CN117312583B - House user data auxiliary management method and system - Google Patents
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Abstract
The invention discloses a house user data auxiliary management method and a system, which relate to the technical field of intelligent management, wherein the method comprises the following steps: performing evaluation operation on the preprocessed user data by using an evaluation processing module to obtain a user data evaluation result; acquiring a housing planning list, performing task segmentation optimization on the housing planning list, and determining a task execution sequence; and constructing a user screening module by using the user data evaluation result and the task execution sequence, carrying out task screening matching on the user, identifying the user data according to the task screening matching result, and constructing auxiliary management information for feedback. The invention solves the technical problems of large manual repeated screening workload and low working efficiency in the prior art due to huge data volume of housing user data, and achieves the technical effects of performing auxiliary screening of user data, reducing the manual screening workload in the early stage and improving the data management efficiency through a computer technology.
Description
Technical Field
The invention relates to the technical field of intelligent management, in particular to a housing user data auxiliary management method and system.
Background
Along with the continuous acceleration of urban treatment, the population is more and more concentrated, the demand of urban housing is also continuously increased, and before housing application, the applicant needs to submit related data, and staff is used for screening various user information, wherein a large amount of screening contents with strong repeatability and mechanization are not consumed, and the number of screening data is huge, so that the screening workload in the early stage is large, and the efficiency of data screening work is influenced.
Disclosure of Invention
The application provides a house user data auxiliary management method and system, which are used for solving the technical problems of large manual repeated screening workload and low working efficiency in the prior art due to huge data volume of house user data.
In a first aspect of the present application, there is provided a method for assisted management of housing subscriber profiles, the method comprising: constructing a user parameter evaluation library, wherein the user parameter evaluation library comprises multidimensional evaluation parameters; acquiring user data and preprocessing the user data; setting an evaluation rule, and constructing an evaluation processing module based on the evaluation rule and a user parameter evaluation library; performing evaluation operation on the preprocessed user data by using the evaluation processing module to obtain a user data evaluation result; acquiring a housing planning list, performing task segmentation optimization on the housing planning list, and determining a task execution sequence; and constructing a user screening module by using the user data evaluation result and the task execution sequence, carrying out task screening matching on a user, identifying the user data according to the task screening matching result, and constructing auxiliary management information for feedback.
In a second aspect of the present application, there is provided a home subscriber profile auxiliary management system, the system comprising: the system comprises a user parameter evaluation library construction module, a user parameter evaluation library generation module and a user parameter evaluation library generation module, wherein the user parameter evaluation library construction module is used for constructing a user parameter evaluation library, and the user parameter evaluation library comprises multidimensional evaluation parameters; the user data preprocessing module is used for acquiring user data and preprocessing the user data; the evaluation processing module construction module is used for setting evaluation rules and constructing an evaluation processing module based on the evaluation rules and a user parameter evaluation library; the user data evaluation result acquisition module is used for carrying out evaluation operation on the preprocessed user data by utilizing the evaluation processing module to obtain a user data evaluation result; the system comprises a task execution sequence determining module, a task execution sequence determining module and a task processing module, wherein the task execution sequence determining module is used for acquiring a housing planning list, performing task segmentation optimization on the housing planning list and determining a task execution sequence; and the management execution information feedback module is used for constructing a user screening module by utilizing the user data evaluation result and the task execution sequence, carrying out task screening matching on a user, identifying the user data according to the task screening matching result, and constructing auxiliary management information for feedback.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The application provides a house user data auxiliary management method, which relates to the technical field of intelligent management, and comprises the steps of constructing a user parameter evaluation library, setting an evaluation rule, constructing an evaluation processing module by combining the user parameter evaluation library, and performing evaluation operation on preprocessed user data by using the evaluation processing module to obtain a user data evaluation result; acquiring a housing planning list, performing task segmentation optimization on the housing planning list, determining a task execution sequence, finally constructing a user screening module by utilizing a user data evaluation result and the task execution sequence, performing task screening matching on a user, identifying the user data according to the task screening matching result, and constructing auxiliary management information for feedback, thereby solving the technical problems of large manual repeatability screening workload and low working efficiency in the prior art caused by huge housing user data volume, realizing auxiliary screening of the user data by a computer technology, reducing the manual screening workload in the early stage and improving the data management efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for assisted management of housing user data according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of determining a task execution sequence in a method for assisting in managing user data of a housing according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a user screening module constructed in a method for assisting in managing user data of a housing according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a system for assisting in managing user data of a house according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a user parameter evaluation library construction module 11, a user data preprocessing module 12, an evaluation processing module construction module 13, a user data evaluation result acquisition module 14, a task execution sequence determination module 15 and a management execution information feedback module 16.
