling program can be configured in advance by a user. Specifically, the preset network address may be a network address of a weather station website, a seismic station website and a news media website; the preset risk keyword may be a keyword related to typhoon, earthquake, flood, mountain fire, etc. The acquired event information includes a plurality of risk events, that is, specific information of natural disasters such as typhoons, earthquakes, floods, mountain fires and the like occurring at a certain location.

For example, a certain risk event is: superstrong typhoon "risk event a", transiting Shenzhen in 9 and 16 months in 2018, with a maximum wind power level of 14 near the center. The "risk event a" of the super-strong typhoon is basic information of the risk event, and the rest is description information of the risk event.

And S120, merging and recombining the risk events contained in the event information according to preset event factors to obtain risk event recombination information.

And merging and recombining the risk events contained in the event information according to a preset event factor to obtain risk event recombination information. Since different websites may all record the same risk, and the same risk event may also be recorded in a certain website for multiple times, it is necessary to merge and recombine multiple duplicate records corresponding to a certain risk event in the event information by using an event factor, that is, only the first record in the multiple duplicate records corresponding to a risk event is reserved to obtain risk event recombination information. Specifically, corresponding event key information is acquired from each risk event according to event factors, and the risk events are merged and recombined based on the event key information, wherein the event factors include names, time, places, risk types and degrees.

In an embodiment, as shown in fig. 2, step S120 includes sub-steps S121 and S122.

And S121, extracting event key information corresponding to each risk event from the event information according to the event factor.

and extracting event key information corresponding to each risk event from the event information according to preset event factors. The event factors comprise names, time, places, risk types and degrees, the event key information corresponding to a certain risk event can be obtained by respectively obtaining name information, time information, place information, type information and degree information correspondingly contained in the risk event according to the event factors, and the event key information corresponding to each risk event can be obtained by the method.

for example, the event key information of the risk event obtained from the above example according to the event factor is shown in table 1.

Name (R)	Time of day	Location of a site	Type of risk	Degree of
					Risk event A	9 and 16 months in 2018	Shenzhen (Shenzhen medicine)	Typhoon	Stage 14

TABLE 1

And S122, merging and recombining the risk events according to the event key information to obtain risk event recombination information.

And merging and recombining the risk events according to the event key information to obtain risk event recombination information. Specifically, whether multiple duplicate records exist in the same risk event is judged according to event key information, if the name information, the time information, the location information and the type information of two risk events are the same, it is indicated that the two risk events are two duplicate records corresponding to a certain risk event, otherwise, the two risk events are records corresponding to two independent risk events respectively. If a certain risk event does not have a plurality of repeated records, the risk event does not need to be merged and recombined; if a certain risk event has a plurality of repeated records, merging and recombining the risk event, that is, only keeping the first record in the plurality of repeated records corresponding to the risk event, and merging and recombining all risk events according to the method to obtain risk event recombination information.

and S130, if a risk early warning request input by a user is received, acquiring a target risk event matched with the risk early warning request in the risk event recombination information.

And if a risk early warning request input by a user is received, acquiring a target risk event matched with the risk early warning request in the risk event recombination information, wherein the risk early warning request comprises geographical position information, risk type information and time information. The risk early warning request is request information which is input by a user and sends corresponding early warning prompt information for the threat of a risk event occurring at a certain time, and the risk early warning request comprises geographical position information, namely a specific place needing risk early warning; the risk early warning request also comprises risk type information, wherein the risk type information only comprises one risk type, namely corresponding early warning prompt information can be sent out for the threat of a risk event corresponding to one risk type, and the risk type information also comprises a plurality of different risk types, namely corresponding early warning prompt information can be sent out for the overall threat of the risk event corresponding to the plurality of risk types; the risk early warning request also comprises time information, and the time information can be the current time or a certain time point in the future.

s140, judging whether the risk type information of the risk early warning request contains a plurality of risk types to obtain a judgment result.

Whether the risk type information of the risk early warning request contains a plurality of risk types is judged to obtain a judgment result, because in a specific application process, the risk type information can only contain one risk type and also can contain a plurality of different risk types, and for one or more risk types contained in the risk early warning request, different methods are needed to obtain corresponding risk coefficients, so that before the risk coefficients of the risk early warning request are obtained, whether the risk type information of the risk early warning request contains a plurality of risk types is judged.

s150, calculating the target risk event according to a preset risk coefficient calculation model and the judgment result to obtain a risk coefficient corresponding to the risk early warning request.

and calculating the target risk event according to a preset risk coefficient calculation model and the judgment result to obtain a risk coefficient corresponding to the risk early warning request.

