CN115062042A - Intelligent query and supervision method for interest of non-standard trust assets principal - Google Patents
Intelligent query and supervision method for interest of non-standard trust assets principal Download PDFInfo
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Abstract
The invention provides an intelligent inquiry and supervision method for principal interest of non-standard trust assets, which comprises the following steps: step 1: abstract elements influencing interest of principal are screened from standard contracts; step 2: establishing an interval formula model based on the abstract elements; and step 3: and (3) carrying out relation binding on the interval formula model and the life cycle of the trusted asset, setting a time binding node, inquiring the interest amount of the principal of the financing party based on the time binding node, acquiring a corresponding supervision early warning instruction based on an inquiry result, and executing corresponding supervision early warning operation. By screening abstract elements related to system contracts and establishing an interval formula model, not only is direct calculation of interest of principal money facilitated, but also working efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of intelligent inquiry and supervision, in particular to an intelligent inquiry and supervision method for interest of principal fund of non-standard trust assets.
Background
When non-standard services are developed, the management of the asset end is generally carried out through a financing contract signed with a financing party, and according to some specific rules agreed by the financing contract: the method comprises the following steps of collecting total financing amount, starting financing date, ending financing date, annual interest rate, repayment frequency, counting frequency, benefit-based clearing mode, actual repayment date, promissory advanced repayment date, head-to-tail calculation mode and the like, subsequently collecting assets in a planned way, and reporting credit once the financing party does not pay according to the promissory repayment.
In the aspect of business requirements, the system needs to visually display the return receipt plan of the asset from the beginning to the end of the whole life cycle, and the calculated plan amount can directly initiate a receipt payment instruction by the appointed accounting date.
It can be seen from the above that the interest calculation rules are influenced, and are complicated, if the interest calculation rules are arranged and combined according to the key elements, hundreds of interest calculation scenes and corresponding algorithms exist, and the original interest plans of the interest calculation scenes and the corresponding algorithms are maintained manually through excel offline, so that the interest calculation rules are different in style. This causes the following problems:
the working efficiency of a trust manager is influenced, a unified entry rule is not provided, the trust manager temporarily creates an excel formula each time a new service is developed, and a result is generated according to the contract entry rule, so that the time consumption is long;
secondly, the method is not intuitive, other people except the input person cannot understand the excel meaning, double rechecks cannot be carried out, and a trust manager and a record-check of accounting are required according to the requirements of a company and a contract and a fund plan;
thirdly, the life cycle of the trusted assets cannot be managed in the system, and the generated money result cannot directly generate an online payment receiving instruction;
and fourthly, influencing supervision and reporting timeliness.
Therefore, the invention provides an intelligent query and supervision method for principal interest of non-standard trust assets.
Disclosure of Invention
The invention provides an intelligent query and supervision method for principal interest of non-standard trust assets, which is used for solving the technical problems.
The invention provides an intelligent inquiry and supervision method for interest of capital of non-standard trust assets, which comprises the following steps:
step 1: abstract elements influencing interest of principal are screened from standard contracts;
step 2: establishing an interval formula model based on the abstract elements;
and 3, step 3: and the interval formula model is related and bound with the life cycle of the trusted asset, a time binding node is set, the interest amount of the principal fund of the financing party is inquired based on the time binding node, a corresponding supervision early warning instruction is obtained based on the inquiry result, and corresponding supervision early warning operation is executed.
Preferably, based on the abstract elements, an interval formula model is established, which includes:
determining contract attributes of the system contract;
calling a consistent initial formula model from a formula database according to the contract attribute;
and mapping the abstract elements to the initial formula model to obtain an interval formula model.
Preferably, in the process of establishing the interval formula model, the method further includes:
obtaining an interval point for creating an interval axis, wherein the interval point is related to a starting point, a present point, a reduced point and a transaction point;
respectively acquiring the driving time of each interval point;
and sequencing the driving-in time according to the time points, combining the same time points, and determining the position taken amount corresponding to each time point.
Preferably, in the process of establishing the interval formula model, the method further includes:
setting a starting point and a deduction point, and recording the deduction amount as the amount to be deducted;
judging whether the starting point is a root reduction point or not;
if yes, judging whether the deduction amount needs to be deducted and reset;
if not, throwing out the over-sale exception and ending;
if the starting point is not the deduction point, for the starting point, deducting the deduction amount by the position holding amount, generating section details of the position holding amount-deduction amount, and updating the position holding amount corresponding to the starting point to be the position holding amount after deduction;
if the position holding amount is not enough to deduct the deduction amount, deducting the residual deduction amount by using the added amount corresponding to the starting point, generating section details of the principal amount-deduction amount, and updating the added transaction amount corresponding to the starting point to be the residual added transaction amount after deduction;
determining all points from the starting point to the subtraction point, and recalculating the position taken amount of each point;
and meanwhile, continuously judging whether the deduction amount is zero or not, if not, setting the corresponding starting point as the next interval point, and continuously pushing backwards to carry out deduction until the deduction amount is reset.
Preferably, in the process of establishing the interval formula model, the method further includes:
establishing a stored transaction interval, and judging whether the starting point is a transaction point and the position holding amount is larger than zero;
if so, generating a detail interval of the principal amount-the incremental amount;
creating a transaction interval for adding the cost, and judging whether unprocessed cost adding operation exists or not and whether the cost adding amount is larger than zero or not;
if so, generating a detail interval of the principal amount-the incremental amount.
Preferably, in the process of establishing the interval formula model, the method further includes:
acquiring a starting point, traversing a time axis based on the starting point as a reference point, sequentially processing a local subtraction point and a transaction point on the time axis, acquiring a first table interval detail and a second table interval detail, and realizing interval splitting;
filtering and combining the first table region detail and the second table region detail, and placing the first table region detail and the second table region detail in the same region;
determining interest of the interval of the same interval;
and generating an interval display identifier based on the determination result, and inquiring a pay-back plan of the financing party based on the display identifier.
