CN112767178A - Survival state monitoring method and device, computer equipment and storage medium - Google Patents
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Abstract
The application belongs to the technical field of intelligent decision making, and provides a survival state monitoring method and device, computer equipment and a computer readable storage medium. This application is through the predetermined risk coefficient that acquires that user's survival state corresponds, judge whether predetermined risk coefficient is greater than or equal to predetermined risk coefficient threshold value, if predetermined risk coefficient is greater than or equal to predetermined risk coefficient threshold value, send the acquisition request of the survival state that the user corresponds to predetermined third party, so that predetermine the third party and return the survival state that the user corresponds according to the acquisition request, receive the survival state that the user that predetermines the third party and return corresponds, monitor the survival state to the user, thereby can select the user that needs to give birth to the transfer according to the predetermined risk coefficient that user's survival state corresponds, and acquire through authoritative predetermined third party the survival state of user can improve the efficiency of giving birth to the transfer to the user, can save the expense of giving birth to the transfer.
Description
Technical Field
The present application relates to the field of intelligent decision making technologies, in particular to the field of statistical analysis technologies, and in particular, to a survival state monitoring method, apparatus, computer device, and computer-readable storage medium.
Background
The survival status is called as life-style survey for short. For industries closely related to the survival state of people, the survival state of a stakeholder needs to be closely concerned so as to process business according to the survival state of the stakeholder. For example, in the insurance industry, since insurance business is closely related to the survival status of an insured person, the survival status of the insured person needs to be closely concerned, and the survival status of the insured person needs to be investigated, that is, the insured person needs to be brought into life. In the insurance industry, the current survival state of an insured person in the traditional technology is generally an offline visit survey of an operator, such as a survey of a client address and a client unit of the insured person), and generally the current survival state is not attended until the transfer is actually initiated. In the insurance industry, people's living state is investigated by offline visits, on one hand, offline investigation is relatively high in consumption of manpower and material resources, and is not timely and accurate, on the other hand, if the insured person is known to be self-caused in the process of transferring money, much payable data in a background is written in, and if the transfer is successful, the accounts are required to be recovered to a customer family offline, so that disputes are easy to cause, and generally difficult to recover, and if the transfer is not initiated, transfer interception and systematic recoiling of accounts payable and vouchers are required to be carried out in two departments of internal coordination business and finance of a company, so that the system flow is abnormal. Therefore, in the conventional technology, the efficiency of the generation and the adjustment of the insured person is low, and the resource loss is easy.
Disclosure of Invention
The application provides a survival state monitoring method and device, computer equipment and a computer readable storage medium, which can solve the problem of low survival state monitoring efficiency in the prior art.
In a first aspect, the present application provides a method for monitoring a survival status, including: acquiring a preset risk coefficient corresponding to the survival state of a user; judging whether the preset risk coefficient is greater than or equal to a preset risk coefficient threshold value or not; if the preset risk coefficient is larger than or equal to a preset risk coefficient threshold value, sending an acquisition request of the survival state corresponding to the user to a preset third party so that the preset third party returns the survival state corresponding to the user according to the acquisition request; and receiving the survival state corresponding to the user returned by the preset third party so as to monitor the survival state of the user.
In a second aspect, the present application further provides a survival status monitoring device, including: the first obtaining unit is used for obtaining a preset risk coefficient corresponding to the survival state of the user; the judging unit is used for judging whether the preset risk coefficient is larger than or equal to a preset risk coefficient threshold value or not; a sending unit, configured to send an acquisition request of a living state corresponding to the user to a preset third party if the preset risk coefficient is greater than or equal to a preset risk coefficient threshold, so that the preset third party returns the living state corresponding to the user according to the acquisition request; and the receiving unit is used for receiving the survival state corresponding to the user returned by the preset third party so as to monitor the survival state of the user.
In a third aspect, the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the survival status monitoring method when executing the computer program.
In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the steps of the survival status monitoring method.
The application provides a survival state monitoring method and device, computer equipment and a computer readable storage medium. The method comprises the steps of judging whether the preset risk coefficient is larger than or equal to a preset risk coefficient threshold value or not through the preset risk coefficient corresponding to the survival state of a user, if the preset risk coefficient is larger than or equal to the preset risk coefficient threshold value, sending an acquisition request of the survival state corresponding to the user to a preset third party so as to enable the preset third party to return the survival state corresponding to the user according to the acquisition request, receiving the survival state corresponding to the user returned by the preset third party, monitoring the survival state of the user, comparing with the prior art, carrying out living adjustment in a mode of walking through a line, screening out the user needing to be brought into life adjustment according to the preset risk coefficient corresponding to the survival state of the user in the embodiment of the application, and acquiring the survival state of the user through an authoritative preset third party, the accurate actual living state that the acquirement user that can be timely corresponds can improve the efficiency of living transferring the user to realize timely intelligent recognition high risk user's living state, realize intelligent living transferring (intelligence living state investigation promptly), simultaneously, need not to live transferring all user's living state, especially when living transferring through predetermineeing the third party, can be when reducing the data bulk of handling in order to improve living transferring efficiency, can save the expense of living transferring.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, 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 application, 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 flowchart of a survival status monitoring method according to an embodiment of the present disclosure;
fig. 2 is a schematic view of a first sub-flow of a survival status monitoring method according to an embodiment of the present application;
fig. 3 is a schematic view of a second sub-flow of a survival status monitoring method according to an embodiment of the present application;
fig. 4 is a schematic view of a third sub-flow of a survival status monitoring method according to an embodiment of the present application;
fig. 5 is a fourth sub-flowchart of a survival status monitoring method according to an embodiment of the present application;
fig. 6 is a schematic view of a fifth sub-flow of a survival status monitoring method according to an embodiment of the present application;
fig. 7 is a sixth sub-flowchart of a survival status monitoring method according to an embodiment of the present application;
fig. 8 is a schematic block diagram of a survival status monitoring apparatus according to an embodiment of the present application; and
fig. 9 is a schematic block diagram of a computer device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. 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 application.
