CN109857750A - Settlement of insurance claim decision-making technique, device, computer equipment and storage medium - Google Patents
Settlement of insurance claim decision-making technique, device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN109857750A CN109857750A CN201910043296.8A CN201910043296A CN109857750A CN 109857750 A CN109857750 A CN 109857750A CN 201910043296 A CN201910043296 A CN 201910043296A CN 109857750 A CN109857750 A CN 109857750A
- Authority
- CN
- China
- Prior art keywords
- insurance
- danger
- data
- information
- settlement
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000003860 storage Methods 0.000 title claims abstract description 18
- 238000012795 verification Methods 0.000 claims abstract description 16
- 238000004590 computer program Methods 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 7
- 238000013507 mapping Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 8
- 239000000284 extract Substances 0.000 description 6
- 241000270322 Lepidosauria Species 0.000 description 5
- 239000000463 material Substances 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 238000003672 processing method Methods 0.000 description 3
- 238000002910 structure generation Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000012550 audit Methods 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 241000209140 Triticum Species 0.000 description 1
- 235000021307 Triticum Nutrition 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 230000009193 crawling Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Landscapes
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The invention discloses a kind of settlement of insurance claim decision-making technique, device, equipment and storage mediums, which comprises obtains settlement of insurance claim request, the settlement of insurance claim request includes insurance policy information；Policy information in the insurance policy information and preset database is subjected to consistency desired result；If verification passes through, insurance is extracted from the insurance policy information and is in danger information, wherein the insurance be in danger packet include strategical vantage point point, be in danger time and weather information of being in danger；Using be in danger place and the time of being in danger as constraint condition, meteorological data is retrieved in preset meteorological data table, obtains Target Weather Information；Calculate the semantic similarity of the Target Weather Information Yu the weather information of being in danger；If the semantic similarity is more than or equal to preset similarity threshold, by the corresponding insurance of the insurance policy information as wait insurance of settling a claim.The settlement of insurance claim decision-making technique realizes the intelligent decision to settlement of insurance claim, improves the settlement of insurance claim efficiency of decision-making.
Description
Technical field
The present invention relates to data processing field more particularly to a kind of settlement of insurance claim decision-making technique, device, computer equipment and
Storage medium.
Background technique
Currently, settlement of insurance claim case relevant to weather, for example, the yield in agricultural insurance Claims Resolution is closely closed with weather
Vehicle driving will receive weather larger impact (such as greasy weather and hail) in connection or car insurance Claims Resolution.It usually requires to protect
The personnel that report a case to the security authorities of danger provide meteorological data proof or the picture of the weather condition of strategical vantage point or video are provided to prove, undoubtedly increase
Add the workload for the personnel of reporting a case to the security authorities, and increased the difficulty of settlement of insurance claim audit, reduces the settlement of insurance claim efficiency of decision-making.
Summary of the invention
The embodiment of the present invention provides a kind of settlement of insurance claim decision-making technique, device, computer equipment and storage medium, to solve
The lower problem of the settlement of insurance claim efficiency of decision-making.
A kind of settlement of insurance claim decision-making technique, comprising:
Settlement of insurance claim request is obtained, the settlement of insurance claim request includes insurance policy information；
Policy information in the insurance policy information and preset database is subjected to consistency desired result；
If verification passes through, insurance is extracted from the insurance policy information and is in danger information, wherein the insurance is in danger
Packet includes strategical vantage point point, be in danger time and weather information of being in danger；
Using be in danger place and the time of being in danger as constraint condition, to meteorological data in preset meteorological data table
It is retrieved, obtains Target Weather Information；
Calculate the semantic similarity of the Target Weather Information Yu the weather information of being in danger；
It is if the semantic similarity is more than or equal to preset similarity threshold, the insurance policy information is corresponding
Insurance be used as wait settle a claim insurance.
A kind of settlement of insurance claim decision making device, comprising:
Policy information obtains module, and for obtaining settlement of insurance claim request, the settlement of insurance claim request includes that insurance policy is believed
Breath；
Policy information verifies correction verification module, for by the policy information in the insurance policy information and preset database
Carry out consistency desired result；
Be in danger data obtaining module, if passing through for verifying, extracts insurance from the insurance policy information and is in danger
Information, wherein it is described insurance be in danger packet include strategical vantage point point, be in danger time and weather information of being in danger；
Weather information obtains module, for using be in danger place and the time of being in danger as constraint condition, preset
Meteorological data is retrieved in meteorological data table, obtains Target Weather Information；
Similarity calculation module, it is similar to the semanteme of the weather information of being in danger for calculating the Target Weather Information
Degree；
Determining module is insured wait settle a claim, if being more than or equal to preset similarity threshold for the semantic similarity,
Then by the corresponding insurance of the insurance policy information as wait insurance of settling a claim.
A kind of computer equipment, including memory, processor and storage are in the memory and can be in the processing
The computer program run on device, the processor realize above-mentioned settlement of insurance claim decision-making technique when executing the computer program.
A kind of computer readable storage medium, the computer-readable recording medium storage have computer program, the meter
Calculation machine program realizes above-mentioned settlement of insurance claim decision-making technique when being executed by processor.
