CN113947474A - Agricultural subject credit evaluation analysis system based on internet of things data acquisition - Google Patents
Agricultural subject credit evaluation analysis system based on internet of things data acquisition Download PDFInfo
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Abstract
The invention belongs to the field of agricultural subject credit evaluation, relates to a credit evaluation analysis technology, and is used for solving the problem that the existing credit evaluation analysis system can not distribute payment limit for high-quality users and common users in different limit distribution modes through historical loan data and asset data, in particular to an agricultural subject credit evaluation analysis system based on internet of things data acquisition, which comprises a credit evaluation analysis platform, wherein the credit evaluation analysis platform is in communication connection with a registration login module, a default analysis module, an asset evaluation module, a payment management module and a storage module; the login registering module is used for verifying and logging in the identity of the user through login information, and the login information comprises a user name and a login password of the user; the loan amount analysis method and the loan amount analysis system preferentially analyze the loan amount of the high-quality user and output the loan amount of the high-quality user by adopting the loan management module, thereby reducing the loan risk on the whole.
Description
Technical Field
The invention belongs to the field of agricultural subject credit evaluation, relates to a credit evaluation analysis technology, and particularly relates to an agricultural subject credit evaluation analysis system based on internet of things data acquisition.
Background
Credit assessment analysis is a preventive advance control method for quantitatively managing loan risk degree by a commercial bank aiming at loan risk to evaluate and analyze the credit degree of loan enterprises (projects) so as to prevent and control the loan risk;
the existing credit evaluation analysis system can generally analyze and evaluate the credit of a user according to the historical loan data of the user, namely risk control is carried out through a credit analysis result during paying, but after credit verification is passed, the credit evaluation analysis system cannot distribute the paying quota of all qualified users through the historical loan data and asset data, and cannot distribute the paying quota of high-quality users and ordinary users in different quota distribution modes.
Disclosure of Invention
The invention aims to provide an agricultural subject credit evaluation and analysis system based on internet of things data acquisition, which is used for solving the problem that the existing credit evaluation and analysis system cannot distribute loan amount for high-quality users and common users in different amount distribution modes through historical loan data and asset data;
The technical problems to be solved by the invention are as follows:
how to provide a credit evaluation analysis system which can distribute loan amount by using different amount distribution modes for a good-quality user and a common user through historical loan data and asset data.
The purpose of the invention can be realized by the following technical scheme:
an agricultural subject credit evaluation analysis system based on internet of things data acquisition comprises a credit evaluation analysis platform, wherein the credit evaluation analysis platform is in communication connection with a registration login module, a default analysis module, an asset evaluation module, a deposit management module and a storage module;
the login registering module is used for verifying and logging in the identity of the user through login information, and the login information comprises a user name and a login password of the user;
the default analysis module is used for analyzing and evaluating the credit of the user through default data records of the user to obtain a default coefficient WYx of the user, and judging whether the user is a qualified user or not according to a comparison result of the default coefficient WYx and a default threshold WYmax;
the asset evaluation module is used for evaluating and analyzing the assets of qualified users through real estate data, deposit data and flow data to obtain an asset coefficient ZCx of the qualified users, and screening the qualified users into high-quality users and common users through the comparison result of the asset coefficient ZCx and an asset threshold ZCmin;
The deposit management module is used for managing deposit amount of qualified users, marking the total deposit amount as ZJ, marking the number of high-quality users as n, and marking the ratio of the total deposit amount ZJ to n as a deposit threshold; inputting the asset coefficient ZCx, default coefficient WYx and loan threshold value of the high-quality user into an amount matching model, and analyzing and calculating the loan amount of the high-quality user through the amount matching model;
marking the sum of the money paid by all the high-quality users as high-quality funds, marking the difference value between the total sum of the money paid by all the high-quality users and the high-quality funds as common funds, sorting the common users from large to small according to the value of the asset coefficient ZCx, selecting the first L1 common users in the sorting as candidate users, and marking the ratio of the common funds to the L1 as the money paid by the common users.
Further, the login registering module acquires the user name of the user and then sends the user name to the storage module;
if the user name can be acquired from the storage module, the user is judged to be a registered user, the verification password of the registered user is acquired through the storage module, and the verification password is consistent with the login password input by the user, so that the login verification is judged to be successful; if the verification password is inconsistent with the login password input by the user, the login password input error is judged, and the login registering module sends a re-input signal to the user side;
If the user name cannot be acquired from the storage module, the user is judged to be an unregistered user, the login registering module sends a login failure signal and a registration link to the user side, the user clicks the registration link to register an account through registration information, and the registration information comprises the name, the age, the mobile phone number, the information of a bound bank card and a self-set login password.