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.
Referring to fig. 1, fig. 1 is a schematic flow chart of a survival status monitoring method according to an embodiment of the present disclosure. As shown in FIG. 1, the method includes the following steps S11-S15:
and S11, acquiring a preset risk coefficient corresponding to the survival state of the user.
Specifically, the risk coefficient corresponding to the survival state of the user may be determined according to the user conditions such as the age and occupation of the user. When the living state of the user is to be monitored, a preset risk coefficient corresponding to the living state of the user is obtained.
And S12, judging whether the preset risk coefficient is larger than or equal to a preset risk coefficient threshold value.
S13, if the preset risk coefficient is larger than or equal to the preset risk coefficient threshold value, sending the obtaining request of the survival state corresponding to the user to a preset third party, so that the preset third party returns the survival state corresponding to the user according to the obtaining request.
And S14, receiving the survival state corresponding to the user returned by the preset third party to monitor the survival state of the user.
And S15, if the preset risk coefficient is larger than or equal to the preset risk coefficient threshold value, not monitoring the survival state.
Specifically, for the risk coefficient corresponding to the living state of the user, a corresponding preset risk coefficient threshold is set according to actual requirements of different services. Judging whether the preset risk coefficient is larger than or equal to the preset risk coefficient threshold value or not according to the risk coefficient corresponding to the living state of the user and the preset risk coefficient threshold value so as to judge whether the living state of the user is verified or not, judging that the risk of the change of the living state of the user is small if the preset risk coefficient is smaller than the preset risk coefficient threshold value, judging that the living state of the user does not need to be verified, filtering out the user, not monitoring the living state of the user, judging that the risk of the change of the living state of the user is large if the preset risk coefficient is larger than or equal to the preset risk coefficient threshold value, judging that the living state of the user needs to be verified so as to confirm the accurate actual living state of the user, and sending the acquisition request of the living state corresponding to the user to a preset third party, the preset third party can be a public security system with user living state authority information, so that the preset third party verifies whether the living state corresponding to the user is a living state or a personal fault state according to the acquisition request, returns to the living state corresponding to the user, receives the living state corresponding to the user returned by the preset third party, and accordingly obtains the determination information that the living state of the user is the living state or the personal fault state, confirms whether the user is the living state or the personal fault state, and realizes the investigation of the living state of the user to monitor the living state of the user.
In the embodiment of the application, the preset risk coefficient corresponding to the survival state of the user is obtained, whether the preset risk coefficient is larger than or equal to the preset risk coefficient threshold value or not is judged, if the preset risk coefficient is larger than or equal to the preset risk coefficient threshold value, the obtaining request of the survival state corresponding to the user is sent to the preset third party, so that the preset third party returns the survival state corresponding to the user according to the obtaining request, the survival state corresponding to the user returned by the preset third party is received, the survival state of the user is monitored, compared with the prior art, the living condition is adjusted through a mode of visiting under a line, the embodiment of the application can screen out the user needing to be adjusted according to the preset risk coefficient corresponding to the survival state of the user, and the survival state of the user is obtained through the authoritative preset third party, the accurate actual living state that the acquirement user that can be timely corresponds can improve the efficiency of living transferring the user to realize timely intelligent recognition high risk user's living state, realize intelligent living transferring (intelligence living state investigation promptly), simultaneously, need not to live transferring all user's living state, especially when living transferring through predetermineeing the third party, can be when reducing the data bulk of handling in order to improve living transferring efficiency, can save the expense of living transferring.
Referring to fig. 2, fig. 2 is a schematic view of a first sub-flow of a survival status monitoring method according to an embodiment of the present application. As shown in fig. 2, in this embodiment, the step of obtaining the preset risk coefficient corresponding to the survival status of the user includes:
s21, acquiring a plurality of preset index risk coefficients corresponding to the user;
and S22, summing the preset index risk coefficients to obtain a preset risk coefficient corresponding to the survival state of the user.