In above-mentioned settlement of insurance claim decision-making technique, device, computer equipment and storage medium, acquisition settlement of insurance claim first is asked
It asks, the policy information in insurance policy information and preset database is subjected to consistency desired result, realizes and insurance policy is believed
The verifying of breath；If verification passes through, insurance is extracted from insurance policy information and is in danger information, wherein insurance is in danger packet
Include strategical vantage point point, be in danger time and weather information of being in danger；Then, place and the time is in danger as constraint condition to be in danger, default
Meteorological data table in meteorological data is retrieved, obtain Target Weather Information；Finally, calculating Target Weather Information and being in danger
The semantic similarity of weather information carries out into one the insurance corresponding insurance of information that is in danger according to the semantic similarity so as to subsequent
Step processing；If semantic similarity is more than or equal to preset similarity threshold, the corresponding insurance of insurance policy information is made
So that user be avoided to need to provide the unnecessary trouble of Claims Resolution material bring of weather condition, to realize wait insurance of settling a claim
To the intelligent decision of settlement of insurance claim, the settlement of insurance claim efficiency of decision-making is improved.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below by institute in the description to the embodiment of the present invention
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is the application environment schematic diagram of settlement of insurance claim decision-making technique provided in an embodiment of the present invention；
Fig. 2 is one exemplary diagram of settlement of insurance claim decision-making technique provided in an embodiment of the present invention；
Fig. 3 is another exemplary diagram of settlement of insurance claim decision-making technique provided in an embodiment of the present invention；
Fig. 4 is another exemplary diagram of settlement of insurance claim decision-making technique provided in an embodiment of the present invention；
Fig. 5 is another exemplary diagram of settlement of insurance claim decision-making technique provided in an embodiment of the present invention；
Fig. 6 is another exemplary diagram of settlement of insurance claim decision-making technique provided in an embodiment of the present invention；
Fig. 7 is a functional block diagram of settlement of insurance claim decision making device provided in an embodiment of the present invention；
Fig. 8 is another functional block diagram of settlement of insurance claim decision making device provided in an embodiment of the present invention；
Fig. 9 is a schematic diagram of computer equipment provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on this hair
Embodiment in bright, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
Settlement of insurance claim decision-making technique provided by the present application, can be applicable in the application environment such as Fig. 1, wherein client (meter
Calculate machine equipment) it is communicated by network with server-side, client sends settlement of insurance claim request to server-side, and server-side will be insured
Policy information in policy information and preset database carries out consistency desired result；If verification passes through, from insurance policy information
In extract insurance and be in danger information, wherein insurance be in danger packet include strategical vantage point point, be in danger time and weather information of being in danger；Into
And to be in danger place and the time is in danger as constraint condition, meteorological data is retrieved in preset meteorological data table, is obtained
Target Weather Information；Finally, calculating the semantic similarity of Target Weather Information with weather information of being in danger；If semantic similarity is greater than
Or be equal to preset similarity threshold, then by the corresponding insurance of insurance policy information as wait insurance of settling a claim.Wherein, client
It can be, but not limited to be various personal computers, laptop, smart phone, tablet computer and portable wearable device.
Server-side can be realized with the server cluster of the either multiple server compositions of independent server.
In one embodiment, as shown in Fig. 2, being applied to be illustrated for the server-side in Fig. 1 in this way, including
Following steps:
S10: obtaining settlement of insurance claim request, and settlement of insurance claim request includes insurance policy information.
Wherein, settlement of insurance claim request be client initiate to currently insuring the trigger request settled a claim.Specifically, it uses
Family inputs corresponding instruction or information by client to trigger settlement of insurance claim request or user at the scene or sales counter mentions
Apply out, settlement of insurance claim request is initiated by client by contact staff.Settlement of insurance claim request is sent to service by client
End, server-side get settlement of insurance claim request.Insurance refer to insurance relevant to property, as in car insurance paddle danger or
Crop yield danger in person's agricultural insurance etc..Insurance policy information refers to that insurance company provides and purchase connects insurable
By information in the policy information table of agreement, the information that the information including insurance information table itself such as declaration form number and user fill in is such as
User's signature and Claims Resolution application information etc..
S20: the policy information in insurance policy information and preset database is subjected to consistency desired result.
Wherein, consistency desired result refer to the policy information in insurance policy information and preset database whether unanimously into
The verification of row judgement, to judge whether insurance policy information is true and reliable.So as to subsequent according to the consistent of insurance policy information
Property or nonuniformity to settlement of insurance claim request respond.Specifically, it can be instructed by structured query language (SQL)
Insurance policy information is inquired in preset database, consistency desired result is carried out according to the result of inquiry.It is to be appreciated that
By the way that the policy information in insurance policy information and preset database is carried out consistency desired result, insurance is protected to realize
The verifying of single information.
S30: if verification passes through, insurance is extracted from insurance policy information and is in danger information, wherein insurance is in danger information
Including place of being in danger, be in danger time and weather information of being in danger.
Wherein, the information that information of being in danger refers to description accident or risk is insured, for as the condition settled a claim.Out
Strategical vantage point point refers to that the geographical location that accident perhaps risk occurs time of being in danger refers to the time that accident or risk occurs, and is in danger
Weather information refers to the weather condition that accident or risk occurs.Information of being such as in danger is " on October 16th, 2018 Shenzhen's Luohu
15 grades of typhoons occur for area, and wheat seedling is caused to be uprooped ", place therein of being in danger, be in danger time and weather information of being in danger difference
It is " on October 16th, 2018 ", " In Luohu District of Shenzhen Municipal " and " 15 grades of typhoons ".
Further, after verification passes through, i.e., the declaration form number in insurance policy information and the guarantor in preset database
Single number is consistent namely insurance policy information truth exists.Then, insurance is extracted in the insurance policy information to be in danger letter
Breath, extracting method can be semantic-based information extracting method, be also possible to the information extracting method based on Python.Wherein
Semantic-based information extracting method be the semanteme for analyzing preset key words, then by the language of preset key words
The word of justice extracts the information that can satisfy querying condition as the letter that is in danger as querying condition, and then in insurance policy information
Breath.And the information extracting method based on Python is by word_tokenize () tool in Python NLTK to guarantor
Dangerous policy information carries out word segmentation, then by the word and preset Keywords matching after segmentation, thus by successful match
Word, which is used as, insures information of being in danger.
S40: place and the time is in danger as constraint condition to be in danger, meteorological data is carried out in preset meteorological data table
Retrieval, obtains Target Weather Information.
Wherein, constraint condition refers to the condition that must be chosen, for guaranteeing to need to obtain the accuracy of data or information.