Further, the specific process of analyzing the credit of the user by the default analysis module includes:
acquiring a user default record in L1 days, and respectively marking the user default times and the default total amount in L1 days as CS and JE, wherein the unit of the default total amount is ten-thousand yuan, and L1 is a time constant;
acquiring a time threshold CSmax and an amount threshold JEmax through a storage module, and comparing the default times CS and the default total amount JE with the time threshold CSmax and the amount threshold JEmax respectively:
if CS is greater than or equal to CSmax or JE is greater than or equal to JEmax, the credit level of the user is judged to be a low credit level;
if CS < CSmax and JE < JEmax, then determining the credit level of the user is a high credit level;
by the formula WYx ═ (α 1 × CS)e+ α 2 × JE obtains the default coefficient WYx of the user with high credit rating, where α 1 and α 2 are both proportional coefficients, α 1 > α 2 > 0, e is a natural constant, and the value of e is 2.73.
Further, the comparison of the default coefficient WYx with the default threshold WYmin includes:
if the default coefficient WYx of the user is smaller than the default threshold WYmax, the user is judged to be qualified for loan, and the corresponding user is marked as a qualified user;
and if the default coefficient WYx of the user is not less than the default threshold WYmax, judging that the user does not qualify for loan, and marking the corresponding user as a disqualified user.
Further, the real estate data of the qualified users is the registered real estate area total MJ of the users, the deposit data of the qualified users is the sum CK of deposits of all financial accounts of the users, the running data of the qualified users is the running sum LS of the financial accounts of the users within half a year, the unit of the real estate data is square meters, and the units of the deposit data and the running data are ten thousand yuan.
Further, the process of analyzing and calculating the asset coefficient comprises the following steps:
by the formula
And obtaining the asset coefficient ZCx of the qualified user, wherein beta 1, beta 2 and beta 3 are all proportionality coefficients, beta 1 > beta 2 > beta 3 > 1, and m is a correction factor.
Further, the comparing of the asset coefficient to the asset threshold comprises:
if the asset coefficient ZCx of the qualified user is less than the asset threshold ZCmin, the corresponding qualified user is determined to be a common user;
And if the asset coefficient ZCx of the qualified user is not less than the asset threshold ZCmin, determining that the corresponding qualified user is a good-quality user.
Further, the specific process of analyzing and calculating the asset coefficient ZCx, the default coefficient WYx and the deposit threshold value by the credit matching model comprises the following steps: establishing a rectangular coordinate system by taking the asset coefficient ZCx as a horizontal coordinate and the deposit amount as a vertical coordinate, and establishing a rectangular coordinate system by a formula
Obtaining a slope k, wherein gamma is a proportionality coefficient, and gamma is more than 0 and less than 1;
and drawing a line segment in the first quadrant of the rectangular coordinate system by taking the origin of the rectangular coordinate system as an endpoint slope k, taking the vertical coordinate of the other endpoint of the line segment as a deposit threshold, marking the obtained line segment as a deposit curve, acquiring the corresponding vertical coordinate of the asset coefficient ZCx of the high-quality user on the deposit curve, and marking the obtained vertical coordinate as the deposit amount for output.
Further, the working method of the credit evaluation analysis platform comprises the following steps:
the method comprises the following steps: a user needing loan logs in a credit evaluation and analysis platform through a registration and login module, a default analysis module obtains a default coefficient of the user by analyzing default data records of the user, and whether the user is a qualified user with loan qualification is judged according to a comparison result of the default coefficient and a default threshold;
Step two: performing asset assessment analysis on qualified users with loan qualification by adopting an asset assessment module to obtain asset coefficients of the qualified users, and screening the qualified users into high-quality users and common users according to the comparison result of the asset coefficients and an asset threshold value;
step three: and preferentially analyzing the paying amount of the high-quality user by adopting an amount matching model, outputting the paying amount of the high-quality user, and analyzing the paying amount of the common user after the paying amount of the high-quality user is distributed.