Detailed Description
The application provides a house user data auxiliary management method which is used for solving the technical problems of large manual repeated screening workload and low working efficiency in the prior art due to huge data volume of house user data.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above-described drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Embodiment one:
As shown in fig. 1, the present application provides a method for assisting in managing housing user data, the method comprising:
P10: constructing a user parameter evaluation library, wherein the user parameter evaluation library comprises multidimensional evaluation parameters;
further, step P10 of the embodiment of the present application further includes:
p11: acquiring a housing audit requirement, extracting parameters of the housing audit requirement, and determining an audit evaluation parameter set;
P12: carrying out parameter influence analysis on the auditing evaluation parameter set, and determining each parameter influence coefficient;
P13: acquiring an association parameter list, performing association calculation by using the association parameter list and the auditing evaluation parameter set, determining association parameters of the auditing evaluation parameters, and setting association evaluation coefficients of the association parameters based on an association calculation result;
P14: and establishing a mapping relation among the auditing evaluation parameters, the parameter influence coefficients, the association parameters and the association evaluation coefficients, and constructing the user parameter evaluation library.
It should be understood that, acquiring a housing audit requirement of a target area, extracting parameters including household income, property, housing condition and the like of an applicant as an audit evaluation parameter set, and respectively performing parameter influence analysis on each parameter in the audit evaluation parameter set, namely analyzing the influence of each audit parameter on the qualification of the applied housing, and determining each parameter influence coefficient, for example, the household condition, the income condition and the parameter influence coefficient of the existing housing condition are larger.
Further, according to the characteristics of each audit evaluation parameter, a correlation parameter list is obtained, wherein the correlation parameter list is a list of parameter components which are related to the audit evaluation parameter and can carry out auxiliary evaluation on the audit evaluation parameter, such as the tax amount and daily expenditure of the applicant, and the correlation calculation is carried out by utilizing the correlation parameter list and the audit evaluation parameter set, namely, the correlation parameter of each audit evaluation parameter is determined by calculating the correlation of each correlation parameter and the audit evaluation parameter, and the correlation evaluation coefficient of each correlation parameter is set based on the correlation calculation result.
Further, a mapping relation between each auditing evaluation parameter and the corresponding parameter influence coefficient, associated parameter and associated evaluation coefficient is established, and the user parameter evaluation library is constructed according to the mapping relation, wherein the user parameter evaluation library comprises multi-dimensional evaluation parameters, namely, the auditing evaluation parameters with multiple dimensions, and can be used as screening basis of housing user data.
P20: acquiring user data and preprocessing the user data;
further, step P20 of the embodiment of the present application further includes:
P21: identifying and classifying the user data to determine a data classification cluster;
P22: matching preprocessing strategies based on the data classification clusters, wherein the preprocessing strategies comprise format preprocessing and authentication preprocessing;
p23: and calling a corresponding execution module by utilizing the matching preprocessing strategy to preprocess the user data.