Constraint condition in the present embodiment is to be in danger place and to be in danger the time, it is possible to understand that ground, if constraint condition is only place of being in danger,
It is that the difference comprising same place of being in danger is in danger multiple information of time according to the information that the constraint condition obtains, if constraint condition
It is only in danger the time, is that the difference comprising the same time of being in danger is in danger the multiple of place according to the information that the constraint condition obtains
Information can generate a large amount of redundancy in these information, and then influence subsequent to be further processed insurance information of being in danger.In advance
If meteorological data table refer to the pre-set tables of data for being stored with weather information, for as weather information inquiry, verifying
Or the dictionary library of analysis etc..Target Weather Information refers to weather condition under specific time and feature site condition, such as 2018
In Luohu District of Shenzhen Municipal on October 16 weather condition under this condition is 15 grades of typhoons, then Target Weather Information is " 15 grades of platforms
Wind ".
Specifically, place and be in danger the time as constraint condition to be in danger, i.e., simultaneously using be in danger place and be in danger the time as
Index is instructed by data query in preset meteorological data table and carries out retrieval and inquisition, obtains Target Weather Information.Wherein count
It can be subquery (subquery) instruction according to inquiry instruction and be also possible to conjunctive query (unionquery) instruction.Subquery refers to
Order refers to one or more nested inquiry instruction in inquiry instruction.For example, SELECT*FROM WeaData WHERE time
IN (SELECT time WHERE time='2018 October 15 ' FROM Weather WHERE city='
shenzhen')；Wherein, WeaData is Target Weather Information, and time and city are respectively be in danger time and place of being in danger,
Weather is preset meteorological data table.Conjunctive query, which refers to, inquires (a plurality of select sentence) for multiple, it is enterprising recording
The querying method of row splicing.For example, SELECT*FROM WeaData WHERE city='shenzhen') union
SELECT*FROM WeaData WHERE time='2018 October 15 '；Wherein, WeaData is Target Weather Information,
Time and city is respectively be in danger time and place of being in danger.
S50: the semantic similarity of Target Weather Information with weather information of being in danger is calculated.
Wherein, semantic similarity refers to degree alike in connotation or semantic content between text or word.Specifically,
Target Weather Information and the semantic similarity for weather information of being in danger can be the calculation method based on vector space model can also be with
It is the calculation method based on Hamming distance.The specific calculating process of calculation method therein based on vector space model are as follows: first
Target Weather Information and weather information of being in danger are converted to space vector respectively, if including meteorological data point in Target Weather Information
It Wei not t1, t2..., tN, then the space vector of Target Weather Information is expressed as T (t1, t2..., tN).It is identical with this, if being in danger gas
In image information comprising meteorological data be respectively s1, s2..., sN, then the space vector for weather information of being in danger is expressed as S (s1, s2...,
sN), at this point, Target Weather Information to be expressed as to the form of vector with weather information of being in danger, semantic similarity can pass through two
Corner dimension between a vector calculates, and angle is bigger, and the semantic similarity of Target Weather Information and weather information of being in danger is just
It is lower.The specific calculating process of calculation method therein based on Hamming distance are as follows: first have to determine Target Weather Information and be in danger
Then each Target Weather Information is expressed as its corresponding code word with weather information of being in danger by the respective code word set of weather information,
X=(X is expressed as the code word of Target Weather Information1, X2..., XN), Y=is expressed as the code word for weather information of being in danger
(Y1, Y2..., YN), formula is used to code word X and code word YIt is calculated, obtains semantic similarity D
(X,Y).In formula, XiAnd YiIt is expressed as i-th bit in Target Weather Information D1 code word corresponding with the weather information D2 that is in danger
Component, value are 0 or 1,It is the operator of XOR operation.
By calculating the semantic similarity of Target Weather Information with weather information of being in danger in this step, so that subsequent basis should
Semantic similarity to insurance be in danger information it is corresponding insurance be further processed.
S60: if semantic similarity is more than or equal to preset similarity threshold, by the corresponding guarantor of insurance policy information
Danger is as wait insurance of settling a claim.
Wherein, the insurance for meeting Claims Resolution condition is referred to wait insurance of settling a claim.Preset similarity threshold refers to be protected for decision
Danger be in danger the corresponding insurance of information semantic similarity critical value.If semantic similarity is more than or equal to preset similarity
Threshold value, i.e. the insurance of the insurance be in danger insurance weather information authenticity in information and validity it is larger, meet Claims Resolution threshold.It can
To understand ground, the semantic similarity of the insurance weather information and Target Weather Information be in danger in information is insured by comparison,
And insure the insurance that semantic similarity is more than or equal to preset similarity threshold as wait settle a claim, so that user be avoided to need
The unnecessary trouble of Claims Resolution material bring of weather condition is provided, the intelligent decision to settlement of insurance claim is realized, improves
The settlement of insurance claim efficiency of decision-making.
In the present embodiment, acquisition settlement of insurance claim request first, by the declaration form in insurance policy information and preset database
Information carries out consistency desired result, realizes the verifying to insurance policy information；If verification passes through, mentioned from insurance policy information
Insurance is taken out to be in danger information, wherein insurance be in danger packet include strategical vantage point point, be in danger time and weather information of being in danger；Then, with
Place and time of being in danger be in danger for constraint condition, meteorological data is retrieved in preset meteorological data table, obtains target
Weather information；Finally, the semantic similarity of Target Weather Information with weather information of being in danger is calculated, so as to subsequent according to the semanteme phase
Like degree to insurance be in danger information it is corresponding insurance be further processed；If semantic similarity is more than or equal to preset similar
Threshold value is spent, then is insured the corresponding insurance of insurance policy information as wait settle a claim, so that user be avoided to need to provide weather condition
The unnecessary trouble of Claims Resolution material bring, realize the intelligent decision to settlement of insurance claim, improve settlement of insurance claim decision effect
Rate.
In one embodiment, as shown in figure 3, before step S30, settlement of insurance claim decision-making technique further include:
S70: meteorological data is obtained using crawler technology.
Wherein, meteorological data refers to that one group of data of reflection weather specifically reflect the gas of different location different time
As situation, such as weather condition and meteorological disaster.Meteorological data includes temperature, precipitation, temperature, humidity, wind speed, sunshine or gas
As data such as disasters, for carrying out decision to settlement of insurance claim relevant to weather, for example, agricultural risk is settled a claim, car insurance Claims Resolution
Deng.