The invention has the following beneficial effects:
1. the default analysis module analyzes default data records of the user to obtain default coefficients of the user, judges whether the user is a qualified user with loan qualification or not according to a comparison result of the default coefficients and the default threshold value, so as to judge whether the user has the loan qualification or not, performs property assessment on the user with the loan qualification, and effectively controls loan risks of the user through credit examination;
2. the property evaluation module is used for carrying out property evaluation analysis on qualified users with loan qualification to obtain property coefficients of the qualified users, the qualified users are screened as high-quality users and ordinary users according to the comparison result of the property coefficients and property threshold values, and the qualification of the qualified users is distinguished and screened through the property of the users, so that the loan amount distribution is carried out on the high-quality users and the ordinary users in different amount distribution modes, the better the overall qualification of the high-quality users is, the lower the loan amount which can be distributed by the ordinary users is, and the loan risk is further reduced;
3. The loan amount analysis is preferentially carried out on the loan amount of the high-quality user by adopting the loan management module, the loan amount of the high-quality user is output, the loan amount matching model formulates a loan curve for each high-quality user, the loan amount is obtained through the asset coefficient of the high-quality user in the loan curve, the higher the loan amount which can be distributed by the user with the better qualification among the high-quality users is, the better the overall qualification of the high-quality user is, the lower the common fund amount used for paying the common user is, the reasonable allocation of the fund is ensured under the condition of certain loan total amount, and the loan risk is reduced on the whole.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
fig. 2 is a flowchart of a method according to a first embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in fig. 1, an agricultural subject credit evaluation analysis system based on internet of things data acquisition comprises a credit evaluation analysis platform, wherein the credit evaluation analysis platform is in communication connection with a registration login module, a default analysis module, an asset evaluation module, a deposit management module and a storage module.
The default analysis module is used for analyzing and evaluating the credit of the user through default data records of the user, the default data records comprise default times and default total amount of money of the user, and the specific analysis and evaluation process comprises the following steps:
acquiring a user default record in L1 days, and respectively marking the user default times and the default total amount in L1 days as CS and JE, wherein the unit of the default total amount is ten-thousand yuan, and L1 is a time constant;
acquiring a time threshold CSmax and an amount threshold JEmax through a storage module, and comparing the default times CS and the default total amount JE with the time threshold CSmax and the amount threshold JEmax respectively:
If CS is larger than or equal to CSmax or JE is larger than or equal to JEmax, the credit level of the user is judged to be a low credit level, the low credit level represents that the user has serious default records in the historical loan process, and the loan qualification analysis is not carried out on the user with the low credit level;
if CS < CSmax and JE < JEmax, determining that the credit level of the user is a high credit level, wherein the high credit level indicates that the user does not have serious default records in the historical loan process, and performing loan qualification analysis on the user with the high credit level;
obtaining the default times CS and the default total amount JE of all the users with high credit rating according to the formula WYx ═ alpha 1 × CSe+ α 2 × JE obtains a default coefficient WYx of the user, it should be noted that the default coefficient WYx is a numerical value that reflects the credit worthiness of the user in the history lending process, and the higher the value of the default coefficient WYx is, the worse the credit of the user in the history reception process is, wherein α 1 and α 2 are proportional coefficients, α 1 > α 2 > 0, e is a natural constant, and the value of e is 2.73;
step S4: compare the default coefficient WYx for the user to the default threshold WYmax:
if the default coefficient WYx of the user is smaller than the default threshold WYmax, the user is judged to be qualified for loan, the corresponding user is marked as a qualified user, the qualified user indicates that the credit performance of the user in the historical loan process is good, and asset assessment analysis is carried out on the qualified user;
If the default coefficient WYx of the user is not less than the default threshold WYmax, judging that the user does not have loan qualification, marking the corresponding user as an unqualified user, wherein the qualified user indicates that the credit performance of the user in the historical loan process is poor, and performing no asset evaluation analysis on the unqualified user;
and sending the default coefficients WYx of the qualified users to the deposit management module and the storage module through the credit evaluation analysis platform.
The login registering module verifies and logs in the identity of the user through login information, the login information comprises a user name and a login password of the user, and the user name is sent to the storage module after the user name of the user is obtained;
if the user name can be acquired from the storage module, the user is judged to be a registered user, the verification password of the registered user is acquired through the storage module, and the verification password is consistent with the login password input by the user, so that the login verification is judged to be successful; if the verification password is inconsistent with the login password input by the user, the login password input error is judged, and the login registering module sends a re-input signal to the user side;
if the user name cannot be acquired from the storage module, the user is judged to be an unregistered user, the login registering module sends a login failure signal and a registration link to the user side, the user clicks the registration link to register an account through registration information, and the registration information comprises the name, the age, the mobile phone number, the information of a bound bank card and a self-set login password.