Optionally, acquiring housing application data of the target user, that is, the user data, identifying and classifying the user data, wherein the classifying basis can be classifying according to audit evaluation parameters contained in the data, determining a data classification cluster, further, constructing a data processing method library of various types of audit data in advance according to historical housing data audit data, and matching corresponding preprocessing strategies for each type of data in the data classification cluster from the data processing method library, wherein the preprocessing strategies comprise format preprocessing and authentication preprocessing, the format preprocessing is conversion of data formats, such as conversion of handwriting data into electronic version, conversion of different fonts into standard fonts, adjustment of photo size and the like, and the authentication preprocessing is anti-counterfeiting authentication, electronic authentication, such as two-dimensional code, bar code authentication and the like, on the data.
Further, the matching preprocessing strategy is utilized to call the corresponding execution module to preprocess the user data, for example, the matching preprocessing strategy is utilized to call the corresponding picture compression module to compress the user picture, the document conversion module is called to perform format conversion on text data, the anti-counterfeiting authentication module is called to verify the data, and the like, so that the purpose of preliminary processing of the user basic data is achieved.
Further, step P21 of the embodiment of the present application further includes:
p21-1: carrying out format standardized conversion processing on the user data according to a preset format conversion requirement to obtain standardized user data;
P21-2: extracting formal numerical parameters from the standardized user data, and determining format difference values according to the formal numerical parameters;
P21-3: determining a difference value correction space based on the format difference value;
P21-4: when the difference value correction space meets the requirement, a correction module is called to correct according to the format difference value;
p21-5: and when the difference value correction space does not meet the requirement, sending format preprocessing early warning information.
The method comprises the steps of carrying out format standardization conversion processing on the user data according to preset format conversion requirements to obtain standardized user data, and further extracting formal numerical parameters of the standardized user data, such as font types, font sizes, paragraph intervals and the like of a standard format document, carrying out difference calculation according to the formal numerical parameters of the standardized user data and the formal numerical parameters of original data submitted by a user to obtain a format difference value, and taking the format difference value as a difference value correction space, namely a format adjustment space of the original data submitted by the user, such as font increasing two word sizes.
Further, when the difference value correction space meets the requirement, that is, when the format difference of the current data can be adjusted, a correction module is called to correct according to the format difference value so as to obtain the user data in the standard format, and when the difference value correction space does not meet the requirement, for example, the definition of the user photo is too low to repair or the document data is too fuzzy to be recognized by a machine, format preprocessing early warning information is sent to feed back the user resubmitted data.
P30: setting an evaluation rule, and constructing an evaluation processing module based on the evaluation rule and a user parameter evaluation library;
Further, step P30 of the embodiment of the present application further includes:
p31: fitting the evaluation rule based on housing audit requirements, determining an evaluation parameter relation, and constructing an evaluation function;
P32: according to the evaluation parameter relation, performing parameter comparison with the user parameter evaluation library, and extracting an auditing evaluation parameter and an association parameter;
p33: constructing a correlation evaluation sub-function according to the auditing evaluation parameters and the correlation parameters;
P34: and constructing an evaluation processing sub-module based on the associated evaluation sub-function, and constructing a parameter mapping relation between the associated evaluation sub-function and the evaluation function to construct the evaluation processing module.
Specifically, based on the housing audit requirement of the target area, such as upper limit regulation of the total amount of the family property, limit range of the building area of the per-capita housing, and the like, fitting application user evaluation rules, namely evaluation rules of user data, respectively performing evaluation relationship analysis on each evaluation parameter to determine the relationship between the evaluation parameter and the evaluation result, and constructing an evaluation function, namely an overall evaluation function for auditing the evaluation parameter. Further, according to the evaluation parameter relation, parameter comparison is carried out in the user parameter evaluation library, corresponding auditing evaluation parameters and associated parameters are matched, the auditing evaluation parameters and the associated parameters are used for constructing a plurality of associated evaluation sub-functions of the auditing evaluation parameters, a plurality of evaluation processing sub-modules are constructed based on the plurality of associated evaluation sub-functions, a parameter mapping relation between the associated evaluation sub-functions and the evaluation functions is established, finally, the plurality of associated evaluation sub-functions and the evaluation functions jointly form the evaluation processing module, and the evaluation processing module can be used for evaluating housing application data of the current applicant and auditing housing qualification of the current applicant.