In one embodiment, as shown in FIG. 3, step S150 includes sub-steps S151 and S152.

and S151, if the judgment result is that the risk type information does not contain a plurality of risk types, calculating the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to the risk type in the risk early warning request.

And if the judgment result is that the risk type information does not contain a plurality of risk types, calculating the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to the risk type in the risk early warning request, wherein the risk coefficient calculation model comprises a risk distance scoring rule, a population density scoring rule, a risk event grading rule and a risk coefficient calculation formula. The risk coefficient calculation model is a model used for calculating a risk coefficient corresponding to the risk early warning request, and specifically includes a risk distance scoring rule, a population density scoring rule, a risk event grading rule and a risk coefficient calculation formula, and the risk distance scoring rule can be used for obtaining a risk distance score corresponding to the risk early warning request; the population density scoring rule can be used for acquiring a population density score corresponding to the risk early warning request; the risk event classification rule is a rule for classifying the specific grades of the risk events, and after the risk events of different risk types are classified, the risk events of different risk types can be quantitatively analyzed; the risk coefficient calculation formula is a formula for calculating the risk coefficient.

In one embodiment, as shown in fig. 4, step S151 includes sub-steps S1511, S1512, S1513, S1514, S1515, S1516 and S1517.

S1511, obtaining a risk distance score E corresponding to the risk early warning request according to the risk distance score rule.

And acquiring a risk distance score E corresponding to the risk early warning request according to the risk distance score rule, wherein the risk distance score rule comprises risk area information and risk distance score mapping information. The risk area information includes a risk area corresponding to each risk type, that is, the risk event can correspond to the corresponding risk area according to the type of the risk event; the risk distance score mapping information is mapping information used for scoring the distance between the geographical position information in the risk early warning request and the corresponding target risk area, the distances between different geographical position information and the target risk area are different, and the probability of disaster risks is also different, so that the risk distance score corresponding to the risk early warning request can be obtained through the risk distance score mapping information.

In an embodiment, step S1511 includes sub-steps S1511a, S1511b, and S1511 c.

S1511a, acquiring a target risk area matched with the risk type in the risk area information according to the risk type in the risk early warning request. The risk area information comprises risk areas corresponding to each risk type, and the risk areas matched with the risk types can be obtained as target risk areas according to the risk types in the risk early warning request.

for example, the target risk area corresponding to the risk type of "flood" is a river or lake, the target risk area corresponding to the risk type of "earthquake" is an earthquake zone, the target risk area corresponding to the risk type of "typhoon" is a coast, and the target risk area corresponding to the risk type of "mountain fire" is a mountain area.

s1511b, acquiring risk distance information between the target risk area and the geographical location information according to the geographical location information in the risk early warning request. If the geographical position information in the risk early warning request is in the risk area, the risk distance information between the target risk area and the geographical position information is '0'; and if the geographical position information in the risk early warning request is not in the risk area, the risk distance information between the target risk area and the geographical position information is the actual distance between the geographical position information and the edge of the target risk area.

S1511c, mapping the risk distance information according to the risk distance score mapping information to obtain a risk distance score E corresponding to the risk early warning request. The risk distance score mapping information comprises a plurality of mapping intervals, each mapping interval corresponds to one score value, the score value corresponding to one mapping interval matched with the risk distance information can be obtained according to the risk distance information, and the obtained score value is the risk distance score E.

For example, the risk distance score mapping information is shown in table 2.

TABLE 2

And the certain risk distance information is 63Km, and a risk distance score E obtained correspondingly according to the risk distance score mapping information in the table 2 is 6.

And S1512, acquiring a population density score P corresponding to the risk early warning request according to the population density score rule.

And acquiring a population density score P corresponding to the risk early warning request according to the population density score rule, wherein the population density score rule comprises population density information and population density score mapping information. The population density information comprises population density values corresponding to all geographic position information, the population density score mapping information is mapping information used for scoring the population density values corresponding to the geographic position information in the risk early warning request, and the harmfulness of the same disaster risk in areas with different population density values is different, so that the population density score corresponding to the risk early warning request can be obtained through the population density score mapping information.

In one embodiment, step S1512 includes sub-steps S1512a and S1512 b.

S1512a, obtaining a population density value corresponding to the geographical location information in population density information according to the geographical location information in the risk early warning request.

The population density information comprises population density values corresponding to the geographic position information, and the population density values corresponding to the geographic position information can be obtained according to the geographic position information in the risk early warning request.

S1512b, mapping the population density value according to the population density score mapping information to obtain a population density score P corresponding to the risk early warning request.