The asset evaluation module is used for evaluating and analyzing the assets of qualified users through real estate data, deposit data and flow data, wherein the real estate data of the qualified users is the registered real estate area total MJ of the users, the deposit data of the qualified users is the sum CK of deposits of all financial accounts of the users, the flow data of the qualified users is the flow sum LS of the financial accounts of the users within half a year, the unit of the real estate data is square meters, and the units of the deposit data and the flow data are ten thousand yuan;
by the formula
Obtaining an asset coefficient ZCx of a qualified user, wherein the asset coefficient ZCx is a numerical value reflecting the fund thickness of the qualified user, the higher the value of the asset coefficient ZCx is, the more the fund of the user is, and correspondingly, the stronger the repayment capability of the user is, wherein β 1, β 2 and β 3 are proportionality coefficients, β 1 > β 2 > β 3 > 1, and m is a correction factor;
the asset coefficient ZCx of the qualified user is compared to an asset threshold ZCmin:
if the asset coefficient ZCx of the qualified user is smaller than the asset threshold ZCmin, it is determined that the corresponding qualified user is a normal user whose capital thickness is normal and whose repayment capacity is relatively low compared with that of a high-quality user;
If the asset coefficient ZCx of the qualified user is not less than the asset threshold ZCmin, the corresponding qualified user is determined to be a high-quality user, the capital thickness of the high-quality user is high, and the repayment capacity of the high-quality user is relatively higher than that of a common user, so that the high-quality user with the relatively high repayment capacity is preferentially subjected to quota analysis during payment;
the asset coefficients ZCx of the regular users and the premium users are sent to the deposit management module through the credit rating analysis platform.
The paying management module is used for managing paying amount of qualified users, marking the paying total amount as ZJ, marking the number of high-quality users as n, marking the ratio of the paying total amount ZJ to n as a paying threshold value, distributing paying threshold values with equal values to each high-quality user, and comprehensively analyzing the specific paying amount of the high-quality users by using asset coefficients and default coefficients of the high-quality users, so that the higher the paying amount which can be obtained by the high-quality users is, the better the paying overdue risk of the high-quality users is reduced as a whole; inputting the asset coefficient ZCx, default coefficient WYx and loan threshold of the high-quality user into a quota matching model, analyzing and calculating the loan amount of the high-quality user through the quota matching model, wherein the loan amount of the high-quality user is influenced by the loan threshold, the asset coefficient and the default coefficient;
The specific process of analyzing and calculating the asset coefficient ZCx, the default coefficient WYx and the deposit threshold value by the credit matching model comprises the following steps: establishing a rectangular coordinate system by taking the asset coefficient ZCx as a horizontal coordinate and the deposit amount as a vertical coordinate, and establishing a rectangular coordinate system by a formula
Obtaining a slope k, wherein the slope k represents the qualification of the high-quality user, and the larger the value of the slope k is, the better the qualification of the high-quality user is, so that under the condition of the same asset coefficient, the higher the value of the slope is, the higher the corresponding deposit amount of the high-quality user on a deposit curve is, and further the fine distribution of the deposit amount is realized, wherein gamma is a proportionality coefficient, and gamma is more than 0 and less than 1;
and drawing a line segment in the first quadrant of the rectangular coordinate system by taking the origin of the rectangular coordinate system as an endpoint slope k, taking the vertical coordinate of the other endpoint of the line segment as a deposit threshold, marking the obtained line segment as a deposit curve, acquiring the corresponding vertical coordinate of the asset coefficient ZCx of the high-quality user on the deposit curve, and marking the obtained vertical coordinate as the deposit amount for output.
Marking the sum of the money put of all the high-quality users as high-quality funds, marking the difference value between the total money put ZJ and the high-quality funds as common funds, sequencing the common users from large to small according to the value of the asset coefficient ZCx, selecting the first L1 common users in the sequence as candidate users, marking the ratio of the common funds to the L1 as the money put of the common users, wherein the better the overall qualification of the high-quality users is, the higher the sum of the high-quality funds is, and therefore, the lower the amount of the common funds for paying the common users is.