P40: performing evaluation operation on the preprocessed user data by using the evaluation processing module to obtain a user data evaluation result;
In one possible embodiment of the application, the evaluation processing module is utilized to evaluate the preprocessed user data, judge whether each data submitted by the current user meets each housing audit rule of the target area, and calculate the matching degree of the user data and the audit rule, so as to be used as the user data evaluation result.
P50: acquiring a housing planning list, performing task segmentation optimization on the housing planning list, and determining a task execution sequence;
optionally, a housing planning list of the target area is obtained, and the resident evaluation tasks in the housing planning list are segmented and optimized according to the types, the number, the planning delivery time and the like of the housings displayed in the list, so that the uniformity and the rationality of task allocation in a specified construction period are ensured, and the working efficiency and the working quality are improved.
Further, as shown in fig. 2, step P50 of the embodiment of the present application further includes:
p51: according to the housing planning list, determining the number of housing plans, the housing planning area, the housing planning type and the housing planning time;
p52: based on the housing planning time, constructing a planning time sequence list according to the housing planning quantity, the housing planning area and the housing planning type;
P53: according to the housing planning types, respectively carrying out auditing and evaluation parameter analysis on the housing planning quantity and the housing planning area of each type to determine an evaluation task quantity;
P54: and adding the evaluation task quantity as a segmentation variable into the planning time sequence list, and performing task optimization segmentation by taking the distribution uniformity of housing planning types and the calculation power matching degree of the evaluation task quantity as targets according to the time distribution sequence of the evaluation task quantity, so as to determine the task execution sequence, wherein the task execution sequence comprises task execution starting time and execution evaluation task quantity.
The housing planning method comprises the steps of logging in a target area pipe network, acquiring a housing planning list of the target area, determining the housing planning quantity, the housing planning area, the housing planning type and the housing planning time of the target area according to the housing planning list, wherein the housing planning type comprises a sold housing, a public leased housing and the like, the housing planning time refers to the planning period of the target area for implementing a housing management task, and further, carrying out planning task allocation on the housing planning quantity, the housing planning area and the housing planning type based on the housing planning time to construct a planning time sequence list.
Further, according to the housing planning types, auditing and evaluating parameter analysis is performed on housings of different types, different housing planning amounts and different housing planning areas, and the evaluating task quantity of each type of housing, namely, the auditing quantity of user data is determined. The assessment task quantity is added into the planning time sequence list as a segmentation variable, assessment task optimization segmentation is carried out by taking the distribution uniformity of housing planning types and the calculation power matching degree of the assessment task quantity as targets according to the time distribution sequence of the assessment task quantity of each type of housing, the assessment task quantity of each type of housing is uniformly distributed into the housing planning time, the task execution sequence is obtained, the task execution sequence comprises the execution starting time of each assessment task and the execution assessment task quantity of each time period, the assessment power can be uniformly assessed, and the assessment process is optimized.
P60: and constructing a user screening module by using the user data evaluation result and the task execution sequence, carrying out task screening matching on a user, identifying the user data according to the task screening matching result, and constructing auxiliary management information for feedback.
The user information evaluation result and the task execution sequence are used for carrying out parameter relation mapping of the user information and housing information, constructing a parameter matching degree optimizing function, combining any optimizing algorithm, constructing a user screening module, carrying out matching of the user information and the housing information through the user screening module, carrying out corresponding housing adaptation identification on the user information according to the task screening matching result, and feeding back the housing adaptation identification serving as housing auxiliary management information serving as a reference for subsequent manual verification.