Specifically, different indexes capable of reflecting the risk corresponding to the user survival state may be set according to different actual services. For example, in the insurance industry, because occupation and age have a large influence on the survival state of the user, occupation and age of the user can be used as preset indexes of the survival state risk of the user, and meanwhile, because in the insurance industry, the interest of the quota on the related party has a large influence, the quota of the user can also be used as a preset index of the survival state risk of the user, so that the preset indexes such as occupation, age and quota of the user are used as indexes for regulating the survival state of the user, and the preset index risk coefficient includes a preset occupation risk coefficient, a preset age risk coefficient and a preset quota risk coefficient. After a plurality of preset indexes of the user for dispatching are determined, for each preset index, according to different situations, and according to different risk degrees of each situation, a corresponding index risk coefficient is determined, so that after the index information of the user is obtained, an index risk coefficient corresponding to the index information of the user can be determined according to the index information of the user, so that when the user is dispatched, a plurality of preset index risk coefficients corresponding to the user can be obtained, for example, in an insurance business, after the age information of the user is obtained when the user is dispatched, a preset age risk coefficient corresponding to the age of the user can be determined according to the age of the user, after the occupation information of the user is obtained, a preset occupation risk coefficient corresponding to the occupation of the user can be determined according to the occupation of the user, and after the quota information of the user is obtained, according to the user's quota, the quota risk coefficient corresponding to the user's quota can be determined, wherein the preset index risk coefficient corresponding to each preset index can be specifically set according to the actual business requirements. Like this, when giving birth to the user, because the predetermined index of user can confirm, can confirm according to the predetermined index of user predetermine the corresponding predetermined index risk coefficient of index, according to preset, can acquire a plurality of predetermined index risk coefficients that the user corresponds, then will be a plurality of predetermined index risk coefficient sums, can obtain the predetermined risk coefficient that user's survival state corresponds. For example, in an insurance service, the risk index coefficient corresponding to the age of the user is a1, the risk index coefficient corresponding to the occupation of the user is a2, the risk index coefficient corresponding to the quota of the user is A3, and the preset risk coefficient corresponding to the living state of the user is a1+ a2+ A3.
Referring to fig. 3, fig. 3 is a second sub-flowchart of a survival status monitoring method according to an embodiment of the present disclosure. As shown in fig. 3, in this embodiment, the step of summing the preset index risk coefficients to obtain the preset risk coefficient corresponding to the survival state of the user includes:
s31, acquiring preset weights corresponding to a plurality of preset index risk coefficients corresponding to the user;
and S32, multiplying each preset index risk coefficient by the corresponding preset weight, and summing the multiplied results to obtain the preset risk coefficient corresponding to the survival state of the user.
Specifically, in order to represent the respective importance degrees of the plurality of predetermined indexes and control the total value of the preset risk coefficients, the respective importance degrees of the plurality of predetermined indexes may be represented by setting the respective preset weights for the plurality of preset index risk coefficients and the respective preset weights corresponding to the preset index risk coefficients. Therefore, when the preset risk coefficient corresponding to the living state of the user is obtained, the preset index risk coefficients corresponding to the user are obtained, the preset weights corresponding to the preset index risk coefficients corresponding to the user are obtained again, each preset index risk coefficient is multiplied by the corresponding preset weight, and the multiplied results are summed to obtain the preset risk coefficient corresponding to the living state of the user. For example, in an insurance service, the risk index coefficient corresponding to the age of the user is a1, the corresponding preset weight is a1, the risk index coefficient corresponding to the occupation of the user is a2, the corresponding preset weight is a2, the risk index coefficient corresponding to the quota of the user is A3, the corresponding preset weight is a,3, and the preset risk coefficient corresponding to the living state of the user is: a1 a1+ a2 a2+ A3 A3.
Referring to fig. 4, fig. 4 is a third sub-flow diagram of a survival status monitoring method according to an embodiment of the present disclosure. As shown in fig. 4, in this embodiment, before the step of obtaining a plurality of preset index risk coefficients corresponding to the user, the method further includes:
s41, acquiring preset index information corresponding to the preset index of the user;
and S42, acquiring a preset index risk coefficient corresponding to the preset index information according to the preset index information.
Specifically, for different preset index information in each preset index, the preset index information may be classified to classify the different preset index information into different classifications, and a corresponding preset index risk coefficient may be set for each classification, so that a preset index risk coefficient corresponding to the preset index information may be determined for each preset index information. For example, in an insurance business, when developing an automatic generation and adjustment model, the risk coefficients of the preset indexes corresponding to the occupation, age and insurance amount of the applicant are set as follows:
1) the accident risk coefficient is set according to the major category of the job of the insured person, the accident coefficient is high for high risk persons, and the accident system is low for low risk persons. For example, the following settings may be made: the risk coefficient of students or family employees is 1; secondly, general indoor logistics are carried out, and the risk system is 10; thirdly, the risk coefficients of the operators in the indoor toxic or harmful gas or other dangerous environments are 70, 60 and 65 in sequence; fourthly, outdoor business outwork, the risk coefficient is 30; the risk coefficient of the site operator is 70; sixthly, operating personnel in high-altitude, high-voltage, deep sea or other outdoor dangerous fields have a risk coefficient of 90.