Example two
As shown in fig. 2, an agricultural subject credit evaluation analysis method based on internet of things data acquisition includes the following steps:
the method comprises the following steps: a user needing loan logs in a credit evaluation and analysis platform through a registration and login module, a login failure signal and a registration link are sent for an unregistered user, the user clicks the registration link to perform account registration through registration information, after the login is successful, a default analysis module analyzes default data records of the user to obtain default coefficients of the user, and whether the user is a qualified user with loan qualification is judged according to a comparison result of the default coefficients and default threshold values;
step two: performing asset assessment analysis on qualified users with loan qualification by adopting an asset assessment module to obtain asset coefficients of the qualified users, and screening the qualified users into high-quality users and common users according to the comparison result of the asset coefficients and an asset threshold value;
step three: and preferentially analyzing the paying amount of the high-quality user by adopting an amount matching model, outputting the paying amount of the high-quality user, and analyzing the paying amount of the common user after the paying amount of the high-quality user is distributed.
A credit evaluation and analysis system for agricultural main bodies based on internet of things data acquisition is characterized in that a user needing loan logs in a credit evaluation and analysis platform through a registration login module, a default analysis module analyzes default data records of the user to obtain default coefficients of the user, a property evaluation module is adopted to perform property evaluation and analysis on qualified users with loan qualifications to obtain property coefficients of the qualified users, a credit line matching model is adopted to preferentially analyze credit lines of high-quality users and output the credit lines of the high-quality users, and the credit line of common users is analyzed after the credit lines of the high-quality users are distributed.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: formula WYx ═ (α 1 × CS)e+ α 2 × JE; collecting multiple groups of sample data by a person skilled in the art and setting a corresponding default coefficient for each group of sample data; substituting the set default coefficient and the acquired sample data into formulas, forming a linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1 and alpha 2 which are 2.15 and 1.87 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and regarding the size of the coefficient, the size depends on the number of sample data and a default coefficient preliminarily set by a person skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantized value is not affected, for example, the default coefficient is proportional to the number of default times.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The examples are intended only to help illustrate the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. An agricultural subject credit evaluation analysis system based on internet of things data acquisition comprises a credit evaluation analysis platform, and is characterized in that the credit evaluation analysis platform is in communication connection with a registration login module, a default analysis module, an asset evaluation module, a deposit management module and a storage module;
The login registering module is used for verifying and logging in the identity of the user through login information, and the login information comprises a user name and a login password of the user;
the default analysis module is used for analyzing and evaluating the credit of the user through default data records of the user to obtain a default coefficient WYx of the user, and judging whether the user is a qualified user or not according to a comparison result of the default coefficient WYx and a default threshold WYmax;
the asset evaluation module is used for evaluating and analyzing the assets of qualified users through real estate data, deposit data and flow data to obtain an asset coefficient ZCx of the qualified users, and screening the qualified users into high-quality users and common users through the comparison result of the asset coefficient ZCx and an asset threshold ZCmin;
the deposit management module is used for managing deposit amount of qualified users, marking the total deposit amount as ZJ, marking the number of high-quality users as n, and marking the ratio of the total deposit amount ZJ to n as a deposit threshold; inputting the asset coefficient ZCx, default coefficient WYx and loan threshold value of the high-quality user into an amount matching model, and analyzing and calculating the loan amount of the high-quality user through the amount matching model;
marking the sum of the money paid by all the high-quality users as high-quality funds, marking the difference value between the total sum of the money paid by all the high-quality users and the high-quality funds as common funds, sorting the common users from large to small according to the value of the asset coefficient ZCx, selecting the first L1 common users in the sorting as candidate users, and marking the ratio of the common funds to the L1 as the money paid by the common users.
2. The agricultural subject credit evaluation analysis system based on internet of things data acquisition according to claim 1, wherein the login module is registered to obtain a user name of a user and then sends the user name to the storage module;
if the user name can be acquired from the storage module, the user is judged to be a registered user, the verification password of the registered user is acquired through the storage module, and the verification password is consistent with the login password input by the user, so that the login verification is judged to be successful; if the verification password is inconsistent with the login password input by the user, the login password input error is judged, and the login registering module sends a re-input signal to the user side;
if the user name cannot be acquired from the storage module, the user is judged to be an unregistered user, the login registering module sends a login failure signal and a registration link to the user side, the user clicks the registration link to register an account through registration information, and the registration information comprises the name, the age, the mobile phone number, the information of a bound bank card and a self-set login password.