Further, as shown in fig. 3, step P60 of the embodiment of the present application further includes:
P61: determining task execution nodes, housing planning types, planning housing areas and planning housing quantity according to the task execution sequences;
p62: establishing an evaluation relationship between a user data evaluation result and a housing planning type, a planning housing area and a planning housing quantity;
p63: based on each evaluation relation, constructing an evaluation relation with the maximum matching degree of the user data as a target;
p64: determining a matching quantity tolerance value according to the number of planned houses, and setting constraint conditions based on the matching quantity tolerance value and task execution nodes;
p65: setting an optimizing algorithm, and constructing the user screening module based on the evaluating relation, the optimizing algorithm and the constraint condition.
It should be understood that, according to the task execution sequence, a plurality of task execution nodes are determined, and the task amount corresponding to each task execution node includes housing planning type, planning housing area, and planning housing number, an evaluation relationship between the user data evaluation result and the housing planning type, the planning housing area, and the planning housing number, that is, a matching relationship between the user data and the house parameters is established, and based on each evaluation relationship, an evaluation relationship is established with the maximum matching degree of the user data and the house parameters as a target, that is, a matching degree optimizing function is established, so as to calculate the matching degree of the data in the data submitted by each user and the house parameters, order the matching degree of the user data from high to low, and select the user with a larger matching degree as an alternative user.
Further, a matching quantity tolerance value is preset according to the number of planned houses, wherein the matching quantity tolerance value is the number of matched proper users, corresponds to the number of planned houses, but is slightly higher than the number of planned houses, so that replaceable alternative users in emergency are ensured. And setting constraint conditions, namely user screening quantity constraint, for each task execution node based on the matching quantity tolerance value.
Further, a proper optimizing algorithm, such as a local optimizing algorithm, a particle swarm optimizing algorithm and the like, is randomly selected, and the user screening module is constructed by combining the evaluation relation and the constraint condition so as to screen user data and select a target user.
In summary, the embodiment of the application has at least the following technical effects:
the application establishes an evaluation rule by constructing a user parameter evaluation library, establishes an evaluation processing module by combining the user parameter evaluation library, and carries out evaluation operation on the preprocessed user data by utilizing the evaluation processing module to obtain a user data evaluation result; acquiring a housing planning list, performing task segmentation optimization on the housing planning list, determining a task execution sequence, finally constructing a user screening module by using a user data evaluation result and the task execution sequence, performing task screening matching on a user, identifying user data according to the task screening matching result, and constructing auxiliary management information for feedback.
The technical effects of performing auxiliary screening of user data, reducing the workload of manual repeated screening in the earlier stage and improving the data management efficiency are achieved through a computer technology.
Embodiment two:
based on the same inventive concept as the method for assisting in managing the house user data in the foregoing embodiments, as shown in fig. 4, the present application provides a system for assisting in managing the house user data, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the user parameter evaluation library construction module 11, wherein the user parameter evaluation library construction module 11 is used for constructing a user parameter evaluation library, and the user parameter evaluation library comprises multidimensional evaluation parameters;
A user data preprocessing module 12, wherein the user data preprocessing module 12 is used for acquiring user data and preprocessing the user data;
the evaluation processing module construction module 13, wherein the evaluation processing module construction module 13 is used for setting evaluation rules and constructing an evaluation processing module based on the evaluation rules and a user parameter evaluation library;
the user data evaluation result acquisition module 14, wherein the user data evaluation result acquisition module 14 is used for performing evaluation operation on the preprocessed user data by using the evaluation processing module to obtain a user data evaluation result;
The task execution sequence determining module 15 is used for acquiring a housing planning list, performing task segmentation optimization on the housing planning list, and determining a task execution sequence;
And the management execution information feedback module 16 is used for constructing a user screening module by utilizing the user data evaluation result and the task execution sequence, carrying out task screening matching on a user, identifying the user data according to the task screening matching result, and constructing auxiliary management information for feedback.