2) The risk factor of the accident is set according to the age of the insured person, the risk factor is low before 65 years old, the risk factor is high above 80 years old, and the risk factor of the accident is medium between 65 and 80. The specific division criteria may be as follows: (ii) 0 to 3 years old, with a risk factor of 20; age 3 to 6 with a risk factor of 10; ③ 6 to 12 years old, with a risk factor of 5; ③ 12 to 18 years old, the risk factor is 10; 18 to 30 years old with a risk factor of 1; sixthly, the risk coefficient is 5 from 30 to 40 years old; seventhly, the age is 40 to 50, and the risk coefficient is 15; eighty (50) to 65 years old, the risk factor is 30; ninthly 65 to 70 years old, with a risk factor of 50; r 70 to 75 years old, with a risk factor of 70;
75 to 80 years old, with a risk factor of 90;
80 to 85 years old, with a risk factor of 95;
age 85 and above, and risk factor 100.
3) According to the height of the accident personal accident insurance premium, a risk coefficient is set, the premium is high, the accident coefficient is high, and the accident coefficient is low. For example, the following settings may be made: the insurance sum is less than 50 ten thousand, and the risk coefficient is 0; the guarantee is less than 100 ten thousand, and the risk coefficient is 10; ③ the guarantee is less than 200 ten thousand, and the risk coefficient is 30; the reserve is below 500 ten thousand, and the risk coefficient is 50; the guarantee is below 1000 ten thousand, and the risk coefficient is 70; sixthly, the guarantee amount is more than 1000 ten thousand, and the risk coefficient is 90.
When the preset index information corresponding to the preset index of the user is obtained, the preset index risk coefficient corresponding to the preset index information can be obtained according to the preset index information. For example, when the age information corresponding to the age of the user is obtained as 66 years old, the age risk coefficient corresponding to the age is 50, when the occupation information corresponding to the occupation of the user is indoor attendance, the occupation risk coefficient corresponding to the occupation is 10, when the quota information corresponding to the quota of the user is obtained as 100 ten thousand or less, the quota risk coefficient corresponding to the quota is 10, and the age risk coefficient of the user is 50, the occupation risk coefficient is 10, and the quota risk coefficient is 10, the risk coefficient corresponding to the living state of the user is 70.
Referring to fig. 5, fig. 5 is a fourth sub-flow diagram of a survival status monitoring method according to an embodiment of the present disclosure. As shown in fig. 5, in this embodiment, if the preset risk coefficient is greater than or equal to a preset risk coefficient threshold, the step of sending the request for acquiring the living state corresponding to the user to a preset third party includes:
s51, if the preset risk coefficient is larger than or equal to a preset risk coefficient threshold, acquiring a preset acquisition request sending frequency corresponding to the preset risk coefficient threshold;
and S52, sending the acquisition request of the survival state corresponding to the user to a preset third party according to the preset acquisition request sending frequency.
Specifically, for different preset risk coefficient thresholds, the obtaining request of the living state corresponding to the user may be sent to a preset third party according to different preset obtaining request sending frequencies, so that when the preset risk coefficient is greater than or equal to the preset risk coefficient threshold, the preset obtaining request sending frequency corresponding to the preset risk coefficient threshold is obtained, and the obtaining request of the living state corresponding to the user is sent to the preset third party according to the preset obtaining request sending frequency, wherein the preset risk coefficient threshold is in direct proportion to the preset obtaining request sending frequency, that is, the larger the preset risk coefficient threshold is, the higher the risk of the living state change of the user is, the higher the preset obtaining request sending frequency is, that is, the shorter the preset period of the preset obtaining request sending is, so that the living state change of the user can be monitored in time, the smaller the preset risk system threshold value is, the smaller the change risk of the living state of the user is, the lower the preset acquisition request sending frequency is, namely, the preset period for sending the preset acquisition request can be relatively prolonged, so that the living state change of the user can be timely monitored, the monitoring efficiency of the living state of the user is improved, and the monitoring cost such as monitoring cost can be saved. For example, in the insurance service, if the life state of the user is monitored by using the age, occupation and insurance amount of the applicant as the preset risk coefficients corresponding to the life state of the user, if the preset risk coefficients corresponding to the age, occupation and insurance amount of the applicant reach the early warning indexes, the vital dispatching monitoring system can be automatically docked with the public security system of the third party to acquire the life state of the insured person, and the preset acquiring request sending frequency can be as follows: the preset risk coefficient threshold value is 60, the preset risk coefficient corresponding to the survival state of the user is greater than or equal to 60, and the preset acquiring request sending frequency can be adjusted once a year; the preset risk coefficient threshold value is 70, the preset risk coefficient corresponding to the survival state of the user is greater than or equal to 70, and the preset acquiring request sending frequency can be adjusted once every half year; the preset risk coefficient threshold is 80, the preset risk coefficient corresponding to the survival state of the user is greater than or equal to 80, and the preset obtaining request sending frequency can be adjusted once every month.
Referring to fig. 6, fig. 6 is a fifth sub-flowchart of a survival status monitoring method according to an embodiment of the present application. As shown in fig. 6, in this embodiment, the step of sending the obtaining request of the living state corresponding to the user to a preset third party according to the preset obtaining request sending frequency includes:
s61, acquiring all preset acquiring request sending frequencies corresponding to the preset risk coefficients;
s62, acquiring the maximum preset acquisition request transmitting frequency contained in all the preset acquisition request transmitting frequencies;
and S63, sending the acquisition request of the survival state corresponding to the user to a preset third party according to the maximum preset acquisition request sending frequency.