And mapping the population density value according to the population density score mapping information to obtain a population density score P corresponding to the risk early warning request. The population density score mapping information comprises a plurality of mapping intervals, each mapping interval corresponds to a score value, the score value corresponding to one mapping interval matched with the population density value can be obtained according to the population density value, and the obtained score value is the population density score P.

for example, the population density score mapping information is shown in table 3.

TABLE 3

A certain population density value is 1.55 ten thousand persons/km 2, and a population density score P is 4.5 correspondingly obtained according to the population density score mapping information in table 3.

S1513, the target risk events are classified according to the risk event classification rule to obtain event quantity information containing the quantity of the target risk events at each level.

and grading the target risk events according to the risk event grading rule to obtain event quantity information containing the quantity of the target risk events at each grade. The risk event classification rule is a rule for classifying the target risk events, the risk event classification rule comprises a specific rule for classifying the risk events of each risk type, and after all the target risk events are classified, event quantity information comprising the quantity of the target risk events of each level can be obtained.

For example, the risk type of the target risk event is "typhoon", and the rating rule corresponding to the risk type is shown in table 4.

Degree (grade)	15 and above	14	13	12	11	10	9	8	7	6
											Rating value	10	9	8	7	6	5	4	3	2	1

TABLE 4

The event quantity information obtained after ranking all the target risk events is shown in table 5.

rating value	Number of
		10	1
9	1
		8	4
7	3
		6	6
5	8
		4	3
3	1
		2	3
1	0

TABLE 5

S1514, obtaining a ranking value Dmax of the highest-level target risk event in the event quantity information, wherein the month of the highest-level target risk event is the same as the month of the time information.

For example, the target risk event with the ranking value of "10" in the event quantity information of table 5 is different from the time information month, the target risk event with the ranking value of "9" is the same as the time information month, and the highest-level target risk event with the ranking value Dmax of 9 is obtained.

S1515, obtaining the target risk event number N which is the same as the month of the time information and has a grading value larger than a first preset value in the event number information.

For example, the first preset value is 5, and the target risk event number with the ranking value greater than 5, which is the same as the month of the time information in the event number information of table 5, is 4.

S1516, acquiring a year F of an interval between the latest occurrence time of the target risk event with the grading value larger than a second preset value in the event quantity information and the time information, wherein the second preset value is larger than the first preset value.

And acquiring the latest occurrence time of the target risk event with the grading value larger than a second preset value in the event quantity information, and acquiring the years F of the interval between the occurrence time and the time information, wherein the second preset value is larger than the first preset value.

For example, the second preset value is 7, the occurrence time of the last target risk event with a ranking value greater than 7 in the event quantity information of table 5 is obtained, and the year F of the interval between the occurrence time and the time information is obtained as 3.

S1517, determining a risk coefficient Dv corresponding to the risk pre-warning request according to the risk coefficient calculation formula Dv ═ w1 × E + w2 × Dmax + w3 × N + w4 × F + w5 × P, where w1, w2, w3, w4, and w5 are weighted values.

And determining a risk coefficient Dv corresponding to the risk early warning request according to the risk coefficient calculation formula Dv-w 1 × E + w2 × Dmax + w3 × N + w4 × F + w5 × P, wherein w1, w2, w3, w4 and w5 are weighted values. Specifically, before using the risk coefficient calculation formula, the risk coefficient calculation formula is trained to adjust the weight values included in the formula. According to the method, the risk coefficient corresponding to the risk early warning request can be calculated, and the larger the numerical value of the risk coefficient is, the larger the risk of the risk event occurring at the corresponding place and the corresponding time in the risk early warning request is.

For example, the trained formula for calculating the risk factor is Dv of 0.12 × E +0.16 × Dmax +0.41 × N +0.33 × F +0.20 × P, and the corresponding risk factor Dv of 0.12 × 6+0.16 × 9+0.41 × 4+0.33 × 3+0.20 × 4.5 is calculated from the above information to 5.69.

and S152, if the judgment result is that the risk type information contains a plurality of risk types, calculating an average value of the target risk events according to a preset risk coefficient calculation model, and taking the average value as a risk coefficient corresponding to the risk early warning request.

and if the judgment result is that the risk type information contains a plurality of risk types, calculating an average value of the target risk events according to a preset risk coefficient calculation model to be used as a risk coefficient corresponding to the risk early warning request. If the risk type information includes a plurality of risk types, the overall risk coefficients of the plurality of risk types can be calculated according to the method, specifically, the target risk events corresponding to each risk type can be calculated respectively through the risk coefficient calculation model, and the average value of the calculation results corresponding to all the risk types obtained through calculation is used as the risk coefficient corresponding to the risk early warning request.