Further, the user parameter evaluation library construction module 11 is further configured to perform the following steps:
acquiring a housing audit requirement, extracting parameters of the housing audit requirement, and determining an audit evaluation parameter set;
carrying out parameter influence analysis on the auditing evaluation parameter set, and determining each parameter influence coefficient;
acquiring an association parameter list, performing association calculation by using the association parameter list and the auditing evaluation parameter set, determining association parameters of the auditing evaluation parameters, and setting association evaluation coefficients of the association parameters based on an association calculation result;
and establishing a mapping relation among the auditing evaluation parameters, the parameter influence coefficients, the association parameters and the association evaluation coefficients, and constructing the user parameter evaluation library.
Further, the user profile preprocessing module 12 is further configured to perform the following steps:
Identifying and classifying the user data to determine a data classification cluster;
matching preprocessing strategies based on the data classification clusters, wherein the preprocessing strategies comprise format preprocessing and authentication preprocessing;
And calling a corresponding execution module by utilizing the matching preprocessing strategy to preprocess the user data.
Further, the user profile preprocessing module 12 is further configured to perform the following steps:
carrying out format standardized conversion processing on the user data according to a preset format conversion requirement to obtain standardized user data;
extracting formal numerical parameters from the standardized user data, and determining format difference values according to the formal numerical parameters;
determining a difference value correction space based on the format difference value;
when the difference value correction space meets the requirement, a correction module is called to correct according to the format difference value;
And when the difference value correction space does not meet the requirement, sending format preprocessing early warning information.
Further, the evaluation processing module 13 is further configured to perform the following steps:
Fitting the evaluation rule based on housing audit requirements, determining an evaluation parameter relation, and constructing an evaluation function;
According to the evaluation parameter relation, performing parameter comparison with the user parameter evaluation library, and extracting an auditing evaluation parameter and an association parameter;
constructing a correlation evaluation sub-function according to the auditing evaluation parameters and the correlation parameters;
and constructing an evaluation processing sub-module based on the associated evaluation sub-function, and constructing a parameter mapping relation between the associated evaluation sub-function and the evaluation function to construct the evaluation processing module.
Further, the task execution sequence determining module 15 is further configured to perform the following steps:
According to the housing planning list, determining the number of housing plans, the housing planning area, the housing planning type and the housing planning time;
based on the housing planning time, constructing a planning time sequence list according to the housing planning quantity, the housing planning area and the housing planning type;
according to the housing planning types, respectively carrying out auditing and evaluation parameter analysis on the housing planning quantity and the housing planning area of each type to determine an evaluation task quantity;
And adding the evaluation task quantity as a segmentation variable into the planning time sequence list, and performing task optimization segmentation by taking the distribution uniformity of housing planning types and the calculation power matching degree of the evaluation task quantity as targets according to the time distribution sequence of the evaluation task quantity, so as to determine the task execution sequence, wherein the task execution sequence comprises task execution starting time and execution evaluation task quantity.