Preferably, based on the abstract elements, an interval formula model is established, which includes:
acquiring element attributes of each abstract element, and calling a corresponding first formula from a formula database based on each element attribute, wherein the number of the element attributes is n;
n- (n-2) combining all element attributes until n combining all element attributes;
acquiring a combination set of each combination, and respectively calling a corresponding second formula from the formula database according to combination factors in the combination set;
constructing a formula matrix according to all the first formulas and all the second formulas, wherein the formula matrix is an n-row and n-column matrix, and the nonexistent elements are filled and supplemented with 0;
determining that intersections exist between adjacent row elements in the formula matrix, respectively constructing n-1 intersection sets, determining that first necessary information exists in each intersection set, constructing a first necessary layer based on the first necessary information, determining first unnecessary information existing in each intersection set, and constructing a first unnecessary layer based on the first unnecessary information;
determining overlapping information existing between first row elements in the formula matrix, taking the overlapping information as second necessary information to construct a second necessary layer, and simultaneously taking non-overlapping information existing between the first row elements as second unnecessary information to construct a second unnecessary layer based on the second unnecessary information;
and respectively allocating corresponding layer positions to the first necessary layer, the second necessary layer, the first unnecessary layer and the second unnecessary layer, and further constructing and obtaining an interval formula model.
Preferably, the relationship binding of the interval formula model and the life cycle of the trusted asset and the setting of the time binding node include:
acquiring execution sub-sets of the life cycle of the trusted asset, respectively determining parameter elements related to each sub-set, determining the participation cycle of each abstract element according to related results, and constructing a cycle array;
acquiring the time point of each row of elements in the periodic array, distributing a layer identifier to each element in a corresponding row of elements based on the time point, establishing a first relation with the first necessary layer, the second necessary layer, the first unnecessary layer and the second unnecessary layer, further determining a corresponding set point, and setting a corresponding first reminding component at the set point;
determining an execution starting point and an execution end point of each abstract element from the period array, setting a first type first reminding point based on the execution starting point, and setting a first type second reminding point at the execution end point;
judging whether a plurality of execution starting points exist at the same time or not based on the periodic array;
if the plurality of execution starting points exist, calling a first setting type according to the number of the plurality of execution starting points, and respectively setting a first reminding point of the corresponding type to each of the plurality of existing execution starting points according to the first setting type;
judging whether a plurality of execution end points exist at the same time or not based on the periodic array;
if the plurality of execution end points exist, calling a second setting type according to the number of the plurality of execution end points, and respectively setting a second reminding point of the corresponding type to each execution end point in the plurality of execution end points according to the second setting type;
obtaining a reminding map based on the first class first reminding points, the first class second reminding points, the corresponding class first reminding points and the corresponding class second reminding points;
determining and constructing reminding point column vectors based on the reminding map, respectively calculating a reminding value of each column vector, and setting a second reminding component and an inquireable component in the corresponding reminding point column vector according to the reminding value;
and setting time points corresponding to the first reminding component, the second reminding component and the queriable component as time binding nodes.
Preferably, the process of querying the interest amount of the principal of the financing party based on the time binding node includes:
carrying out weight calculation on the time binding nodes, classifying the time binding nodes according to the weights to obtain a plurality of classes of nodes, and distributing initial query time and query intervals to each class of nodes according to the weights;
determining an inquiry line of the initial inquiry time and the inquiry interval of each type of node, and judging whether different types of nodes have inquiry of the same time point or not;
if yes, setting multi-path parallel query at the same corresponding time point, and configuring the corresponding class nodes and the multi-path one by one according to the weight interval of the corresponding class nodes;
and after the multi-path parallel query is finished, sequentially restoring to the original path to continue the query.
Preferably, based on the query result, a corresponding supervision and early warning instruction is obtained, and a corresponding supervision and early warning operation is executed, including:
acquiring the query frequency of each time binding node, and meanwhile, determining corresponding query inconsistency information under the query frequency based on the query result;
analyzing the query inconsistent information with corresponding standard information and a corresponding standard program respectively to obtain a standard-meeting index and a non-meeting index of the query inconsistent information;
determining the number of the indicators which do not accord with the indicators and the weight of the indicators so as to obtain corresponding non-accord values;
determining the number of indexes meeting the standard indexes and the index weight so as to obtain corresponding meeting values;
wherein Y1 represents the corresponding non-compliance value; y2 represents the corresponding compliance value; n1 represents the number of non-compliance indicators; n2 represents the number of criteria met; alpha is alpha i1 A non-compliance factor representing the i1 th non-compliance index;
a weight representing the i1 th non-compliance index; alpha (alpha) ("alpha") i2 A conformity factor representing the i2 th conformity criterion;
represents the weight of the i1 th meeting standard index; max represents the sign of the maximum function; min represents the sign of the minimum function.
Comparing the non-conforming value with the conforming value, and if the non-conforming value meets the comparison standard, acquiring a conventional early warning instruction to perform conventional supervision early warning operation;
otherwise, acquiring the characteristic early warning index matched with the non-conforming index to perform characteristic supervision early warning operation.
Compared with the prior art, the beneficial effects of this application are as follows:
1. by screening abstract elements related to system contracts and establishing an interval formula model, not only is direct calculation of interest of principal money facilitated, but also working efficiency can be improved.
2. By determining the element attributes and combining n, a formula matrix can be effectively obtained, two necessary layers and two unnecessary layers can be effectively obtained by performing row intersection analysis and column overlap analysis on the corresponding row of the formula matrix, and then an interval formula model is effectively constructed according to the layer positions of different layers, so that follow-up supervision and query are facilitated, and the management efficiency is improved.