Wherein, reptile instrument is the tool for crawling web page contents corresponding to web page address automatically according to certain rules,
Such as Python reptile instrument.Specifically, web page contents corresponding to target webpage address are crawled using reptile instrument, to obtain
Meteorological data in the present embodiment, crawls the corresponding webpage in target webpage address using reptile instrument, is not necessarily to manual search, favorably
Efficiency is obtained in improving meteorological data.
It should be noted that reptile instrument, which obtains meteorological data frequency, can be real-time acquisition, it is also possible to every fixation
Time interval periodically obtain.It can specifically be selected according to the needs of practical application, herein with no restrictions.
S80: meteorological data is handled into structuring, obtains targeted gas phase data.
Wherein, structuring processing, which refers to, is converted into structural data, including data column and attribute column for unstructured data,
Data column are the data point or observed value being collected into, and attribute column is to indicate the field of the single attribute of each observed value.For example, coming
There is the attribute column for indicating client trading event from the data of online retail shop and comprising bought commodity, quantity, price, timestamp
Etc. information data column.Targeted gas phase data refer to structuring meteorological data, and the structuring processing method in the present embodiment can be with
It is the structuring processing method using optimization table, is also possible to the structuring processing method of relational database, concrete foundation is answered
It is selected with scene, is not construed as limiting herein.
It is to be appreciated that each meteorological data includes meteorological date and meteorological place, different time and different ground
Point, meteorological data is multifarious, in the present embodiment, is tied according to meteorological date and meteorological place to the meteorological data of acquisition
Structureization processing.The meteorological date refers to this kind meteorological data corresponding time, and meteorological place refers to the meteorological data correspondingly
Manage position.
S90: targeted gas phase data are saved using Object Relation Mapping frame, obtain meteorological data table.
Wherein, Object Relation Mapping frame includes but is not limited to Tbatis, and Tbatis is a kind of object relationship of lightweight
(ORM) frame is mapped, for storing to file.In the present embodiment, Tbatis is for storing targeted gas phase data.
Specifically, targeted gas phase data are passed into backstage by SpingMVC frame, then data is saved in by Tbatis
Library.Wherein, SpringMVC frame provides the global function MVC module of building web application.It can be inserted into using Spring
MVC framework, can choose is using built-in Spring Web frame Web frame.Tbatis will by way of xml or note
The various targeted gas phase data configurations to be executed, and mapped by the sql sentence in java object and statement
The sql sentence finally executed is generated, sql sentence is finally executed by Tbatis frame and targeted gas phase data are mapped to java pairs
As and return, obtain meteorological data table.Realize quick and precisely generation meteorological data table.Wherein, source of meteorological data refers to by gas
Image data composition relational database in meteorological data table, the tables of data can as how passing meteorological data carry out verifying or
Person's inquiry, to reduce the cumbersome verifying called data from meteorological system or provide video or picture proof.
In the present embodiment, the corresponding webpage in target webpage address is crawled using reptile instrument first, manual search is not necessarily to, has
Efficiency is obtained conducive to meteorological data is improved.Then meteorological data is handled into structuring, obtains targeted gas phase data.Finally, making
Targeted gas phase data are saved with Object Relation Mapping frame, obtain meteorological data table, to reduce from meteorological system tune
With data or provide the cumbersome verifying that video or picture prove.
In one embodiment, as shown in figure 4, in step S70, meteorological data is handled into structuring, is specifically included as follows
Step:
S71: target webpage is obtained.
Wherein, target webpage refers to webpage relevant to weather information.Target network address in the present embodiment can be China
Meteorological network (http://www.cma.gov.cn/), be also possible to Chinese weather net (http: //
www.weather.com.cn/).Specifically, the network address that target webpage can be read by read () method, to getHtml ()
One network address of function passes, and full page is downloaded, obtain the page of target webpage.
S72: the information in target webpage is extracted using preset regular expression, obtains target information.
Wherein, preset regular expression is a kind of string matching and processing rule, for extracting the information in webpage.
Preset regular expression includes but is not limited to Python regular expression.Target information refers to the net with regular expression matching
Page information.The target information can be the information such as temperature, precipitation, humidity, wind speed, sunshine or meteorological calamity.
Specifically, the information with preset regular expression matching is filtered from target webpage, then extracts the information, into
And obtain target information.It is to be appreciated that the information in target webpage is extracted by using preset regular expression,
Improve the accuracy of target information.
S73: parsing target information obtains meteorological data.
Specifically, target information detailed process is parsed are as follows: the parsing module in library is parsed by crawler first and is believed target
Breath carries out data analysis, then passage path expression formula extracts the target information after parsing, and the target information after parsing is saved
In the database, finance product information is obtained.Crawler therein parsing library can be BeautifulSoup parsing library, can also be with
It is lxml parsing library.It is to be appreciated that can rapidly and accurately get meteorological data by parsing target information.
In the present embodiment, firstly, obtaining target webpage；Then, using preset regular expression in target webpage
Information extracts, and improves the accuracy of target information；Finally, parsing target information, obtains meteorological data, thus quickly quasi-
Really get meteorological data.
In one embodiment, as shown in figure 5, in step S80, meteorological data is handled into structuring, is specifically included as follows
Step:
S81: according to meteorological data, the corresponding tables of data of template generation meteorological data is generated according to preset data list structure
Structure.
Wherein, preset data list structure generation template refers to preset for generating number corresponding with meteorological data
According to the template of storage organization.Data list structure refers to the tactic pattern of data storage.It is to be appreciated that in meteorological data application
Search condition includes beginning and ending time (meteorological date) and area station information (meteorological place), therefore, in the corresponding data of meteorological data
In table structure, beginning of area's station number as line unit, i.e. line unit are area's station number _ retrieval time _ area code mark.Observe class field
Four different column families are belonging respectively to according to temperature, air pressure, direction and four major class of meteorological disaster in meteorological data, to retrieve
When as the few as possible extraneous data of load to lifting system performance.