In an embodiment, step S152 includes sub-steps S1521, S1522, S1523 and S1524.

S1521, obtaining risk distance scores corresponding to each risk type in the risk early warning request according to the risk distance scoring rules to obtain risk distance scoring information.

And acquiring a risk distance score corresponding to each risk type in the risk early warning request according to the risk distance scoring rule to obtain risk distance scoring information, wherein the risk distance scoring rule comprises risk area information and risk distance scoring mapping information.

S1522, obtaining a population density score corresponding to the risk early warning request according to the population density score rule.

And acquiring a population density score corresponding to the risk early warning request according to the population density score rule, wherein the population density score rule comprises population density information and population density score mapping information.

S1523, the target risk events corresponding to each risk type are classified according to the risk event classification rule to obtain event classification quantity information containing each risk type.

And grading the target risk event corresponding to each risk type according to the risk event grading rule to obtain the event grading quantity information containing each risk type.

S1524, inputting the time information, the risk distance scoring information and the event grading number information in the risk early warning request into the risk coefficient calculation formula, so as to obtain an average value corresponding to a plurality of risk types in the risk early warning request, and obtain a risk coefficient.

And inputting the time information, the risk distance grading information and the event grading quantity information in the risk early warning request into the risk coefficient calculation formula so as to obtain an average value corresponding to a plurality of risk types in the risk early warning request to obtain a risk coefficient.

And S160, generating early warning prompt information corresponding to the risk early warning request according to a preset early warning prompt model and the risk coefficient.

And generating early warning prompt information corresponding to the risk early warning request according to a preset early warning prompt model and the risk coefficient. The early warning prompt information corresponding to the risk early warning request can be obtained through a preset early warning prompt model, specifically, the early warning prompt model comprises a plurality of prompt levels, each prompt level corresponds to one risk coefficient interval, the prompt level corresponding to the risk coefficient can be obtained according to the early warning prompt model, the early warning prompt information comprising the prompt level is generated, and the generated early warning prompt information can enhance the prompt effect on the user.

In one embodiment, as shown in fig. 5, step S160 includes sub-steps S161 and S162.

And S161, obtaining prompt grade information corresponding to the risk coefficient in the early warning prompt model.

And acquiring prompt grade information corresponding to the risk coefficient in the early warning prompt model. The early warning prompt model comprises a plurality of prompt levels, each prompt level corresponds to one risk coefficient interval, the risk coefficient is matched with the risk coefficient intervals, a certain risk coefficient interval in which the risk coefficient falls can be obtained, and the prompt level corresponding to the risk coefficient interval is obtained, so that the corresponding prompt level information can be obtained.

Specifically, in the early warning prompt model, multiple prompt levels can be distinguished through different prompt colors, different prompt characters and different prompt frequencies. For example, the display colors corresponding to the cue levels may be distinguished by four colors, green, blue, yellow, and red.

And S162, generating corresponding early warning prompt information according to the risk early warning request, namely the early warning grade information.

And generating corresponding early warning prompt information according to the risk early warning request, namely the early warning grade information. The risk early warning request comprises geographical position information and risk type information, corresponding early warning prompt information can be generated according to the geographical position information, the risk type information and the early warning grade information contained in the risk early warning request, and a user can determine the geographical position information, the risk type information and the threat of occurrence of corresponding risk events contained in the risk early warning prompt information after receiving the early warning prompt information.

In the disaster risk early warning method provided by the embodiment of the invention, the information crawling program is executed to obtain the risk events, the risk events are merged and recombined according to the time factors to obtain the risk event recombination information, the target risk events matched with the risk early warning request in the risk event recombination information are obtained, the target risk events are calculated according to the preset risk coefficient calculation model to obtain the risk coefficients, and the early warning prompt information corresponding to the risk coefficients and the risk early warning request is obtained. By the method, the risk of the risk event occurring at any time can be pre-warned, factors such as geographical position information and time information are integrated in the pre-warning process, and the accuracy of pre-warning the risk of the risk event occurring is improved.

The embodiment of the invention also provides a disaster risk early warning device, which is used for executing any embodiment of the disaster risk early warning method. Specifically, referring to fig. 6, fig. 6 is a schematic block diagram of a disaster risk early warning device according to an embodiment of the present invention. The disaster risk early warning device may be configured in a user terminal.