Further, the management execution information feedback module 16 is further configured to execute the following steps:
determining task execution nodes, housing planning types, planning housing areas and planning housing quantity according to the task execution sequences;
establishing an evaluation relationship between a user data evaluation result and a housing planning type, a planning housing area and a planning housing quantity;
based on each evaluation relation, constructing an evaluation relation with the maximum matching degree of the user data as a target;
determining a matching quantity tolerance value according to the number of planned houses, and setting constraint conditions based on the matching quantity tolerance value and task execution nodes;
Setting an optimizing algorithm, and constructing the user screening module based on the evaluating relation, the optimizing algorithm and the constraint condition.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (6)
1. The method is applied to a housing intelligent management system, and comprises the following steps:
Constructing a user parameter evaluation library, wherein the user parameter evaluation library comprises multidimensional evaluation parameters;
Acquiring user data and preprocessing the user data;
setting an evaluation rule, and constructing an evaluation processing module based on the evaluation rule and a user parameter evaluation library;
Performing evaluation operation on the preprocessed user data by using the evaluation processing module to obtain a user data evaluation result;
acquiring a housing planning list, performing task segmentation optimization on the housing planning list, and determining a task execution sequence;
Constructing a user screening module by using the user data evaluation result and the task execution sequence, carrying out task screening matching on a user, identifying the user data according to the task screening matching result, and constructing auxiliary management information for feedback;
the acquiring a housing planning list, performing task segmentation optimization on the housing planning list, and determining a task execution sequence comprises the following steps:
According to the housing planning list, determining the number of housing plans, the housing planning area, the housing planning type and the housing planning time;
based on the housing planning time, constructing a planning time sequence list according to the housing planning quantity, the housing planning area and the housing planning type;
according to the housing planning types, respectively carrying out auditing and evaluation parameter analysis on the housing planning quantity and the housing planning area of each type to determine an evaluation task quantity;
Adding the evaluation task quantity as a segmentation variable into the planning time sequence list, performing task optimization segmentation by taking the distribution uniformity of housing planning types and the calculation power matching degree of the evaluation task quantity as targets according to the time distribution sequence of the evaluation task quantity, and determining the task execution sequence, wherein the task execution sequence comprises task execution starting time and execution evaluation task quantity;
The step of constructing a user screening module by using the user data evaluation result and the task execution sequence to perform task screening matching on a user comprises the following steps:
determining task execution nodes, housing planning types, planning housing areas and planning housing quantity according to the task execution sequences;
establishing an evaluation relationship between a user data evaluation result and a housing planning type, a planning housing area and a planning housing quantity;
based on each evaluation relation, constructing an evaluation relation with the maximum matching degree of the user data as a target;
determining a matching quantity tolerance value according to the number of planned houses, and setting constraint conditions based on the matching quantity tolerance value and task execution nodes;
Setting an optimizing algorithm, and constructing the user screening module based on the evaluating relation, the optimizing algorithm and the constraint condition.
2. The method of claim 1, wherein the constructing a user parameter assessment library comprises:
acquiring a housing audit requirement, extracting parameters of the housing audit requirement, and determining an audit evaluation parameter set;
carrying out parameter influence analysis on the auditing evaluation parameter set, and determining each parameter influence coefficient;
Acquiring a correlation parameter list, performing correlation calculation by using the correlation parameter list and the auditing evaluation parameter set, determining correlation parameters of auditing evaluation parameters, and setting correlation evaluation coefficients of the correlation parameters based on correlation calculation results;
and establishing a mapping relation among the auditing evaluation parameters, the parameter influence coefficients, the association parameters and the association evaluation coefficients, and constructing the user parameter evaluation library.
3. The method of claim 1, wherein the obtaining and preprocessing of the user profile comprises:
Identifying and classifying the user data to determine a data classification cluster;
matching preprocessing strategies based on the data classification clusters, wherein the preprocessing strategies comprise format preprocessing and authentication preprocessing;
And calling a corresponding execution module by utilizing the matching preprocessing strategy to preprocess the user data.
4. The method of claim 3, wherein the format preprocessing comprises:
carrying out format standardized conversion processing on the user data according to a preset format conversion requirement to obtain standardized user data;
extracting formal numerical parameters from the standardized user data, and determining format difference values according to the formal numerical parameters;
determining a difference value correction space based on the format difference value;
when the difference value correction space meets the requirement, a correction module is called to correct according to the format difference value;
And when the difference value correction space does not meet the requirement, sending format preprocessing early warning information.
5. The method of claim 2, wherein the setting the evaluation rule, building an evaluation processing module based on the evaluation rule, a user parameter evaluation library, comprises:
Fitting the evaluation rule based on housing audit requirements, and determining an evaluation parameter relation to construct an evaluation function;
According to the evaluation parameter relation, performing parameter comparison with the user parameter evaluation library, and extracting an auditing evaluation parameter and an association parameter;
constructing a correlation evaluation sub-function according to the auditing evaluation parameters and the correlation parameters;
and constructing an evaluation processing sub-module based on the associated evaluation sub-function, and constructing a parameter mapping relation between the associated evaluation sub-function and the evaluation function to construct the evaluation processing module.