3. The number of times of inquiring of the time binding nodes is determined, the inconsistent information is inquired, the number of corresponding indexes and the index weight are determined, the coincidence value and the non-coincidence value are calculated, whether conventional supervision early warning or special supervision early warning is carried out or not can be determined through comparison of the values, and supervision early warning efficiency is improved.
4. The method comprises the steps of establishing a period array by preliminarily determining the participation period of each element, distributing layer identification based on time points, realizing establishment of contact with different layers, setting a reminding component, setting a reminding point for a starting point and an end point corresponding to the elements in the period array, judging a plurality of starting points and end points of the same time point, effectively guaranteeing reasonable coverage of the starting point and the end point, and further indicating the reminding point, calculating a reminding value by determining a column vector in a reminding map, setting the component, obtaining the set time point, realizing reasonable reminding coverage of an interval formula model, automatically inquiring and supervising when a certain set time point is reached, and improving the inquiring and supervising efficiency.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of an intelligent query and supervision method for principal interest of a non-standard trust asset in an embodiment of the present invention;
FIG. 2 is a flow chart of creating a partition axis in an embodiment of the present invention;
FIG. 3 is a flowchart of creating a segment for reducing costs in an embodiment of the present invention;
FIG. 4 is a first flowchart of creating a transaction interval, according to an embodiment of the present invention;
FIG. 5 is a second flowchart of creating a transaction interval in an embodiment of the present invention;
fig. 6 is a flowchart of splitting an interval according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1:
the invention provides an intelligent inquiry and supervision method for principal interest of non-standard trust assets, as shown in figure 1, comprising the following steps:
step 1: abstract elements influencing interest of principal are screened from standard contracts;
step 2: establishing an interval formula model based on the abstract elements;
and step 3: and the interval formula model is related and bound with the life cycle of the trusted asset, a time binding node is set, the interest amount of the principal fund of the financing party is inquired based on the time binding node, a corresponding supervision early warning instruction is obtained based on the inquiry result, and corresponding supervision early warning operation is executed.
In this embodiment, abstract elements, such as: principal, increasing principal point, decreasing principal point, starting point, ending point, increasing interest, time limit, etc.
In this embodiment, the interval formula model is established, that is, efficient calculation is realized by a set of unified algorithm to perform effective calculation of interest of principal for the type of standard contract, which saves time.
In this embodiment, the relationship binding refers to binding with the interval formula model according to query results that may be related to different time nodes, for example: based on the standard contract, information such as interest, principal, income and the like can be inquired at the time point 1, and at the moment, the corresponding results such as interest, principal, income and the like can be calculated at the time point 1 through the interval formula model.
In this embodiment, the supervision early warning instruction, for example, as a result of the query at time point 1, is that the financing party needs to pay at the time point 1, but at this time, a relevant payment record is not queried, and then the supervision early warning instruction needs to be sent to the financing party to remind of paying, for example, a payment code in accordance with the payment amount is directly generated and transmitted to the financing party.
In the embodiment, the design of the interval formula model is adapted to the diversity of the actual scenes of the non-standard assets. In the management of the trust assets, the cost increase, cost reduction and interest rate change of the assets are uncertain when the assets happen, and the principal interest factors defined by the assets of different trust plans are different. Once a plurality of transactions with the change of cost increase and cost reduction occur, if the interval model is not designed, the fund and interest to be returned of the asset end can not be calculated accurately. Meanwhile, business personnel are liberated from the complicated manual interest-counting method during offline.
The beneficial effects of the above technical scheme are: by screening abstract elements related to system contracts and establishing an interval formula model, not only is direct calculation of interest of principal money facilitated, but also working efficiency can be improved.
Example 2:
based on the embodiment 1, based on the abstract elements, an interval formula model is established, which includes:
determining contract attributes of the standard contract;
calling a consistent initial formula model from a formula database according to the contract attribute;
and mapping the abstract elements to the initial formula model to obtain an interval formula model.
In this embodiment, since interest calculation rules and the like corresponding to different contracts are different, the initial formula model is called according to the contract attributes.
In this embodiment, the formula database includes formula templates corresponding to different contracts, that is, corresponding initial formula models.
In this embodiment, the abstract elements are mapped to the initial formula model, and the method is mainly used for adjusting and optimizing the initial formula model to obtain the interval formula model.
The beneficial effects of the above technical scheme are: by determining contract attributes, an initial formula model is convenient to call, and an interval formula model is obtained through mapping operation of elements.
Example 3:
based on the embodiment 1, in the process of establishing the interval formula model, the method further includes:
obtaining an interval point for creating an interval axis, wherein the interval point is related to a starting point, a present point, a reduced point and a transaction point;
respectively acquiring the driving time of each interval point;
and sequencing the driving-in time according to the time points, combining the same time points, and determining the position taken amount corresponding to each time point.
In this embodiment, in the process of creating the model, the following steps are included: defining intervals, interval details, interval points, interval axes, and the like.
The section comprises a starting date, an ending date, a calculation number of days, a position holding amount, a change amount, a payment date and a detail section list.
The section details include start date, end date, calculation days, principal amount, interest rate, payment date, head and tail calculation mode, and amount of money to be billed.
The interval points comprise point types, dates, position holding amounts and transaction lists.
The dot type: and the hash set structure comprises point types of an increasing point, a decreasing point and a trading point. One interval point may have a plurality of point types, but one interval point cannot have two or more identical point types. And (3) position holding amount: the position holding amount of the current point is equal to the position holding amount of the previous point plus the change amount of the previous point. The taken position of the first node is 0.
The transaction type: the method comprises increasing cost, decreasing cost and changing interest rate.
List of transactions: hashmap structure, key is transaction type, value is transaction value.
The interval axis is an axis consisting of continuous intervals and is a single-direction linked list.