For example, during retrieval on October 1st, 2018 (starting when 0) on October 1st, 2018 (ending when 24), Shenzhen sieve
The meteorological measuring of lake region, it is " 0001_201810010000_+086 " that starting line unit, which can be set, terminates line unit and is
" 0001_201810012400_+086 ", wherein 0001 be expressed as area's station number mark, " 201810010000 " and
" 201810012400 " are expressed as initial time and end time, and "+086 " is expressed as the area identification of In Luohu District of Shenzhen Municipal.
S82: parsing meteorological data using POI technology, obtains initial meteorological data.
Wherein, POI is a kind of open source code function library, for providing API to java applet to Microsoft Office
The function that format archives are read and write.In the present embodiment, meteorological data is parsed using POI technology, so that meteorological data mark
Standardization, so that more efficiently the meteorological data is conducted further analysis or be stored.Initial meteorological data refers to standardization
The meteorological data of structure.
S83: initial meteorological data is imported into data list structure, obtains meteorological data table.
Specifically, when client initiation data import request, server-side gets what needs imported from importing request
The initial meteorological data is matched with the line unit in data list structure, if successful match, is arranged by initial meteorological data
Key matching, after column key successful match, then imported into data list structure for the initial meteorological data, obtains meteorological data table, protects
The acquisition efficiency of meteorological data table is demonstrate,proved.
In the present embodiment, firstly, generating template generation meteorological data according to preset data list structure according to meteorological data
Corresponding data list structure；Then, meteorological data is parsed using POI technology, obtains initial meteorological data；Finally, will
Initial meteorological data is imported into data list structure, is obtained meteorological data table, be ensure that the acquisition efficiency of meteorological data table.
In one embodiment, Target Weather Information includes target master data and targeted gas phase disaster data, meteorology of being in danger
Packet includes dangerous master data and meteorological disaster data of being in danger.
Wherein, target master data refers to the corresponding number of base values relevant to weather condition in Target Weather Information
According to such as temperature, humidity or wind direction corresponding data describe makings disaster in targeted gas phase disaster data Target Weather Information
Data, such as hail, 16 grades of typhoon or heavy rain (snow).Master data of being in danger refer to be in danger in weather information with weather feelings
The corresponding data of the relevant base values of condition, meteorological disaster of being in danger data, which refer to be in danger in weather information, describes makings disaster
Data.
In this embodiment, it as shown in fig. 6, in step S50, calculates targeted gas phase and is in danger and weather information and be in danger meteorology
The semantic similarity of information, specifically comprises the following steps:
S51: the semantic similarity of target master data with master data of being in danger is calculated, as master data similarity.
Wherein, master data similarity is for reflecting that the semanteme between target master data and master data of being in danger is similar
Degree.Specifically, master data similarity can be calculated using the calculation method of the semantic similarity in step S50, herein
It repeats no more.
S52: the semantic similarity of targeted gas phase disaster data with meteorological disaster data of being in danger is calculated, as meteorological disaster number
According to similarity.
Wherein, meteorological disaster data similarity be for reflect targeted gas phase disaster data and be in danger meteorological disaster data it
Between semantic similarity.Specifically, meteorological disaster can be calculated using the calculation method of the semantic similarity in step S50
Data similarity, details are not described herein again.
S53: master data similarity is weighted with meteorological disaster data similarity and is added, semantic similarity is obtained.
Specifically, corresponding weight, then, base are respectively set to master data similarity and meteorological disaster data similarity
Notebook data similarity and meteorological disaster data obtain semantic similar respectively multiplied by carrying out being added calculating after corresponding weight
Degree, due to having fully considered the influence of master data similarity and meteorological disaster data to computing semantic similarity, to improve
The accuracy of semantic similarity.
It should be noted that the weight that the weight setting in the present embodiment should meet meteorological disaster data is greater than master data
The weight of similarity advantageously ensures that the accuracy of semantic similarity.Due to being protected caused by meteorological disaster in settlement of insurance claim case
The case where danger Claims Resolution, is in the majority, therefore more lays particular emphasis on meteorological disaster data similarity, more can guarantee the accuracy of calculating, Jin Erti
The accuracy of high subsequent settlement of insurance claim decision.
In one embodiment, after step 50, settlement of insurance claim decision-making technique further include:
If semantic similarity is less than preset similarity threshold, the corresponding insurance policy information of semantic similarity is sent
It is audited to client.
Specifically, if semantic similarity is less than preset similarity threshold, i.e. the insurance information credibility that is in danger is lower, because
This, is sent to client for the corresponding insurance policy information of semantic similarity and audits, and guarantees the accurate of settlement of insurance claim decision
Property, the appearance of fraud settlement of insurance claim is prevented, the anti-fraud control to settlement of insurance claim is realized.
In the present embodiment, if semantic similarity is less than preset similarity threshold, by the corresponding insurance of semantic similarity
Policy information is sent to client and is audited, and guarantees the accuracy of settlement of insurance claim decision, prevents the appearance of fraud settlement of insurance claim,
Realize the anti-fraud control to settlement of insurance claim.
It should be understood that the size of the serial number of each step is not meant that the order of the execution order in above-described embodiment, each process
Execution sequence should be determined by its function and internal logic, the implementation process without coping with the embodiment of the present invention constitutes any limit
It is fixed.