Specifically, to the situation that has different preset risk coefficient threshold values, if preset risk coefficient that user's survival state corresponds is greater than a plurality of preset risk coefficient threshold values, can produce preset risk coefficient corresponds a plurality of the condition of presetting acquisition request sending frequency, to this kind of condition, in order to improve user's living tone efficiency, realize the in time monitoring to user's survival state, can acquire all preset acquisition request sending frequencies that preset risk coefficient corresponds acquire all the biggest preset acquisition request sending frequency that contains in the preset acquisition request sending frequency, according to the biggest preset acquisition request sending frequency will the acquisition request of the survival state that the user corresponds sends to preset third party. For example, in an insurance service, if the age, occupation and allowance of an applicant are taken as preset risk coefficients corresponding to the survival state of a user to monitor the survival state of the user, if the preset risk coefficients corresponding to the age, occupation and allowance of the applicant are 90, preset risk coefficient thresholds for generating and dispatching early warning are 60, 70 and 80, wherein the preset risk coefficient threshold is 60, the preset acquiring request sending frequency can be generated and dispatched once a year, the preset risk coefficient threshold is 70, and the preset acquiring request sending frequency can be generated and dispatched once every half a year; the preset risk coefficient threshold is 80, the preset obtaining request sending frequency can be adjusted once per month, and since the preset risk coefficient 90 corresponding to the living state of the user is greater than 60, and is also greater than 70 and 80, the preset obtaining request sending frequency is adjusted once per month.
Referring to fig. 7, fig. 7 is a sixth sub-flowchart illustrating a survival status monitoring method according to an embodiment of the present application. As shown in fig. 7, in this embodiment, before the step of obtaining the preset risk coefficient corresponding to the survival status of the user, the method further includes:
s71, traversing all users contained in the preset user database;
s72, taking the user with the survival state marked as the survival state as a target user, and executing the step of obtaining the preset risk coefficient corresponding to the survival state of the user on the target user.
Specifically, since the preset user database includes users whose survival states are survival states and users whose survival states are fault states, and the users whose fault states do not need to be regenerated, when monitoring the survival states of the users, all the users included in the preset user database can be traversed,
the user who will the survival state mark of user is survival state is as the target user to select the target user who marks as survival state, only right the target user carries out obtain the step of presetting the risk factor that user's survival state corresponds, thereby only to carry out the survival state monitoring to the target user can, can avoid the waste of unnecessary monitoring resource, improve the monitoring efficiency to user survival state.
In an embodiment, after the step of receiving the survival status corresponding to the user returned by the preset third party, the method further includes:
judging whether the survival state corresponding to the user returned by the preset third party is a self-failure state or not;
and if the survival state corresponding to the user returned by the preset third party is the self-accident state, updating the survival state corresponding to the user to be the self-accident state, and storing the dispatching time corresponding to dispatching and returning the self-accident time corresponding to the user.
Specifically, according to the survival state corresponding to the user returned by the preset third party, for example, according to the survival state corresponding to the user returned by the public security system, whether the survival state corresponding to the user returned by the preset third party is the self-fault state is judged, if the survival state corresponding to the user returned by the preset third party is not the self-fault state, namely if the user is currently still in the survival state, the current live time is recorded, the current processing is finished, if the user is not currently in the survival state and is the self-fault state, the current live time and the returned self-fault time are recorded, the current live time and the returned self-fault time are updated into the client system, the client survival state is updated into the self-fault state, and the task is finished so as to monitor the survival state of the user.
In an embodiment, after the steps of updating the survival status corresponding to the user to the self-condition status, storing the generation time corresponding to the generation and dispatch, and returning the self-condition time corresponding to the user, if the survival status corresponding to the user returned by the preset third party is the self-condition status, the method further includes:
and sending the survival state corresponding to the user as a self-fault state to a preset target person so that the target person can manually verify the survival state of the user.
Specifically, after receiving the latest survival status of the user and turning the latest survival status to the casualty status, the latest survival status corresponding to the user may be sent to the preset target person, and the content sent to the preset target person may further include a birth control time corresponding to the birth control and a return time corresponding to the user, so that the target person performs manual verification on the survival status of the user, for example, in the insurance industry, a manual verification task may be pushed to an exhibition worker of the user, and the exhibition worker verifies a feedback to ensure the authenticity and accuracy of the survival status corresponding to the user, thereby avoiding other disputes caused by a fault occurring in the process of automatically performing intelligent birth control.
In an embodiment, after the step of sending the survival status corresponding to the user as a self-fault status to a preset target person so that the target person performs manual verification on the survival status of the user, the method further includes:
and if the survival state corresponding to the user is determined to be the self-fault state according to the manual verification, migrating the preset user data corresponding to the user to a preset data backup library, and deleting the preset user data from a preset data formal library.