In this embodiment, the opening start point: SectionPoint is created from the start date (the origination date), the initial principal, point type: adding a cost point, adding a cost point and an initial cost point in a change list;
and (3) setting a principal change point: according to the principal plan, increasing the principal, recording and increasing the principal points, changing the list and adding the 'increase the principal, and changing the principal'; reducing the principal, recording the reduction point, adding the reduction point into the change list, and changing the principal;
and (3) transaction point making: generating transaction time points (counter points and payment points) according to the payment frequency; recording transaction points according to point types, adding a change list, recording null according to change amount, and calculating when generating intervals;
tying point: according to the end date (due date), the knot point, the point type: transaction point, amount record null;
and (3) time axis arrangement: and sorting (ascending order) according to the time points, combining the same time points, and calculating the position sum of each point.
As shown in particular in fig. 2.
The beneficial effects of the above technical scheme are: the interval model can be preliminarily defined conveniently, and the effective model can be obtained conveniently.
Example 4:
based on the embodiment 1, in the process of establishing the interval formula model, the method further includes:
setting a starting point and a deduction point, and recording the deduction amount as the deduction amount to be deducted;
judging whether the starting point is a root reduction point or not;
if yes, judging whether the deduction amount needs to be deducted and reset;
if not, throwing out the over-sale exception and ending;
if the starting point is not the deduction point, for the starting point, deducting the deduction amount by the position holding amount, generating section details of the position holding amount-deduction amount, and updating the position holding amount corresponding to the starting point to be the position holding amount after deduction;
if the position holding amount is not enough to deduct the deduction amount, deducting the residual deduction amount by using the added amount corresponding to the starting point, generating section details of the principal amount-deduction amount, and updating the added transaction amount corresponding to the starting point to be the residual added transaction amount after deduction;
determining all the points from the starting point to the subtracting point, and recalculating the position taken amount of each point;
and meanwhile, continuously judging whether the deduction amount is zero, if not, setting the corresponding starting point as the next interval point, and continuously pushing backwards to carry out deduction until the deduction amount is reset.
This embodiment is shown in detail in fig. 3.
The beneficial effects of the above technical scheme are: through the process setting of the section of subtracting the local, the section formula model is convenient to further enrich, and the uniformity of subsequent calculation of the section formula model is convenient to guarantee.
Example 5:
based on the embodiment 1, in the process of establishing the interval formula model, the method further includes:
establishing a stored transaction interval, and judging whether the starting point is a transaction point and the position holding amount is larger than zero;
if so, generating a detail interval of the principal amount-the incremental amount;
creating a transaction interval of the incremental cost, and judging whether unprocessed incremental cost operation exists or not and whether the incremental cost amount is larger than zero or not;
if so, generating a detail interval of the principal amount-the incremental amount.
See fig. 4 and 5 for this embodiment in particular.
The beneficial effects of the above technical scheme are: by dividing the processing of the transaction points into two aspects, the effectiveness of the detail interval obtained finally is convenient to guarantee, a calculation basis is provided for the interval formula model, and the uniformity of subsequent calculation is guaranteed.
Example 6:
based on the embodiment 1, in the process of establishing the interval formula model, the method further includes:
acquiring a starting point, traversing a time axis based on the starting point as a reference point, sequentially processing a local subtraction point and a transaction point on the time axis, acquiring a first table interval detail and a second table interval detail, and realizing interval splitting;
filtering and combining the first table region detail and the second table region detail, and placing the first table region detail and the second table region detail in the same region;
determining interest of the interval of the same interval;
and generating an interval display identifier based on the determination result, and inquiring a return interest plan of the financing party based on the display identifier.
See fig. 6 for this embodiment.
The beneficial effects of the above technical scheme are: by carrying out interval splitting and carrying out money calculation on the split intervals, corresponding intervals and interval details are generated, a calculation basis is further provided for an interval formula model, the rationality of subsequent calculation is ensured, and the efficiency is improved.
Example 7:
based on the embodiment 1, based on the abstract elements, an interval formula model is established, which includes:
acquiring element attributes of each abstract element, and calling a corresponding first formula from a formula database based on each element attribute, wherein the number of the element attributes is n;
n- (n-2) combining all element attributes until n combining all element attributes;
acquiring a combination set of each combination, and respectively calling a corresponding second formula from the formula database according to combination factors in the combination set;
constructing a formula matrix according to all the first formulas and all the second formulas, wherein the formula matrix is an n-row and n-column matrix, and the nonexistent elements are filled and supplemented with 0;
determining that intersections exist between adjacent row elements in the formula matrix, respectively constructing n-1 intersection sets, determining that first necessary information exists in each intersection set, constructing a first necessary layer based on the first necessary information, determining first unnecessary information existing in each intersection set, and constructing a first unnecessary layer based on the first unnecessary information;
determining overlapping information existing between first row elements in the formula matrix, taking the overlapping information as second necessary information to construct a second necessary layer, and simultaneously taking non-overlapping information existing between the first row elements as second unnecessary information to construct a second unnecessary layer based on the second unnecessary information;
and respectively allocating corresponding layer positions to the first necessary layer, the second necessary layer, the first unnecessary layer and the second unnecessary layer, and further constructing and obtaining an interval formula model.
In this embodiment, each element has its corresponding attribute, for example, the attribute corresponding to the incremental point is zeng, that is, it is represented by a special character, which is convenient to retrieve the formula related to the incremental point from the formula database, that is, the formula used conventionally.
In this embodiment, n- (n-2) combination refers to pairwise combination of all abstract elements, and n combination refers to full combination of all elements, and according to this manner, a corresponding second formula is obtained, for example: only one group of elements exists after n is combined, at the moment, a formula corresponding to the group of elements is called, and so on,
in this embodiment, in the formula matrix, the non-0 elements of each row are sequentially decreased by 1.