As shown in fig. 6, the disaster risk early warning device 100 includes an event information acquisition unit 110, an event information merging and recombining unit 120, a target risk event acquisition unit 130, a risk type information determination unit 140, a risk coefficient calculation unit 150, and an early warning presentation information generation unit 160.

The event information acquiring unit 110 is configured to crawl information that matches with preset risk keywords in a webpage corresponding to a preset network address to obtain event information that includes multiple risk events.

The event information merging and recombining unit 120 is configured to merge and recombine the risk events included in the event information according to a preset event factor to obtain risk event recombining information.

in another embodiment of the present invention, as shown in fig. 7, the event information merging and recombining unit 120 includes sub-units: an event key information extracting unit 121 and an event reorganization information acquiring unit 122.

An event key information extracting unit 121, configured to extract, according to the event factor, event key information corresponding to each risk event from the event information.

And the event reorganization information obtaining unit 122 is configured to merge and reorganize the risk events according to the event key information to obtain risk event reorganization information.

A target risk event obtaining unit 130, configured to obtain a target risk event matching the risk early warning request in the risk event reassembly information if a risk early warning request input by a user is received.

A risk type information determining unit 140, configured to determine whether the risk type information of the risk pre-warning request includes multiple risk types to obtain a determination result.

And a risk coefficient calculation unit 150, configured to calculate the target risk event according to a preset risk coefficient calculation model and the determination result to obtain a risk coefficient corresponding to the risk early warning request.

In other embodiments of the present invention, as shown in fig. 8, the risk coefficient calculating unit 150 includes sub-units: a first risk factor calculation unit 151 and a second risk factor calculation unit 152.

a first risk coefficient calculating unit 151, configured to calculate the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to a risk type in the risk early warning request if the determination result indicates that the risk type information does not include multiple risk types.

In another embodiment of the present invention, as shown in fig. 8, the first risk coefficient calculating unit 151 includes sub-units: a risk distance score acquisition unit 1511, a population density score acquisition unit 1512, an event number information acquisition unit 1513, a maximum ranking value acquisition unit 1514, an event number statistics unit 1515, a year information acquisition unit 1516, and a risk coefficient calculation unit 1517.

And a risk distance score obtaining unit 1511, configured to obtain a risk distance score E corresponding to the risk early warning request according to the risk distance score rule.

In other embodiments of the present invention, the risk distance score obtaining unit 1511 includes sub-units: a target risk zone matching unit 1511a, a risk distance information acquiring unit 1511b, and a risk distance information mapping unit 1511 c.

And a target risk area matching unit 1511a, configured to obtain, according to the risk type in the risk early warning request, a target risk area matched with the risk type in the risk area information.

and acquiring a target risk area matched with the risk type in the risk area information according to the risk type in the risk early warning request. The risk area information comprises risk areas corresponding to each risk type, and the risk areas matched with the risk types can be obtained as target risk areas according to the risk types in the risk early warning request.

a risk distance information obtaining unit 1511b, configured to obtain risk distance information between the target risk area and the geographical location information according to the geographical location information in the risk early warning request.

And acquiring risk distance information between the target risk area and the geographical position information according to the geographical position information in the risk early warning request. If the geographical position information in the risk early warning request is in the risk area, the risk distance information between the target risk area and the geographical position information is '0'; and if the geographical position information in the risk early warning request is not in the risk area, the risk distance information between the target risk area and the geographical position information is the actual distance between the geographical position information and the edge of the target risk area.

And a risk distance information mapping unit 1511c, configured to map the risk distance information according to the risk distance score mapping information to obtain a risk distance score E corresponding to the risk early warning request.

And mapping the risk distance information according to the risk distance score mapping information to obtain a risk distance score E corresponding to the risk early warning request. The risk distance score mapping information comprises a plurality of mapping intervals, each mapping interval corresponds to one score value, the score value corresponding to one mapping interval matched with the risk distance information can be obtained according to the risk distance information, and the obtained score value is the risk distance score E.

A population density score obtaining unit 1512, configured to obtain a population density score P corresponding to the risk early warning request according to the population density score rule.

in other embodiments of the present invention, the population density score obtaining unit 1512 includes sub-units: a population density value obtaining unit 1512a and a population density value mapping unit 1512 b.

The population density value obtaining unit 1512a is configured to obtain, according to the geographic location information in the risk early warning request, a population density value corresponding to the geographic location information in population density information.

The population density value mapping unit 1512b is configured to map the population density value according to the population density score mapping information to obtain a population density score P corresponding to the risk early warning request.

An event quantity information obtaining unit 1513, configured to grade the target risk events according to the risk event grading rule to obtain event quantity information including the number of target risk events at each level.