In one embodiment, a kind of settlement of insurance claim decision making device is provided, the settlement of insurance claim decision making device and above-described embodiment
Middle settlement of insurance claim decision-making technique corresponds.As shown in fig. 7, the settlement of insurance claim decision making device includes that policy information obtains module
10, policy information verification correction verification module 20, the data obtaining module 30 that is in danger, weather information obtain module 40, similarity calculation mould
Block 50 and determining module 60 is insured wait settle a claim.Detailed description are as follows for each functional module:
Policy information obtains module 10, and for obtaining settlement of insurance claim request, settlement of insurance claim request includes insurance policy information；
Policy information verifies correction verification module 20, for by the policy information in insurance policy information and preset database into
Row consistency desired result；
Be in danger data obtaining module 30, if passing through for verifying, insurance is extracted from insurance policy information and is in danger letter
Breath, wherein insurance be in danger packet include strategical vantage point point, be in danger time and weather information of being in danger；
Weather information obtains module 40, for place and being in danger the time as constraint condition to be in danger, in preset meteorological number
Meteorological data is retrieved according in table, obtains Target Weather Information；
Similarity calculation module 50, for calculating the semantic similarity of Target Weather Information with weather information of being in danger；
Determining module 60 is insured wait settle a claim, if being more than or equal to preset similarity threshold for semantic similarity,
By the corresponding insurance of insurance policy information as wait insurance of settling a claim.
Preferably, settlement of insurance claim decision making device further include meteorological data obtain module, targeted gas phase data acquisition module and
Meteorological data table obtains module.
Meteorological data obtains module, for obtaining meteorological data using crawler technology；
Targeted gas phase data acquisition module obtains targeted gas phase data for being handled into structuring meteorological data；
Meteorological data table is obtained module and obtained for being saved using Object Relation Mapping frame to targeted gas phase data
To meteorological data table.
Preferably, it includes target webpage acquiring unit, target information acquiring unit and meteorological number that meteorological data, which obtains module,
According to acquiring unit.
Target webpage acquiring unit, for obtaining target webpage；
Target information acquiring unit, for being extracted using preset regular expression to the information in target webpage,
Obtain target information；
Meteorological data acquiring unit obtains meteorological data for parsing target information.
Preferably, targeted gas phase data acquisition module includes data list structure generation unit, initial meteorological data acquisition list
Member and meteorological data table acquiring unit.
Data list structure generation unit, for generating template generation according to preset data list structure according to meteorological data
The corresponding data list structure of meteorological data；
Initial meteorological data acquiring unit obtains initial gas as number for parsing using POI technology to meteorological data
According to；
Meteorological data table acquiring unit obtains meteorological data for initial meteorological data to be imported into data list structure
Table.
Preferably, it as shown in figure 8, Target Weather Information includes target master data and targeted gas phase disaster data, is in danger
Weather information includes be in danger master data and meteorological disaster data of being in danger；Similarity calculation module includes master data similarity meter
Calculate unit 51, meteorological disaster data similarity calculated 52 and similarity calculated 53.
Master data similarity calculated 51, it is similar to the semanteme for master data of being in danger for calculating target master data
Degree, as master data similarity；
Meteorological disaster data similarity calculated 52, for calculating targeted gas phase disaster data and the meteorological disaster number that is in danger
According to semantic similarity, as meteorological disaster data similarity；
Similarity calculated 53, for master data similarity and meteorological disaster data similarity to be weighted phase
Add, obtains semantic similarity.
Preferably, settlement of insurance claim decision making device further includes auditing module, if being less than for semantic similarity preset similar
Threshold value is spent, then the corresponding insurance policy information of semantic similarity is sent to client and audited.
Specific about settlement of insurance claim decision making device limits the limit that may refer to above for settlement of insurance claim decision-making technique
Fixed, details are not described herein.Modules in above-mentioned settlement of insurance claim decision making device can fully or partially through software, hardware and its
Combination is to realize.Above-mentioned each module can be embedded in the form of hardware or independently of in the processor in computer equipment, can also be with
It is stored in the memory in computer equipment in a software form, in order to which processor calls the above modules of execution corresponding
Operation.
In one embodiment, a kind of computer equipment is provided, which can be server, internal junction
Composition can be as shown in Figure 9.The computer equipment include by system bus connect processor, memory, network interface and
Database.Wherein, the processor of the computer equipment is for providing calculating and control ability.The memory packet of the computer equipment
Include non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system, computer program and data
Library.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculating
The database of machine equipment is for storing the data arrived used in settlement of insurance claim decision-making technique.The network interface of the computer equipment
For being communicated with external terminal by network connection.To realize a kind of settlement of insurance claim when the computer program is executed by processor
Decision-making technique.
In one embodiment, a kind of computer equipment is provided, including memory, processor and storage are on a memory
And the computer program that can be run on a processor, processor perform the steps of when executing computer program
Settlement of insurance claim request is obtained, the settlement of insurance claim request includes insurance policy information；
Policy information in the insurance policy information and preset database is subjected to consistency desired result；
If verification passes through, insurance is extracted from the insurance policy information and is in danger information, wherein the insurance is in danger
Packet includes strategical vantage point point, be in danger time and weather information of being in danger；
Using be in danger place and the time of being in danger as constraint condition, to meteorological data in preset meteorological data table
It is retrieved, obtains Target Weather Information；
Calculate the semantic similarity of the Target Weather Information Yu the weather information of being in danger；
It is if the semantic similarity is more than or equal to preset similarity threshold, the insurance policy information is corresponding
Insurance be used as wait settle a claim insurance.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program performs the steps of when being executed by processor
Settlement of insurance claim request is obtained, the settlement of insurance claim request includes insurance policy information；
Policy information in the insurance policy information and preset database is subjected to consistency desired result；
If verification passes through, insurance is extracted from the insurance policy information and is in danger information, wherein the insurance is in danger
Packet includes strategical vantage point point, be in danger time and weather information of being in danger；
Using be in danger place and the time of being in danger as constraint condition, to meteorological data in preset meteorological data table
It is retrieved, obtains Target Weather Information；
Calculate the semantic similarity of the Target Weather Information Yu the weather information of being in danger；
It is if the semantic similarity is more than or equal to preset similarity threshold, the insurance policy information is corresponding
Insurance be used as wait settle a claim insurance.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each function
Can unit, module division progress for example, in practical application, can according to need and by above-mentioned function distribution by different
Functional unit, module are completed, i.e., the internal structure of described device is divided into different functional unit or module, more than completing
The all or part of function of description.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although referring to aforementioned reality
Applying example, invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modified
Or replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution should all
It is included within protection scope of the present invention.