Specifically, if it is determined that the survival state corresponding to the user is really the self-owned state according to the manual verification, the preset user data corresponding to the user may be migrated to a preset data backup library, and the preset user data may be deleted from a preset data formal library. For example, in the insurance industry, manual verification is generally performed by exhibition personnel, if the feedback is that the user really is the result of the manual verification performed by the exhibition personnel on the living state of the user, the data of the user in each business system can be migrated to the preset data backup library except the related data which still can enjoy the interests for the user, so that the data of the user can be sealed and saved for the user, the data volume of the preset data formal library is reduced, the data of the system can be simplified, and the performance of the system can be improved.
It should be noted that, the survival status monitoring method described in each of the above embodiments may recombine the technical features included in different embodiments as needed to obtain a combined implementation, but all of them are within the protection scope claimed in the present application.
Referring to fig. 8, fig. 8 is a schematic block diagram of a survival status monitoring apparatus according to an embodiment of the present disclosure. Corresponding to the survival state monitoring method, the embodiment of the application also provides a survival state monitoring device. As shown in fig. 8, the survival state monitoring apparatus includes a unit for executing the survival state monitoring method described above, and the survival state monitoring apparatus may be configured in a computer device. Specifically, referring to fig. 8, the survival status monitoring apparatus 80 includes a first obtaining unit 81, a determining unit 82, a sending unit 83 and a receiving unit 84.
The first obtaining unit 81 is configured to obtain a preset risk coefficient corresponding to a living state of a user;
a determining unit 82, configured to determine whether the preset risk coefficient is greater than or equal to a preset risk coefficient threshold;
a sending unit 83, configured to send an obtaining request of a living state corresponding to the user to a preset third party if the preset risk coefficient is greater than or equal to a preset risk coefficient threshold, so that the preset third party returns the living state corresponding to the user according to the obtaining request;
a receiving unit 84, configured to receive the survival status corresponding to the user returned by the preset third party, so as to monitor the survival status of the user.
In an embodiment, the first obtaining unit 81 includes:
the first obtaining subunit is configured to obtain a plurality of preset index risk coefficients corresponding to the user;
and the summation subunit is used for summing the preset index risk coefficients to obtain the preset risk coefficient corresponding to the living state of the user.
In an embodiment, the summing subunit comprises:
the second obtaining subunit is configured to obtain preset weights corresponding to a plurality of preset index risk coefficients corresponding to the user;
and the multiplication subunit is used for multiplying each preset index risk coefficient by the corresponding preset weight, and then summing the multiplied results to obtain the preset risk coefficient corresponding to the survival state of the user.
In an embodiment, the first obtaining unit 81 further includes:
the second obtaining subunit is configured to obtain preset index information corresponding to a preset index of the user;
and the third obtaining subunit is configured to obtain, according to the preset index information, a preset index risk coefficient corresponding to the preset index information.
In one embodiment, the sending unit 83 includes:
a fourth obtaining subunit, configured to obtain, if the preset risk coefficient is greater than or equal to a preset risk coefficient threshold, a preset obtaining request sending frequency corresponding to the preset risk coefficient threshold;
and the first sending subunit is used for sending the acquisition request of the living state corresponding to the user to a preset third party according to the preset acquisition request sending frequency.
In one embodiment, the first transmitting subunit includes:
a fifth obtaining subunit, configured to obtain all preset obtaining request sending frequencies corresponding to the preset risk coefficients;
a sixth obtaining subunit, configured to obtain a maximum preset obtaining request sending frequency included in all the preset obtaining request sending frequencies;
and the second sending subunit is used for sending the obtaining request of the survival state corresponding to the user to a preset third party according to the maximum preset obtaining request sending frequency.
In one embodiment, the survival status monitoring device 80 further comprises:
the traversing unit is used for traversing all users contained in the preset user database;
and the execution unit is used for taking the user with the survival state marked as the survival state as a target user so as to execute the step of acquiring the preset risk coefficient corresponding to the survival state of the user for the target user.
It should be noted that, as can be clearly understood by those skilled in the art, the detailed implementation process of the survival status monitoring apparatus and each unit may refer to the corresponding description in the foregoing method embodiment, and for convenience and brevity of description, no further description is provided herein.
Meanwhile, the division and connection modes of the units in the survival state monitoring device are only used for illustration, in other embodiments, the survival state monitoring device may be divided into different units as required, and the units in the survival state monitoring device may also adopt different connection sequences and modes to complete all or part of the functions of the survival state monitoring device.
The above-mentioned state of life monitoring apparatus may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 9.
Referring to fig. 9, fig. 9 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a computer device such as a desktop computer or a server, or may be a component or part of another device.
Referring to fig. 9, the computer device 500 includes a processor 502, a memory, which may include a non-volatile storage medium 503 and an internal memory 504, which may also be a volatile storage medium, and a network interface 505 connected by a system bus 501.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, causes the processor 502 to perform the steps of the method for monitoring survival status as described above.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the operation of 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 perform a method for monitoring a survival status as described above.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 9 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computer device 500 to which the present application may be applied, and that a particular computer device 500 may include more or less components than those shown, or may combine certain components, or have 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. 9, and are not described herein again.
Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to implement the following steps: acquiring a preset risk coefficient corresponding to the survival state of a user; judging whether the preset risk coefficient is greater than or equal to a preset risk coefficient threshold value or not; if the preset risk coefficient is larger than or equal to a preset risk coefficient threshold value, sending an acquisition request of the survival state corresponding to the user to a preset third party so that the preset third party returns the survival state corresponding to the user according to the acquisition request; and receiving the survival state corresponding to the user returned by the preset third party so as to monitor the survival state of the user.
In an embodiment, when the processor 502 implements the step of obtaining the preset risk coefficient corresponding to the survival state of the user, the following steps are specifically implemented:
acquiring a plurality of preset index risk coefficients corresponding to the user;
and summing the preset index risk coefficients to obtain a preset risk coefficient corresponding to the living state of the user.
In an embodiment, when the processor 502 performs the step of summing the plurality of preset index risk coefficients to obtain the preset risk coefficient corresponding to the living state of the user, the following steps are specifically performed:
acquiring preset weights corresponding to a plurality of preset index risk coefficients corresponding to the user;
and multiplying each preset index risk coefficient by the corresponding preset weight, and summing the multiplied results to obtain the preset risk coefficient corresponding to the living state of the user.
In an embodiment, before implementing the step of obtaining a plurality of preset index risk coefficients corresponding to the user, the processor 502 further implements the following steps:
acquiring preset index information corresponding to preset indexes of the user;
and acquiring a preset index risk coefficient corresponding to the preset index information according to the preset index information.
In an embodiment, when the processor 502 performs the step of sending the request for acquiring the living state corresponding to the user to a preset third party if the preset risk coefficient is greater than or equal to the preset risk coefficient threshold, the following steps are specifically performed:
if the preset risk coefficient is greater than or equal to a preset risk coefficient threshold, acquiring a preset acquisition request sending frequency corresponding to the preset risk coefficient threshold;
and sending the acquisition request of the living state corresponding to the user to a preset third party according to the preset acquisition request sending frequency.
In an embodiment, when the processor 502 performs the step of sending the obtaining request of the living state corresponding to the user to a preset third party according to the preset obtaining request sending frequency, the following steps are specifically performed:
acquiring all preset acquiring request sending frequencies corresponding to the preset risk coefficients;
acquiring the maximum preset acquisition request sending frequency contained in all the preset acquisition request sending frequencies;
and sending the acquisition request of the living state corresponding to the user to a preset third party according to the maximum preset acquisition request sending frequency.
In an embodiment, before the step of obtaining the preset risk coefficient corresponding to the survival status of the user, the processor 502 further implements the following steps:
traversing all users contained in a preset user database;
and taking the user with the survival state marked as the survival state as a target user, and executing the step of acquiring the preset risk coefficient corresponding to the survival state of the user for the target user.
It should be understood that in the embodiment of the present Application, 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.
It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiments may be implemented by a computer program, and the computer program may be stored in a computer readable storage medium. The computer program is executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present application also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, the computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the steps of:
a computer program product which, when run on a computer, causes the computer to perform the steps of the state-of-life monitoring method described in the embodiments above.
The computer readable storage medium may be an internal storage unit of the aforementioned device, such as a hard disk or a memory of the device. The computer readable storage medium may also be an external storage device of the device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the device. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the apparatus.
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.
The storage medium is an entity and non-transitory storage medium, and may be various entity storage media capable of storing computer programs, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a magnetic disk, or an optical disk.
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 application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the application can be combined, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present application 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, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing an electronic device (which may be a personal computer, a terminal, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of survival monitoring, comprising:
acquiring a preset risk coefficient corresponding to the survival state of a user;
judging whether the preset risk coefficient is greater than or equal to a preset risk coefficient threshold value or not;
if the preset risk coefficient is larger than or equal to a preset risk coefficient threshold value, sending an acquisition request of the survival state corresponding to the user to a preset third party so that the preset third party returns the survival state corresponding to the user according to the acquisition request;
and receiving the survival state corresponding to the user returned by the preset third party so as to monitor the survival state of the user.
2. The method for monitoring survival status according to claim 1, wherein the step of obtaining the preset risk coefficient corresponding to the survival status of the user comprises:
acquiring a plurality of preset index risk coefficients corresponding to the user;
and summing the preset index risk coefficients to obtain a preset risk coefficient corresponding to the living state of the user.
3. The survival state monitoring method according to claim 2, wherein the step of summing the plurality of preset index risk coefficients to obtain the preset risk coefficient corresponding to the survival state of the user comprises:
acquiring preset weights corresponding to a plurality of preset index risk coefficients corresponding to the user;
and multiplying each preset index risk coefficient by the corresponding preset weight, and summing the multiplied results to obtain the preset risk coefficient corresponding to the living state of the user.
4. The survival state monitoring method according to claim 2, wherein before the step of obtaining the plurality of preset index risk coefficients corresponding to the user, the method further comprises:
acquiring preset index information corresponding to preset indexes of the user;
and acquiring a preset index risk coefficient corresponding to the preset index information according to the preset index information.