A maximum ranking value obtaining unit 1514, configured to obtain a ranking value Dmax of a highest-level target risk event in the event quantity information, which is the same as the month of the time information.

And the event quantity counting unit 1515 is configured to obtain the target risk event quantity N, which is the same as the month of the time information and has a ranking value greater than a first preset value, in the event quantity information.

A year information obtaining unit 1516, configured to obtain a year F of an interval between the latest occurrence time of the target risk event with a ranking value larger than a second preset value in the event number information and the time information, where the second preset value is larger than the first preset value.

A risk coefficient calculation unit 1517, configured to determine a risk coefficient Dv corresponding to the risk pre-warning request according to the risk coefficient calculation formula Dv ═ w1 × E + w2 × Dmax + w3 × N + w4 × F + w5 × P, where w1, w2, w3, w4, and w5 are all weight values.

And a second risk coefficient calculation unit 152, configured to, if the determination result indicates that the risk type information includes multiple risk types, calculate an average value obtained by calculating the target risk event according to a preset risk coefficient calculation model, and use the average value as a risk coefficient corresponding to the risk early warning request.

In other embodiments of the present invention, the second risk factor calculating unit 152 includes sub-units: a risk distance score information acquisition unit 1521, a population density score acquisition unit 1522, an event ranking number information acquisition unit 1523, and a risk coefficient average calculation unit 1524.

A risk distance score information obtaining unit 1521, configured to obtain, according to the risk distance score rule, a risk distance score corresponding to each risk type in the risk early warning request to obtain risk distance score information.

A population density score obtaining unit 1522, configured to obtain, according to the population density score rule, a population density score corresponding to the risk early warning request.

An event ranking number information obtaining unit 1523, configured to rank, according to the risk event ranking rule, the target risk event corresponding to each risk type to obtain event ranking number information including each risk type.

a risk coefficient average value calculating unit 1524, configured to input the time information, the risk distance scoring information, and the event ranking number information in the risk early warning request into the risk coefficient calculation formula, so as to obtain an average value corresponding to multiple risk types in the risk early warning request, so as to obtain a risk coefficient.

And an early warning prompt information generating unit 160, configured to generate early warning prompt information corresponding to the risk early warning request according to a preset early warning prompt model and the risk coefficient.

in other embodiments of the present invention, as shown in fig. 10, the warning prompt information generating unit 160 includes sub-units: a presentation level information acquisition unit 161 and a presentation information generation unit 162.

a prompt level information obtaining unit 161, configured to obtain prompt level information corresponding to the risk coefficient in the early warning prompt model.

And a prompt information generating unit 162, configured to generate corresponding early warning prompt information according to the risk early warning request, that is, the early warning level information.

The disaster risk early warning device provided by the embodiment of the invention is used for executing the disaster risk early warning method, an information crawling program is executed to obtain risk events, the risk events are merged and recombined according to time factors to obtain risk event recombination information, target risk events matched with risk early warning requests in the risk event recombination information are obtained, the target risk events are calculated according to a preset risk coefficient calculation model to obtain risk coefficients, and early warning prompt information corresponding to the risk coefficients and the risk early warning requests is obtained. By the method, the risk of the risk event occurring at any time can be pre-warned, factors such as geographical position information and time information are integrated in the pre-warning process, and the accuracy of pre-warning the risk of the risk event occurring is improved.

The disaster risk early warning apparatus may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 11.

Referring to fig. 11, fig. 11 is a schematic block diagram of a computer device according to an embodiment of the present invention.

referring to fig. 11, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.

The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, may cause the processor 502 to perform a disaster risk early warning method.

The processor 502 is used to provide computing and control capabilities that support the operation of the overall computer device 500.

The internal memory 504 provides an environment for running the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 may be enabled to execute the disaster risk early warning method.

The network interface 505 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 11 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 500 to which aspects of the present invention may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to implement the following functions: crawling information matched with preset risk keywords in a webpage corresponding to a preset network address to obtain event information containing a plurality of risk events; merging and recombining the risk events contained in the event information according to preset event factors to obtain risk event recombination information, wherein the event factors comprise names, time, places, risk types and degrees; if a risk early warning request input by a user is received, acquiring a target risk event matched with the risk early warning request in the risk event recombination information, wherein the risk early warning request comprises geographical position information, risk type information and time information; judging whether the risk type information of the risk early warning request contains a plurality of risk types to obtain a judgment result; calculating the target risk event according to a preset risk coefficient calculation model and the judgment result to obtain a risk coefficient corresponding to the risk early warning request; and generating early warning prompt information corresponding to the risk early warning request according to a preset early warning prompt model and the risk coefficient.