Claims (10)
1. a kind of settlement of insurance claim decision-making technique, which is characterized in that the settlement of insurance claim decision-making technique includes:
Settlement of insurance claim request is obtained, the settlement of insurance claim request includes insurance policy information；
Policy information in the insurance policy information and preset database is subjected to consistency desired result；
If verification passes through, insurance is extracted from the insurance policy information and is in danger information, wherein the insurance is in danger information
Including place of being in danger, be in danger time and weather information of being in danger；
Using be in danger place and the time of being in danger as constraint condition, meteorological data is carried out in preset meteorological data table
Retrieval, obtains Target Weather Information；
Calculate the semantic similarity of the Target Weather Information Yu the weather information of being in danger；
If the semantic similarity is more than or equal to preset similarity threshold, by the corresponding guarantor of the insurance policy information
Danger is as wait insurance of settling a claim.
2. settlement of insurance claim decision-making technique as described in claim 1, which is characterized in that described with the place and described of being in danger
Be in danger the time be constraint condition, meteorological data is retrieved in preset meteorological data table, obtain Target Weather Information it
Before, the settlement of insurance claim decision-making technique further include:
Meteorological data is obtained using crawler technology；
The meteorological data is handled into structuring, obtains targeted gas phase data；
The targeted gas phase data are saved using Object Relation Mapping frame, obtain meteorological data table.
3. settlement of insurance claim decision-making technique as claimed in claim 2, which is characterized in that described to obtain meteorological number using crawler technology
According to, comprising:
Obtain target webpage；
The information in the target webpage is extracted using preset regular expression, obtains target information；
The target information is parsed, the meteorological data is obtained.
4. settlement of insurance claim decision-making technique as claimed in claim 2, which is characterized in that it is described to the meteorological data into structuring
Processing, comprising:
According to the meteorological data, the corresponding tables of data knot of template generation meteorological data is generated according to preset data list structure
Structure；
The meteorological data is parsed using POI technology, obtains initial meteorological data；
The initial meteorological data is imported into the data list structure, the meteorological data table is obtained.
5. settlement of insurance claim decision-making technique as described in claim 1, which is characterized in that the Target Weather Information includes target base
Notebook data and targeted gas phase disaster data, the weather information of being in danger include be in danger master data and meteorological disaster data of being in danger；
It is described to calculate the targeted gas phase and be in danger the semantic similarity of weather information and the weather information of being in danger, comprising:
The semantic similarity for calculating the target master data Yu the master data of being in danger, as master data similarity；
The semantic similarity for calculating the targeted gas phase disaster data with meteorological disaster data of being in danger, as meteorological disaster data phase
Like degree；
The master data similarity is weighted with the meteorological disaster data similarity and is added, it is similar to obtain the semanteme
Degree.
6. settlement of insurance claim decision-making technique as described in claim 1, which is characterized in that described to calculate the Target Weather Information
After the semantic similarity of the weather information of being in danger, the settlement of insurance claim decision-making technique further include:
If the semantic similarity is less than preset similarity threshold, by the corresponding insurance policy information of the semantic similarity
Client is sent to be audited.
7. a kind of settlement of insurance claim decision-making technique device, which is characterized in that the settlement of insurance claim decision making device includes:
Policy information obtains module, and for obtaining settlement of insurance claim request, the settlement of insurance claim request includes insurance policy information；
Policy information verifies correction verification module, for carrying out the policy information in the insurance policy information and preset database
Consistency desired result；
Be in danger data obtaining module, if passing through for verifying, insurance is extracted from the insurance policy information and is in danger information,
Wherein, it is described insurance be in danger packet include strategical vantage point point, be in danger time and weather information of being in danger；
Weather information obtains module, for using be in danger place and the time of being in danger as constraint condition, in preset meteorology
Meteorological data is retrieved in tables of data, obtains Target Weather Information；
Similarity calculation module, for calculating the semantic similarity of the Target Weather Information Yu the weather information of being in danger；
Determining module is insured wait settle a claim, it, will if being more than or equal to preset similarity threshold for the semantic similarity
The corresponding insurance of the insurance policy information is as wait insurance of settling a claim.
8. settlement of insurance claim decision making device as claimed in claim 7, which is characterized in that the Target Weather Information includes target base
Notebook data and targeted gas phase disaster data, the weather information of being in danger include be in danger master data and meteorological disaster data of being in danger；
The similarity calculation module includes:
Master data similarity calculated, for calculating the semantic phase of the target master data with the master data of being in danger
Like degree, as master data similarity；
Meteorological disaster data similarity calculated, for calculating the targeted gas phase disaster data and meteorological disaster data of being in danger
Semantic similarity, as meteorological disaster data similarity；
Similarity calculated, for the master data similarity and the meteorological disaster data similarity to be weighted phase
Add, obtains the semantic similarity.
9. a kind of computer equipment, including memory, processor and storage are in the memory and can be in the processor
The computer program of upper operation, which is characterized in that the processor realized when executing the computer program as claim 1 to
Any one of 6 settlement of insurance claim decision-making techniques.