In this embodiment, the intersection set is to obtain a handover set in order to determine the existing necessary intersection information, for example, intersection information in the elements of the first row and the second row, and so on to obtain n-1 handover sets.
In this embodiment, the first unnecessary information may refer to remaining information except for the intersection.
In this embodiment, further, an essential layer and an unnecessary layer can be obtained.
In this embodiment, the first row element is a full element, the overlapping information is sequentially obtained, and as long as the overlapping information occurs twice, the overlapping information can be regarded as overlapping, and the second necessary information and the second unnecessary information are further obtained.
The beneficial effects of the above technical scheme are: by determining the element attributes and combining n, a formula matrix can be effectively obtained, two necessary layers and two unnecessary layers can be effectively obtained by performing row intersection analysis and column overlap analysis on the corresponding row of the formula matrix, and then an interval formula model is effectively constructed according to the layer positions of different layers, so that follow-up supervision and query are facilitated, and the management efficiency is improved.
Example 8:
on the basis of the embodiment 1, the method for binding the interval formula model with the life cycle of the trusted asset in a relation manner and setting a time binding node comprises the following steps:
acquiring execution sub-sets of the life cycle of the trusting asset, respectively determining parameter elements related to each sub-set, determining the participation cycle of each abstract element according to related results, and constructing a cycle array;
acquiring the time point of each row of elements in the periodic array, distributing a layer identifier to each element in a corresponding row of elements based on the time point, establishing a first relation with the first necessary layer, the second necessary layer, the first unnecessary layer and the second unnecessary layer, further determining a corresponding set point, and setting a corresponding first reminding component at the set point;
determining an execution starting point and an execution end point of each abstract element from the period array, setting a first type first reminding point based on the execution starting point, and setting a first type second reminding point at the execution end point;
judging whether a plurality of execution starting points exist at the same time or not based on the periodic array;
if the plurality of execution starting points exist, calling a first setting type according to the number of the plurality of execution starting points, and respectively setting a first reminding point of the corresponding type to each of the plurality of existing execution starting points according to the first setting type;
judging whether a plurality of execution end points exist at the same time or not based on the periodic array;
if the plurality of execution end points exist, calling a second setting type according to the number of the plurality of execution end points, and respectively setting a second reminding point of the corresponding type to each execution end point in the plurality of execution end points according to the second setting type;
obtaining a reminding map based on the first class first reminding points, the first class second reminding points, the corresponding class first reminding points and the corresponding class second reminding points;
determining and constructing reminding point column vectors based on the reminding map, respectively calculating a reminding value of each column vector, and setting a second reminding component and an inquireable component in the corresponding reminding point column vector according to the reminding value;
and setting time points corresponding to the first reminding component, the second reminding component and the queriable component as time binding nodes.
In this embodiment, the execution subset is determined by mr, all subsets are constructed to obtain the execution subset, and the participation period of the abstract element is determined based on the parameter elements involved in each subset, so as to construct the period array.
In this embodiment, the period array is sequentially created on the basis of the participation period of each element according to a time axis, for example, h1 represents element 1, h2 represents element 2, and h3 represents element 3, in this case, the parameter period of element 1 is from time 1 to time 4, the parameter period of element 2 is from time 2 to time 3, and the parameter period of element 3 is from time 2 to time 5, thereby creating the period array.
Such as: periodic array:
array 1 (element 1): time 1, time 2, time 3, time 4
Array 2 (element 2): time point 2 and time point 3
Array 3 (element 3): time point 2, time point 3, time point 4, time point 5
Wherein, the time point of each column element is determined, for example, the time point of the first column element is time point 1, and so on.
In this embodiment, the layer mark refers to a unique identification code, for example, the time point 2 occupies the elements 1, 2 and 3, and in this case, the identification needs to be assigned like the elements 1, 2 and 3.
In this embodiment, the first association is established for different time points, such as the identifiers assigned to different elements, and the set point refers to the time point needing to be reminded, of course, the time points are more than 4, and at least 10 or more.
In this embodiment, for example, there are 2 layer identifiers for the set point, and at this time, the reminding result needs to be determined according to the identifier types, the layer types, and the identifier numbers of the 2 layer identifiers.
In this embodiment, for example, element 1, the execution start point is time point 1, and the execution end point is time point 4.
In this embodiment, the first reminding point and the second reminding point are mainly used for distinguishing the starting point from the ending point, and if there are multiple execution starting points at the same time point, the types are set according to the number, and then the reminding point is set at the ending point.
In this embodiment, the reminding map is to comprehensively determine the set reminding points according to the end points, the start points, the different types and the number of different elements, and the types are, for example: loan type, etc.
In this embodiment, the set reminding points may construct a column vector of the reminding points, such as: the column vector includes elements: 122, the corresponding reminding value can be 5, and the corresponding reminding component and the queriable component are set in turn.
In this embodiment, setting the time point refers to setting a point of different components on the interval formula model, and further, may be used as a time binding node, for example, an interest query point at each time.
The beneficial effects of the above technical scheme are: the method comprises the steps of establishing a period array by preliminarily determining the participation period of each element, distributing layer identification based on time points, realizing establishment of contact with different layers, setting a reminding component, setting a reminding point for a starting point and an end point corresponding to the elements in the period array, judging a plurality of starting points and end points of the same time point, effectively guaranteeing reasonable coverage of the starting point and the end point, and further indicating the reminding point, calculating a reminding value by determining a column vector in a reminding map, setting the component, obtaining the set time point, realizing reasonable reminding coverage of an interval formula model, automatically inquiring and supervising when a certain set time point is reached, and improving the inquiring and supervising efficiency.