In an embodiment, when the processor 502 performs the step of merging and recombining the risk events included in the event information according to the preset event factor to obtain the risk event recombination information, the following operations are performed: extracting event key information corresponding to each risk event from the event information according to the event factors; and merging and recombining the risk events according to the event key information to obtain risk event recombination information.

In an embodiment, when the processor 502 performs the step of calculating the target risk event according to a preset risk coefficient calculation model and the determination result to obtain a risk coefficient corresponding to the risk pre-warning request, the following operations are performed: if the judgment result is that the risk type information does not contain a plurality of risk types, calculating the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to the risk type in the risk early warning request; and if the judgment result is that the risk type information contains a plurality of risk types, calculating an average value of the target risk events according to a preset risk coefficient calculation model to be used as a risk coefficient corresponding to the risk early warning request.

In an embodiment, when the processor 502 performs the step of calculating the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to a risk type in the risk early warning request, the following operations are performed: acquiring a risk distance score E corresponding to the risk early warning request according to the risk distance score rule; acquiring a population density score P corresponding to the risk early warning request according to the population density score rule; grading the target risk events according to the risk event grading rule to obtain event quantity information containing the quantity of the target risk events at each grade, wherein each grade of target risk events corresponds to a grading value; obtaining the grade value Dmax of the highest-grade target risk event in the event quantity information, which is the same as the month of the time information; acquiring a target risk event number N which is the same as the month of the time information and has a grading value larger than a first preset value in the event number information; acquiring the year F of an interval between the latest occurrence time of the target risk event with the grading value larger than a second preset value in the event quantity information and the time information, wherein the second preset value is larger than the first preset value; and determining a risk coefficient Dv corresponding to the risk early warning request according to the risk coefficient calculation formula Dv-w 1 × E + w2 × Dmax + w3 × N + w4 × F + w5 × P, wherein w1, w2, w3, w4 and w5 are weighted values.

In an embodiment, when the processor 502 executes the step of generating the warning prompt information corresponding to the risk warning request according to a preset warning prompt model and the risk coefficient, the following operations are executed: acquiring early warning grade information corresponding to the risk coefficient in the early warning prompt model; and generating corresponding early warning prompt information according to the risk early warning request, namely the early warning grade information.

Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 11 does not constitute a limitation on the specific construction of the computer device, and that in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 11, and are not described herein again.

It should be understood that, in the embodiment of the present invention, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

in another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a non-volatile computer readable storage medium. The computer-readable storage medium stores a computer program, wherein the computer program when executed by a processor implements the steps of: crawling information matched with preset risk keywords in a webpage corresponding to a preset network address to obtain event information containing a plurality of risk events; merging and recombining the risk events contained in the event information according to preset event factors to obtain risk event recombination information, wherein the event factors comprise names, time, places, risk types and degrees; if a risk early warning request input by a user is received, acquiring a target risk event matched with the risk early warning request in the risk event recombination information, wherein the risk early warning request comprises geographical position information, risk type information and time information; judging whether the risk type information of the risk early warning request contains a plurality of risk types to obtain a judgment result; calculating the target risk event according to a preset risk coefficient calculation model and the judgment result to obtain a risk coefficient corresponding to the risk early warning request; and generating early warning prompt information corresponding to the risk early warning request according to a preset early warning prompt model and the risk coefficient.

in an embodiment, the step of merging and recombining the risk events included in the event information according to a preset event factor to obtain risk event recombination information includes: extracting event key information corresponding to each risk event from the event information according to the event factors; and merging and recombining the risk events according to the event key information to obtain risk event recombination information.

in an embodiment, the step of calculating the target risk event according to a preset risk coefficient calculation model and the determination result to obtain a risk coefficient corresponding to the risk early warning request includes: if the judgment result is that the risk type information does not contain a plurality of risk types, calculating the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to the risk type in the risk early warning request; and if the judgment result is that the risk type information contains a plurality of risk types, calculating an average value of the target risk events according to a preset risk coefficient calculation model to be used as a risk coefficient corresponding to the risk early warning request.