6. A home subscriber profile auxiliary management system, the system comprising:
The system comprises a user parameter evaluation library construction module, a user parameter evaluation library generation module and a user parameter evaluation library generation module, wherein the user parameter evaluation library construction module is used for constructing a user parameter evaluation library, and the user parameter evaluation library comprises multidimensional evaluation parameters;
the user data preprocessing module is used for acquiring user data and preprocessing the user data;
The evaluation processing module construction module is used for setting evaluation rules and constructing an evaluation processing module based on the evaluation rules and a user parameter evaluation library;
The user data evaluation result acquisition module is used for carrying out evaluation operation on the preprocessed user data by utilizing the evaluation processing module to obtain a user data evaluation result;
The system comprises a task execution sequence determining module, a task execution sequence determining module and a task processing module, wherein the task execution sequence determining module is used for acquiring a housing planning list, performing task segmentation optimization on the housing planning list and determining a task execution sequence;
The management execution information feedback module is used for constructing a user screening module by utilizing the user data evaluation result and the task execution sequence, carrying out task screening matching on a user, identifying the user data according to the task screening matching result, and constructing auxiliary management information for feedback;
the task execution sequence determining module is further configured to perform the following steps:
According to the housing planning list, determining the number of housing plans, the housing planning area, the housing planning type and the housing planning time;
based on the housing planning time, constructing a planning time sequence list according to the housing planning quantity, the housing planning area and the housing planning type;
according to the housing planning types, respectively carrying out auditing and evaluation parameter analysis on the housing planning quantity and the housing planning area of each type to determine an evaluation task quantity;
Adding the evaluation task quantity as a segmentation variable into the planning time sequence list, performing task optimization segmentation by taking the distribution uniformity of housing planning types and the calculation power matching degree of the evaluation task quantity as targets according to the time distribution sequence of the evaluation task quantity, and determining the task execution sequence, wherein the task execution sequence comprises task execution starting time and execution evaluation task quantity;
the management execution information feedback module is further configured to execute the following steps:
determining task execution nodes, housing planning types, planning housing areas and planning housing quantity according to the task execution sequences;
establishing an evaluation relationship between a user data evaluation result and a housing planning type, a planning housing area and a planning housing quantity;
based on each evaluation relation, constructing an evaluation relation with the maximum matching degree of the user data as a target;
determining a matching quantity tolerance value according to the number of planned houses, and setting constraint conditions based on the matching quantity tolerance value and task execution nodes;
Setting an optimizing algorithm, and constructing the user screening module based on the evaluating relation, the optimizing algorithm and the constraint condition.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107464049A (en) * | 2017-07-27 | 2017-12-12 | 深圳市佰仟金融服务有限公司 | A kind of task distribution method, device and terminal device |
| CN111241262A (en) * | 2020-01-20 | 2020-06-05 | 深圳壹账通智能科技有限公司 | Loan qualification auditing method based on artificial intelligence and related equipment |
| US11715156B1 (en) * | 2022-07-13 | 2023-08-01 | Chengdu Qinchuan Iot Technology Co., Ltd. | Risk assessment methods and systems for affordable housing application in a smart city based on internet of things |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107464049A (en) * | 2017-07-27 | 2017-12-12 | 深圳市佰仟金融服务有限公司 | A kind of task distribution method, device and terminal device |
| CN111241262A (en) * | 2020-01-20 | 2020-06-05 | 深圳壹账通智能科技有限公司 | Loan qualification auditing method based on artificial intelligence and related equipment |
| US11715156B1 (en) * | 2022-07-13 | 2023-08-01 | Chengdu Qinchuan Iot Technology Co., Ltd. | Risk assessment methods and systems for affordable housing application in a smart city based on internet of things |
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