Example 9:
based on the embodiment 1, the process of querying the principal interest amount of the fund financing party based on the time binding node comprises the following steps:
carrying out weight calculation on the time binding nodes, classifying the time binding nodes according to the weights to obtain a plurality of classes of nodes, and distributing initial query time and query intervals to each class of nodes according to the weights;
determining an inquiry line of the initial inquiry time and the inquiry interval of each type of node, and judging whether different types of nodes have inquiry of the same time point or not;
if yes, setting multi-path parallel query at the same corresponding time point, and configuring the corresponding class nodes and the multi-path one by one according to the weight interval of the corresponding class nodes;
and after the multi-path parallel query is finished, sequentially restoring to the original path to continue the query.
In this embodiment, the cumulative sum of all weights of the time binding nodes is 1.
In this embodiment, classification refers to classification by type of query and supervision.
In this embodiment, the larger the weight is, the smaller the corresponding time interval is, and the shorter the corresponding query time is.
In this embodiment, the same query time point may exist for different types of nodes, and at this time, line diversion is required, for example, 2 nodes exist at the same time point, and at this time, 2 paths exist, and 2 paths of parallel queries are performed, so that query time is saved.
In this embodiment, for example, the weights 0.2 and 0.4 are used as boundary lines to perform weight classification, and different classes of nodes are obtained.
The beneficial effects of the above technical scheme are: by carrying out weight calculation and classifying according to the nodes, the query time and the query interval can be effectively set, and then the nodes existing at the same time point are used for carrying out the inquiry in multiple paths and in parallel, thereby further improving the query and monitoring efficiency.
Example 10:
based on the embodiment 1, based on the query result, the method obtains a corresponding supervision early warning instruction, and executes a corresponding supervision early warning operation, including:
acquiring the query frequency of each time binding node, and meanwhile, determining corresponding query inconsistency information under the query frequency based on the query result;
analyzing the query inconsistent information with corresponding standard information and a corresponding standard program respectively to obtain a standard-meeting index and a non-meeting index of the query inconsistent information;
determining the number of the indicators which do not accord with the indicators and the weight of the indicators so as to obtain corresponding non-accord values;
determining the number of indexes meeting the standard indexes and the index weight so as to obtain corresponding meeting values;
wherein Y1 represents the corresponding non-compliance value; y2 represents the corresponding compliance value; n1 represents the number of non-compliance indicators; n2 represents the number of criteria met; alpha is alpha i1 Denotes the ith1 non-compliance factor not complying with the index;
a weight representing the i1 th non-compliance index; alpha (alpha) ("alpha") i2 A conformity factor representing an i2 th conformity criterion;
represents the weight of the i1 th meeting standard index; max represents the sign of the maximum function; min represents the sign of the minimum function.
Comparing the non-conforming value with the conforming value, and if the non-conforming value meets the comparison standard, acquiring a conventional early warning instruction to perform conventional supervision early warning operation;
otherwise, acquiring the characteristic early warning index matched with the non-conforming index to perform characteristic supervision early warning operation.
In the embodiment, the supervision instruction is determined according to the query result, and if the query result shows that 1 day is left before the repayment date, an urgent repayment supervision early warning instruction is generated.
In this embodiment, the query frequency refers to the number of queries for the same event, such as queries performed 10 days away from the repayment date, queries performed 5 days away from the repayment date, and the like, and the query nonconformity information refers to, for example, the repayment amount, whether there is a small repayment amount, and the like.
In this embodiment, both the compliance index and the non-compliance index are determined based on a standard program (standard rule), and therefore, the compliance value and the non-compliance value are calculated.
In this embodiment, the comparison criterion may be a corresponding reminding threshold, so as to determine what kind of supervision early warning operation is performed.
The beneficial effects of the above technical scheme are: the number of times of querying of the time binding nodes is determined, the inconsistent information is queried, the number of corresponding indexes and the weight of the indexes are determined, the coincidence value and the non-coincidence value are calculated, whether conventional supervision early warning or special supervision early warning is carried out or not can be determined through comparison of the values, and supervision early warning efficiency is improved.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. An intelligent inquiry and supervision method for interest of non-standard trust assets principal is characterized by comprising the following steps:
step 1: abstract elements influencing interest of principal are screened from standard contracts;
step 2: establishing an interval formula model based on the abstract elements;
and 3, step 3: and the interval formula model is related and bound with the life cycle of the trusted asset, a time binding node is set, the interest amount of the principal fund of the financing party is inquired based on the time binding node, a corresponding supervision early warning instruction is obtained based on the inquiry result, and corresponding supervision early warning operation is executed.
2. The intelligent query and supervision method for principal interest in non-standard trust assets of claim 1, wherein building an interval formula model based on the abstract elements comprises:
determining contract attributes of the standard contract;
calling a consistent initial formula model from a formula database according to the contract attribute;
and mapping the abstract elements to the initial formula model to obtain an interval formula model.
3. The intelligent query and supervision method for principal interest of non-standard trust assets of claim 1, wherein in the process of establishing the interval formula model, further comprising:
obtaining an interval point for creating an interval axis, wherein the interval point is related to a starting point, a present point, a reduced point and a transaction point;
respectively acquiring the driving time of each interval point;
and sequencing the driving-in time according to the time points, combining the same time points, and determining the position taken amount corresponding to each time point.
4. The intelligent query and supervision method for principal interest of non-standard trust assets of claim 1, wherein in the process of establishing the interval formula model, further comprising:
setting a starting point and a deduction point, and recording the deduction amount as the deduction amount to be deducted;
judging whether the starting point is a root reduction point or not;
if yes, judging whether the deduction amount needs to be deducted and reset;
if not, throwing out the over-sale exception and ending;
if the starting point is not the deduction point, for the starting point, deducting the deduction amount by the position holding amount, generating section details of the position holding amount-deduction amount, and updating the position holding amount corresponding to the starting point to be the position holding amount after deduction;
if the position holding amount is not enough to deduct the deduction amount, deducting the residual deduction amount by using the added amount corresponding to the starting point, generating section details of the principal amount-deduction amount, and updating the added transaction amount corresponding to the starting point to be the residual added transaction amount after deduction;
determining all the points from the starting point to the subtracting point, and recalculating the position taken amount of each point;
and meanwhile, continuously judging whether the deduction amount is zero, if not, setting the corresponding starting point as the next interval point, and continuously pushing backwards to carry out deduction until the deduction amount is reset.