In an embodiment, the step of calculating the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to a risk type in the risk early warning request includes: acquiring a risk distance score E corresponding to the risk early warning request according to the risk distance score rule; acquiring a population density score P corresponding to the risk early warning request according to the population density score rule; grading the target risk events according to the risk event grading rule to obtain event quantity information containing the quantity of the target risk events at each grade, wherein each grade of target risk events corresponds to a grading value; obtaining the grade value Dmax of the highest-grade target risk event in the event quantity information, which is the same as the month of the time information; acquiring a target risk event number N which is the same as the month of the time information and has a grading value larger than a first preset value in the event number information; acquiring the year F of an interval between the latest occurrence time of the target risk event with the grading value larger than a second preset value in the event quantity information and the time information, wherein the second preset value is larger than the first preset value; and determining a risk coefficient Dv corresponding to the risk early warning request according to the risk coefficient calculation formula Dv-w 1 × E + w2 × Dmax + w3 × N + w4 × F + w5 × P, wherein w1, w2, w3, w4 and w5 are weighted values.

in an embodiment, the step of generating the early warning prompt information corresponding to the risk early warning request according to a preset early warning prompt model and the risk coefficient includes: acquiring early warning grade information corresponding to the risk coefficient in the early warning prompt model; and generating corresponding early warning prompt information according to the risk early warning request, namely the early warning grade information.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a computer-readable storage medium, which includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a magnetic disk, or an optical disk.

While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A disaster risk early warning method is characterized by comprising the following steps:

2. the disaster risk early warning method according to claim 1, wherein the merging and recombining the risk events included in the event information according to a preset event factor to obtain risk event recombination information comprises:

Extracting event key information corresponding to each risk event from the event information according to the event factors;

And merging and recombining the risk events according to the event key information to obtain risk event recombination information.

3. the disaster risk early warning method according to claim 1, wherein the calculating the target risk event according to a preset risk coefficient calculation model and the judgment result to obtain a risk coefficient corresponding to the risk early warning request comprises:

If the judgment result is that the risk type information does not contain a plurality of risk types, calculating the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to the risk type in the risk early warning request;

and if the judgment result is that the risk type information contains a plurality of risk types, calculating an average value of the target risk events according to a preset risk coefficient calculation model to be used as a risk coefficient corresponding to the risk early warning request.

4. the disaster risk early warning method according to claim 3, wherein the risk coefficient calculation model includes a risk distance score rule, a population density score rule, a risk event classification rule and a risk coefficient calculation formula, and the calculating the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to a risk type in the risk early warning request includes:

Acquiring a risk distance score E corresponding to the risk early warning request according to the risk distance score rule;

acquiring a population density score P corresponding to the risk early warning request according to the population density score rule;

grading the target risk events according to the risk event grading rule to obtain event quantity information containing the quantity of the target risk events at each grade, wherein each grade of target risk events corresponds to a grading value;

Obtaining the grade value Dmax of the highest-grade target risk event in the event quantity information, which is the same as the month of the time information;

Acquiring a target risk event number N which is the same as the month of the time information and has a grading value larger than a first preset value in the event number information;

Acquiring the year F of an interval between the latest occurrence time of the target risk event with the grading value larger than a second preset value in the event quantity information and the time information, wherein the second preset value is larger than the first preset value;

And determining a risk coefficient Dv corresponding to the risk early warning request according to the risk coefficient calculation formula Dv-w 1 × E + w2 × Dmax + w3 × N + w4 × F + w5 × P, wherein w1, w2, w3, w4 and w5 are weighted values.

5. The disaster risk early warning method according to claim 1, wherein the generating early warning prompt information corresponding to the risk early warning request according to a preset early warning prompt model and the risk coefficient comprises:

Acquiring early warning grade information corresponding to the risk coefficient in the early warning prompt model;

And generating corresponding early warning prompt information according to the risk early warning request, namely the early warning grade information.

6. a disaster risk early warning device, comprising:

7. the disaster risk early warning device according to claim 6, wherein the event information merging and recombining unit comprises:

The event key information extraction unit is used for extracting event key information corresponding to each risk event from the event information according to the event factor;

And the event recombination information acquisition unit is used for merging and recombining the risk events according to the event key information to obtain risk event recombination information.

8. The disaster risk early warning device according to claim 6, wherein the risk coefficient calculation unit comprises:

A first risk coefficient calculation unit, configured to calculate the target risk event according to a preset risk coefficient calculation model to obtain a risk coefficient corresponding to a risk type in the risk early warning request if the determination result indicates that the risk type information does not include multiple risk types;

And the second risk coefficient calculation unit is used for calculating an average value of the target risk event according to a preset risk coefficient calculation model to serve as a risk coefficient corresponding to the risk early warning request if the judgment result shows that the risk type information contains a plurality of risk types.

9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the disaster risk warning method according to any one of claims 1 to 5 when executing the computer program.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to execute the disaster risk early warning method according to any one of claims 1 to 5.