5. The method for monitoring survival status according to claim 1, wherein the step of sending the request for obtaining the survival status corresponding to the user to a preset third party if the preset risk coefficient is greater than or equal to a preset risk coefficient threshold value comprises:
if the preset risk coefficient is greater than or equal to a preset risk coefficient threshold, acquiring a preset acquisition request sending frequency corresponding to the preset risk coefficient threshold;
and sending the acquisition request of the living state corresponding to the user to a preset third party according to the preset acquisition request sending frequency.
6. The method for monitoring survival status according to claim 5, wherein the step of sending the acquisition request of the survival status corresponding to the user to a preset third party according to the preset acquisition request sending frequency comprises:
acquiring all preset acquiring request sending frequencies corresponding to the preset risk coefficients;
acquiring the maximum preset acquisition request sending frequency contained in all the preset acquisition request sending frequencies;
and sending the acquisition request of the living state corresponding to the user to a preset third party according to the maximum preset acquisition request sending frequency.
7. The method for monitoring survival status according to claim 1, wherein before the step of obtaining the preset risk coefficient corresponding to the survival status of the user, the method further comprises:
traversing all users contained in a preset user database;
and taking the user with the survival state marked as the survival state as a target user, and executing the step of acquiring the preset risk coefficient corresponding to the survival state of the user for the target user.
8. A state of life monitoring device, comprising:
the first obtaining unit is used for obtaining a preset risk coefficient corresponding to the survival state of the user;
the judging unit is used for judging whether the preset risk coefficient is larger than or equal to a preset risk coefficient threshold value or not;
a sending unit, configured to send an acquisition request of a living state corresponding to the user to a preset third party if the preset risk coefficient is greater than or equal to a preset risk coefficient threshold, so that the preset third party returns the living state corresponding to the user according to the acquisition request;
and the receiving unit is used for receiving the survival state corresponding to the user returned by the preset third party so as to monitor the survival state of the user.
9. A computer device, comprising a memory and a processor coupled to the memory; the memory is used for storing a computer program; the processor is adapted to run the computer program to perform the steps of the method according to any of claims 1-7.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when being executed by a processor, realizes the steps of the method according to any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113434562A (en) * | 2021-06-30 | 2021-09-24 | 平安养老保险股份有限公司 | Survival investigation screening method and device, computer equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120109765A1 (en) * | 2010-08-31 | 2012-05-03 | CEA Overseas LLC | International e-commerce system |
| CN106897402A (en) * | 2017-02-13 | 2017-06-27 | 山大地纬软件股份有限公司 | The method and user's portrait maker of user's portrait are built based on social security data |
| CN109785149A (en) * | 2018-12-13 | 2019-05-21 | 中国平安人寿保险股份有限公司 | Ensure analytical equipment, method and the storage medium of data |
| CN110084455A (en) * | 2018-01-26 | 2019-08-02 | 阿里巴巴集团控股有限公司 | A kind of data processing method, apparatus and system |
| US20190272915A1 (en) * | 2018-02-22 | 2019-09-05 | Ivan Keith Tolbert | System and methods for alternate-path access to medicare advance care planning education and conversation benefits on-demand by non-patients within home and other non-medical community settings |
| CN111062820A (en) * | 2019-11-29 | 2020-04-24 | 泰康保险集团股份有限公司 | Method and device for processing claim settlement service, block chain node and storage medium |
| CN111985703A (en) * | 2020-08-12 | 2020-11-24 | 支付宝(杭州)信息技术有限公司 | User identity state prediction method, device and equipment |
-
2020
- 2020-12-31 CN CN202011627015.2A patent/CN112767178A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120109765A1 (en) * | 2010-08-31 | 2012-05-03 | CEA Overseas LLC | International e-commerce system |
| US20150339760A1 (en) * | 2010-08-31 | 2015-11-26 | CEA Overseas LLC | International e-commerce system |
| CN106897402A (en) * | 2017-02-13 | 2017-06-27 | 山大地纬软件股份有限公司 | The method and user's portrait maker of user's portrait are built based on social security data |
| CN110084455A (en) * | 2018-01-26 | 2019-08-02 | 阿里巴巴集团控股有限公司 | A kind of data processing method, apparatus and system |
| US20190272915A1 (en) * | 2018-02-22 | 2019-09-05 | Ivan Keith Tolbert | System and methods for alternate-path access to medicare advance care planning education and conversation benefits on-demand by non-patients within home and other non-medical community settings |
| CN109785149A (en) * | 2018-12-13 | 2019-05-21 | 中国平安人寿保险股份有限公司 | Ensure analytical equipment, method and the storage medium of data |
| CN111062820A (en) * | 2019-11-29 | 2020-04-24 | 泰康保险集团股份有限公司 | Method and device for processing claim settlement service, block chain node and storage medium |
| CN111985703A (en) * | 2020-08-12 | 2020-11-24 | 支付宝(杭州)信息技术有限公司 | User identity state prediction method, device and equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113434562A (en) * | 2021-06-30 | 2021-09-24 | 平安养老保险股份有限公司 | Survival investigation screening method and device, computer equipment and storage medium |
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