5. The intelligent query and supervision method for principal interest of non-standard trust assets of claim 1, wherein in the process of establishing the interval formula model, further comprising:
establishing a stored transaction interval, and judging whether the starting point is a transaction point and the position holding amount is larger than zero;
if so, generating a detail interval of the principal amount-the incremental amount;
creating a transaction interval for adding the cost, and judging whether unprocessed cost adding operation exists or not and whether the cost adding amount is larger than zero or not;
if yes, generating a detail interval of principal amount-incremental amount.
6. The intelligent query and supervision method for principal interest of non-standard trust assets of claim 1, wherein in the process of establishing the interval formula model, further comprising:
acquiring a starting point, traversing a time axis based on the starting point as a reference point, sequentially processing a local subtraction point and a transaction point on the time axis, acquiring a first table interval detail and a second table interval detail, and realizing interval splitting;
filtering and combining the first table region detail and the second table region detail, and placing the first table region detail and the second table region detail in the same region;
determining interest of the interval in the same interval;
and generating an interval display identifier based on the determination result, and inquiring a pay-back plan of the financing party based on the display identifier.
7. The intelligent query and supervision method for principal interest in non-standard trust assets of claim 1, wherein building an interval formula model based on the abstract elements comprises:
acquiring element attributes of each abstract element, and calling a corresponding first formula from a formula database based on each element attribute, wherein the number of the element attributes is n;
n- (n-2) combining all element attributes until n combining all element attributes;
acquiring a combination set of each combination, and respectively calling a corresponding second formula from the formula database according to combination factors in the combination set;
constructing a formula matrix according to all the first formulas and all the second formulas, wherein the formula matrix is an n-row and n-column matrix, and the nonexistent elements are filled and supplemented with 0;
determining that intersections exist between adjacent row elements in the formula matrix, respectively constructing n-1 intersection sets, determining that first necessary information exists in each intersection set, constructing a first necessary layer based on the first necessary information, determining first unnecessary information existing in each intersection set, and constructing a first unnecessary layer based on the first unnecessary information;
determining overlapping information existing between first row elements in the formula matrix, taking the overlapping information as second necessary information to construct a second necessary layer, and simultaneously taking non-overlapping information existing between the first row elements as second unnecessary information to construct a second unnecessary layer based on the second unnecessary information;
and respectively allocating corresponding layer positions to the first necessary layer, the second necessary layer, the first unnecessary layer and the second unnecessary layer, and further constructing and obtaining an interval formula model.
8. The intelligent query and supervision method for principal interest of non-standard trusted assets of claim 1, wherein the relational binding of the interval formula model with the life cycle of trusted assets and setting of time binding nodes comprises:
acquiring execution sub-sets of the life cycle of the trusted asset, respectively determining parameter elements related to each sub-set, determining the participation cycle of each abstract element according to related results, and constructing a cycle array;
acquiring the time point of each row of elements in the periodic array, distributing a layer identifier to each element in a corresponding row of elements based on the time point, establishing a first relation with the first necessary layer, the second necessary layer, the first unnecessary layer and the second unnecessary layer, further determining a corresponding set point, and setting a corresponding first reminding component at the set point;
determining an execution starting point and an execution end point of each abstract element from the period array, setting a first type first reminding point based on the execution starting point, and setting a first type second reminding point at the execution end point;
judging whether a plurality of execution starting points exist at the same time or not based on the periodic array;
if the plurality of execution starting points exist, calling a first setting type according to the number of the plurality of execution starting points, and respectively setting a first reminding point of the corresponding type to each of the plurality of existing execution starting points according to the first setting type;
judging whether a plurality of execution end points exist at the same time or not based on the periodic array;
if the plurality of execution end points exist, calling a second setting type according to the number of the plurality of execution end points, and respectively setting a second reminding point of the corresponding type to each execution end point in the plurality of execution end points according to the second setting type;
obtaining a reminding map based on the first class first reminding points, the first class second reminding points, the corresponding class first reminding points and the corresponding class second reminding points;
determining and constructing reminding point column vectors based on the reminding map, respectively calculating a reminding value of each column vector, and setting a second reminding component and an inquireable component in the corresponding reminding point column vector according to the reminding value;
and setting time points corresponding to the first reminding component, the second reminding component and the queriable component as time binding nodes.
9. The intelligent query and supervision method for principal interest of non-trusted assets of claim 1, wherein the process of querying principal interest amount of financing party based on the time binding node comprises:
carrying out weight calculation on the time binding nodes, classifying the time binding nodes according to the weights to obtain a plurality of classes of nodes, and distributing initial query time and query intervals to each class of nodes according to the weights;
determining an inquiry line of the initial inquiry time and the inquiry interval of each type of node, and judging whether different types of nodes have inquiry of the same time point or not;
if yes, setting multi-path parallel query at the same corresponding time point, and configuring the corresponding class nodes and the multi-path one by one according to the weight interval of the corresponding class nodes;
and after the multi-path parallel query is finished, sequentially restoring to the original path to continue the query.