3. The agricultural subject credit evaluation analysis system based on internet of things data collection according to claim 1, wherein the default analysis module analyzes the credit of the user by a specific process comprising:
Acquiring a user default record in L1 days, and respectively marking the user default times and the default total amount in L1 days as CS and JE, wherein the unit of the default total amount is ten-thousand yuan, and L1 is a time constant;
acquiring a time threshold CSmax and an amount threshold JEmax through a storage module, and comparing the default times CS and the default total amount JE with the time threshold CSmax and the amount threshold JEmax respectively:
if CS is greater than or equal to CSmax or JE is greater than or equal to JEmax, the credit level of the user is judged to be a low credit level;
if CS < CSmax and JE < JEmax, then determining the credit level of the user is a high credit level;
by the formula WYx ═ (α 1 × CS)e+ α 2 × JE obtains the default coefficient WYx of the user with high credit rating, where α 1 and α 2 are both proportional coefficients, α 1 > α 2 > 0, e is a natural constant, and the value of e is 2.73.
4. The agricultural subject credit evaluation analysis system based on internet of things data collection of claim 2, wherein the comparison process of the default coefficient WYx and the default threshold WYmin comprises:
if the default coefficient WYx of the user is smaller than the default threshold WYmax, the user is judged to be qualified for loan, and the corresponding user is marked as a qualified user;
and if the default coefficient WYx of the user is not less than the default threshold WYmax, judging that the user does not qualify for loan, and marking the corresponding user as a disqualified user.
5. The agricultural subject credit evaluation analysis system based on the internet of things data collection according to claim 4, wherein the real estate data of the qualified users is the registered real estate area total MJ of the users, the deposit data of the qualified users is the sum CK of deposits of all financial accounts of the users, the running data of the qualified users is the running sum LS of the financial accounts of the users within half a year, the unit of the real estate data is square meters, and the units of the deposit data and the running data are ten thousand yuan.
6. The agricultural subject credit evaluation analysis system based on internet of things data collection according to claim 5, wherein the asset coefficient analysis and calculation process comprises:
by the formula
And obtaining the asset coefficient ZCx of the qualified user, wherein beta 1, beta 2 and beta 3 are all proportionality coefficients, beta 1 > beta 2 > beta 3 > 1, and m is a correction factor.
7. The agricultural subject credit evaluation analysis system based on internet of things data collection of claim 6, wherein the comparing process of the asset coefficient and the asset threshold value comprises:
if the asset coefficient ZCx of the qualified user is less than the asset threshold ZCmin, the corresponding qualified user is determined to be a common user;
and if the asset coefficient ZCx of the qualified user is not less than the asset threshold ZCmin, determining that the corresponding qualified user is a good-quality user.
8. The agricultural subject credit evaluation analysis system based on internet of things data collection of claim 7, wherein the specific process of the quota matching model for analyzing and calculating the asset coefficient ZCx, the default coefficient WYx and the deposit threshold value comprises: establishing a rectangular coordinate system by taking the asset coefficient ZCx as a horizontal coordinate and the deposit amount as a vertical coordinate, and establishing a rectangular coordinate system by a formula
Obtaining a slope k, wherein gamma is a proportionality coefficient, and gamma is more than 0 and less than 1;
and drawing a line segment in the first quadrant of the rectangular coordinate system by taking the origin of the rectangular coordinate system as an endpoint slope k, taking the vertical coordinate of the other endpoint of the line segment as a deposit threshold, marking the obtained line segment as a deposit curve, acquiring the corresponding vertical coordinate of the asset coefficient ZCx of the high-quality user on the deposit curve, and marking the obtained vertical coordinate as the deposit amount for output.
9. The agricultural subject credit evaluation analysis system based on internet of things data collection of claim 8, wherein: the working method of the credit evaluation analysis platform comprises the following steps:
the method comprises the following steps: a user needing loan logs in a credit evaluation and analysis platform through a registration and login module, a default analysis module obtains a default coefficient of the user by analyzing default data records of the user, and whether the user is a qualified user with loan qualification is judged according to a comparison result of the default coefficient and a default threshold;
Step two: performing asset assessment analysis on qualified users with loan qualification by adopting an asset assessment module to obtain asset coefficients of the qualified users, and screening the qualified users into high-quality users and common users according to the comparison result of the asset coefficients and an asset threshold value;
step three: and preferentially analyzing the paying amount of the high-quality user by adopting an amount matching model, outputting the paying amount of the high-quality user, and analyzing the paying amount of the common user after the paying amount of the high-quality user is distributed.
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