10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In realization settlement of insurance claim decision-making technique as described in any one of claim 1 to 6 when the computer program is executed by processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910043296.8A CN109857750A (en) | 2019-01-17 | 2019-01-17 | Settlement of insurance claim decision-making technique, device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910043296.8A CN109857750A (en) | 2019-01-17 | 2019-01-17 | Settlement of insurance claim decision-making technique, device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109857750A true CN109857750A (en) | 2019-06-07 |
Family
ID=66894979
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910043296.8A Pending CN109857750A (en) | 2019-01-17 | 2019-01-17 | Settlement of insurance claim decision-making technique, device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109857750A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113724094A (en) * | 2021-08-31 | 2021-11-30 | 平安养老保险股份有限公司 | Risk information checking method, device, equipment and medium |
| CN113919962A (en) * | 2021-10-21 | 2022-01-11 | 平安科技（深圳）有限公司 | Intelligent analysis method, device and equipment for insurance policy insurance and storage medium |
| CN114756638A (en) * | 2022-04-14 | 2022-07-15 | 中国银行保险信息技术管理有限公司 | Meteorological disaster information query report generation method and system for insurance application |
| US11449951B1 (en) | 2021-05-18 | 2022-09-20 | International Business Machines Corporation | Intelligent secure automation of claim preemptive subrogation |
| CN116307607A (en) * | 2023-03-24 | 2023-06-23 | 探保网络科技(广州)有限公司 | Insurance core system monitoring system and method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007103402A2 (en) * | 2006-03-08 | 2007-09-13 | Guy Carpenter & Company, Inc. | Spatial database system for generation of weather event and risk reports |
| CN103177041A (en) * | 2011-12-26 | 2013-06-26 | 北京四维图新科技股份有限公司 | Electronic map generation method, information publish method, road planning method and road planning device |
| CN108257028A (en) * | 2017-07-25 | 2018-07-06 | 平安科技（深圳）有限公司 | Processing method, device, computer equipment and the storage medium of settlement of insurance claim |
| WO2018201974A1 (en) * | 2017-05-05 | 2018-11-08 | 平安科技（深圳）有限公司 | Insurance claim settlement interaction method, system, server and storage medium |
| CN109035032A (en) * | 2018-06-11 | 2018-12-18 | 中国平安人寿保险股份有限公司 | Data structured processing method, device, computer equipment and storage medium |
-
2019
- 2019-01-17 CN CN201910043296.8A patent/CN109857750A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007103402A2 (en) * | 2006-03-08 | 2007-09-13 | Guy Carpenter & Company, Inc. | Spatial database system for generation of weather event and risk reports |
| CN103177041A (en) * | 2011-12-26 | 2013-06-26 | 北京四维图新科技股份有限公司 | Electronic map generation method, information publish method, road planning method and road planning device |
| WO2018201974A1 (en) * | 2017-05-05 | 2018-11-08 | 平安科技（深圳）有限公司 | Insurance claim settlement interaction method, system, server and storage medium |
| CN108257028A (en) * | 2017-07-25 | 2018-07-06 | 平安科技（深圳）有限公司 | Processing method, device, computer equipment and the storage medium of settlement of insurance claim |
| CN109035032A (en) * | 2018-06-11 | 2018-12-18 | 中国平安人寿保险股份有限公司 | Data structured processing method, device, computer equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 贾君枝;刘焘;李景峰;: "基于Web保险信息的语义分析初探", 现代图书情报技术, no. 03, 25 March 2006 (2006-03-25) * |
| 赵思健;张峭;聂谦;郑茗曦;: "农作物气象灾害风险识别与评估研究", 灾害学, no. 02, 20 April 2018 (2018-04-20) * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11449951B1 (en) | 2021-05-18 | 2022-09-20 | International Business Machines Corporation | Intelligent secure automation of claim preemptive subrogation |
| CN113724094A (en) * | 2021-08-31 | 2021-11-30 | 平安养老保险股份有限公司 | Risk information checking method, device, equipment and medium |
| CN113919962A (en) * | 2021-10-21 | 2022-01-11 | 平安科技（深圳）有限公司 | Intelligent analysis method, device and equipment for insurance policy insurance and storage medium |
| CN113919962B (en) * | 2021-10-21 | 2023-05-26 | 平安科技（深圳）有限公司 | Intelligent analysis method, device, equipment and storage medium for insurance policy |
| CN114756638A (en) * | 2022-04-14 | 2022-07-15 | 中国银行保险信息技术管理有限公司 | Meteorological disaster information query report generation method and system for insurance application |
| CN114756638B (en) * | 2022-04-14 | 2024-06-18 | 中国银行保险信息技术管理有限公司 | Meteorological disaster information query report generation method and system for insurance application |
| CN116307607A (en) * | 2023-03-24 | 2023-06-23 | 探保网络科技(广州)有限公司 | Insurance core system monitoring system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109857750A (en) | Settlement of insurance claim decision-making technique, device, computer equipment and storage medium | |
| US9329751B2 (en) | Method and a system to generate a user interface for analytical models | |
| CN112464117B (en) | Request processing method, apparatus, computer device and storage medium | |
| US9361656B2 (en) | Data mining and logic checking tools | |
| CN101459672B (en) | Webpage content authentication system and method | |
| KR102160612B1 (en) | System for providing closed platform based condition matching type realestate brokerage service | |
| CN106779278A (en) | The evaluation system of assets information and its treating method and apparatus of information | |
| CN112214456B (en) | House property data processing method and device and electronic equipment | |
| US10210151B2 (en) | Dynamic function builder | |
| CN110472895B (en) | Financial system wind control method and device, computer equipment and storage medium | |
| EP3360108A1 (en) | Managing technical process data | |
| CN112434216A (en) | Intelligent investment project recommendation method and device, storage medium and computer equipment | |
| Wiengarten et al. | Value creation through expanding the online distribution channel | |
| CN112861056A (en) | Enterprise website construction information display and release system and method | |
| CN104408062B (en) | Security file processing method and device | |
| US11783206B1 (en) | Method and system for making binary predictions for a subject using historical data obtained from multiple subjects | |
| CN110544165B (en) | Credit risk score card creating method and device and electronic equipment | |
| CN113569549A (en) | Report conversion processing method and device, computer equipment and readable storage medium | |
| WO2022089235A1 (en) | Product demonstration method and apparatus, computer device, and storage medium | |
| CN117813601A (en) | System and method for enabling relevant data to be extracted from multiple documents | |
| Yang et al. | Assessing qualitative similarities between financial reporting frameworks using visualization and rules: COREP vs. pillar 3 | |
| KR100888329B1 (en) | Real time automatic information extraction system and method using rules | |
| CN116228419A (en) | Information processing method, device, equipment and storage medium | |
| CN112016268B (en) | Online document processing method, device, computer equipment and readable storage medium | |
| CN113051257B (en) | Service data cleaning method and device |
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 |