10. The intelligent querying and supervising method for principal interest of non-standard trust assets of claim 1, wherein based on the query result, acquiring corresponding supervised pre-warning instruction, executing corresponding supervised pre-warning operation, comprises:
acquiring the query frequency of each time binding node, and meanwhile, determining corresponding query inconsistency information under the query frequency based on the query result;
analyzing the query inconsistent information with corresponding standard information and a corresponding standard program respectively to obtain a standard-meeting index and a non-meeting index of the query inconsistent information;
determining the number of the indicators which do not accord with the indicators and the weight of the indicators so as to obtain corresponding non-accord values;
determining the number of indexes meeting the standard indexes and the index weight so as to obtain corresponding meeting values;
wherein Y1 represents the corresponding non-compliance value; y2 represents the corresponding compliance value; n1 represents the number of non-compliance indicators; n2 represents the number of criteria met; alpha is alpha i1 A non-compliance factor representing the i1 th non-compliance index;
a weight representing the i1 th non-compliance index; alpha is alpha i2 A conformity factor representing an i2 th conformity criterion;
represents the weight of the i1 th meeting standard index; max represents the sign of the maximum function; min represents the sign of the minimum function;
comparing the non-conforming value with the conforming value, and if the non-conforming value meets the comparison standard, acquiring a conventional early warning instruction to perform conventional supervision early warning operation;
otherwise, acquiring the characteristic early warning index matched with the non-conforming index to perform characteristic supervision early warning operation.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060059032A1 (en) * | 2004-09-01 | 2006-03-16 | Wong Kevin N | System, computer program product, and method for enterprise modeling, temporal activity-based costing and utilization |
| US20070083495A1 (en) * | 2005-10-07 | 2007-04-12 | International Business Machines Corporation | System and method for generating suitable data for statistical analysis |
| CN104715416A (en) * | 2013-12-17 | 2015-06-17 | 远光软件股份有限公司 | Method and system for entrusted loan and trust loan business management |
| CN109886800A (en) * | 2019-01-24 | 2019-06-14 | 平安科技（深圳）有限公司 | Electronic device, nonstandard creditor's assets estimation method and computer readable storage medium |
| US20190228094A1 (en) * | 2018-01-25 | 2019-07-25 | Amadeus S.A.S. | Re-computing pre-computed query results |
| CN110619583A (en) * | 2019-09-20 | 2019-12-27 | 中国银行股份有限公司 | Account early warning information generation method and device |
| JP2020155013A (en) * | 2019-03-22 | 2020-09-24 | みずほ情報総研株式会社 | Procurement fund management system, procurement fund management method and procurement fund management program |
| CN112835952A (en) * | 2021-01-23 | 2021-05-25 | 闫国壮 | Stock index-based calculation analysis method and system |
| CN113240520A (en) * | 2021-03-09 | 2021-08-10 | 杭州政云数据技术有限公司 | Method for realizing rural finance three-resource supervision by using algorithm link based on data model |
| US20210326976A1 (en) * | 2020-04-06 | 2021-10-21 | Troutwood, LLC | System and Method For Simulating Financial Growth Over a Period of Time |
| CN113886435A (en) * | 2021-12-08 | 2022-01-04 | 国能大渡河大数据服务有限公司 | Information query method and system based on recurrent neural network |
| CN113934740A (en) * | 2021-10-18 | 2022-01-14 | 中国工商银行股份有限公司 | Financial data processing method and device, computer equipment and storage medium |
| CN114170004A (en) * | 2021-12-10 | 2022-03-11 | 深圳前海微众银行股份有限公司 | Scoring decision-making method, device, equipment and storage medium based on multiple events |
-
2022
- 2022-06-15 CN CN202210676349.1A patent/CN115062042B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060059032A1 (en) * | 2004-09-01 | 2006-03-16 | Wong Kevin N | System, computer program product, and method for enterprise modeling, temporal activity-based costing and utilization |
| US20070083495A1 (en) * | 2005-10-07 | 2007-04-12 | International Business Machines Corporation | System and method for generating suitable data for statistical analysis |
| CN104715416A (en) * | 2013-12-17 | 2015-06-17 | 远光软件股份有限公司 | Method and system for entrusted loan and trust loan business management |
| US20190228094A1 (en) * | 2018-01-25 | 2019-07-25 | Amadeus S.A.S. | Re-computing pre-computed query results |
| CN109886800A (en) * | 2019-01-24 | 2019-06-14 | 平安科技（深圳）有限公司 | Electronic device, nonstandard creditor's assets estimation method and computer readable storage medium |
| JP2020155013A (en) * | 2019-03-22 | 2020-09-24 | みずほ情報総研株式会社 | Procurement fund management system, procurement fund management method and procurement fund management program |
| CN110619583A (en) * | 2019-09-20 | 2019-12-27 | 中国银行股份有限公司 | Account early warning information generation method and device |
| US20210326976A1 (en) * | 2020-04-06 | 2021-10-21 | Troutwood, LLC | System and Method For Simulating Financial Growth Over a Period of Time |
| CN112835952A (en) * | 2021-01-23 | 2021-05-25 | 闫国壮 | Stock index-based calculation analysis method and system |
| CN113240520A (en) * | 2021-03-09 | 2021-08-10 | 杭州政云数据技术有限公司 | Method for realizing rural finance three-resource supervision by using algorithm link based on data model |
| CN113934740A (en) * | 2021-10-18 | 2022-01-14 | 中国工商银行股份有限公司 | Financial data processing method and device, computer equipment and storage medium |
| CN113886435A (en) * | 2021-12-08 | 2022-01-04 | 国能大渡河大数据服务有限公司 | Information query method and system based on recurrent neural network |
| CN114170004A (en) * | 2021-12-10 | 2022-03-11 | 深圳前海微众银行股份有限公司 | Scoring decision-making method, device, equipment and storage medium based on multiple events |
Non-Patent Citations (1)
| Title |
|---|
| 曾庆成;张笑菊;陈文浩;朱晓聪;: "基于BCMP排队网络的码头集卡预约优化模型", 系统工程学报, no. 05 * |
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|---|---|
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