CN117787676A - Decision flow simulation method, device, electronic equipment and computer readable medium - Google Patents
Decision flow simulation method, device, electronic equipment and computer readable medium Download PDFInfo
- Publication number
- CN117787676A CN117787676A CN202311798844.0A CN202311798844A CN117787676A CN 117787676 A CN117787676 A CN 117787676A CN 202311798844 A CN202311798844 A CN 202311798844A CN 117787676 A CN117787676 A CN 117787676A
- Authority
- CN
- China
- Prior art keywords
- decision
- simulation
- flow
- decision flow
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004088 simulation Methods 0.000 title claims abstract description 281
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000004519 manufacturing process Methods 0.000 claims abstract description 89
- 238000004364 calculation method Methods 0.000 claims description 32
- 238000012545 processing Methods 0.000 claims description 13
- 230000002159 abnormal effect Effects 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 11
- 230000004048 modification Effects 0.000 claims description 11
- 238000012986 modification Methods 0.000 claims description 11
- 230000004044 response Effects 0.000 claims description 9
- 230000004931 aggregating effect Effects 0.000 claims description 3
- 238000012360 testing method Methods 0.000 abstract description 19
- 230000008569 process Effects 0.000 abstract description 13
- 230000000694 effects Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 9
- 238000007726 management method Methods 0.000 description 5
- 230000001360 synchronised effect Effects 0.000 description 4
- 238000003066 decision tree Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 238000013500 data storage Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000012550 audit Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012954 risk control Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses a decision flow simulation method, which comprises the following steps: responding to the decision flow updating instruction, and modifying the corresponding production decision flow to determine a simulation decision flow; responding to a decision flow simulation instruction, and acquiring historical flow data according to a historical time period indicated by the decision flow simulation instruction; wherein the production decision stream is run during the historical time period; determining a parameter sequence to be decided corresponding to the historical flow data based on the simulation decision flow; and deciding the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result. The effect of carrying out the simulation test of the production operation process on the simulation decision flow is achieved, the effectiveness of the simulation decision flow is improved, and the simulation decision flow for completing the simulation test can be well adapted to the production operation process. The application also discloses a decision flow simulation device, electronic equipment and a computer readable medium.
Description
Technical Field
The present invention relates to the technical field of decision flow, and in particular, to a decision flow simulation method, a decision flow simulation device, an electronic device, and a computer readable medium.
Background
In the financial consumer business scenario, the decision flow engine is a key tool used by risk control personnel to control the entire user credit lifecycle. The whole structure of the decision flow is similar to that of a workflow, and the decision flow is used for arranging the execution sequence of various decision tools such as an existing decision set, a decision table, a cross decision table, a decision tree, a scoring card, a complex scoring card, a credit measurement, a decision matrix, a decision model and the like so as to clearly and intuitively realize a large complex business rule.
However, after the decision flow is put into production operation, the decision flow cannot be directly adjusted in the production operation process, and the adjusted decision flow cannot be subjected to simulation test in the production operation process.
Disclosure of Invention
The present application aims to solve one of the technical problems in the related art to some extent. To this end, the present application provides a decision flow simulation method, a decision flow simulation apparatus, an electronic device, and a computer readable medium.
As a first aspect of the present application, there is provided a method for simulating a decision flow, wherein the method includes:
responding to the decision flow updating instruction, and modifying the corresponding production decision flow to determine a simulation decision flow;
responding to a decision flow simulation instruction, and acquiring historical flow data according to a historical time period indicated by the decision flow simulation instruction; wherein the production decision stream is run during the historical time period;
determining a parameter sequence to be decided corresponding to the historical flow data based on the simulation decision flow;
and deciding the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result.
Optionally, the method further includes, after the deciding the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result:
synchronizing the running production decision flow in a production engine into the simulation decision flow under the condition that the simulation decision result meets the preset termination simulation condition;
and under the condition that the simulation decision result does not meet the preset termination simulation condition, modifying the simulation decision flow, and determining a parameter sequence to be decided corresponding to the historical flow data based on the modified simulation decision flow.
Optionally, the simulation decision results are multiple, each simulation decision result corresponds to one parameter sequence to be decided, and each simulation decision result is one of passing, failing and abnormal results; the preset termination simulation conditions comprise:
the number of simulation decision results to be passed is larger than a first preset threshold value in proportion to the number of the simulation decision results, and/or the number of simulation decision results to be abnormal results is smaller than a second preset threshold value in proportion to the number of the simulation decision results.
Optionally, the production decision flow and the simulation decision flow each include a plurality of decision nodes and parameter conditions corresponding to each decision node;
the response to the decision flow update instruction modifies the corresponding production decision flow to determine a simulation decision flow, including:
responding to a decision flow updating instruction, and acquiring a production decision flow corresponding to the decision flow updating instruction from a production engine;
and modifying corresponding decision nodes and/or parameter conditions in the production decision flow according to the decision flow updating instruction so as to determine a simulation decision flow.
Optionally, the parameter condition includes an index parameter condition or a base parameter condition; the determining the parameter sequence to be decided corresponding to the historical flow data based on the simulation decision flow comprises the following steps:
generating a basic parameter sequence according to the historical flow data;
acquiring a plurality of index parameter calculation information corresponding to a plurality of index parameter conditions in the simulation decision flow;
and determining the parameter sequence to be decided according to the basic parameter sequence and the index parameter calculation information.
Optionally, the determining the parameter sequence to be decided according to the basic parameter sequence and the calculation information of the plurality of index parameters includes:
calculating a plurality of index parameters corresponding to each basic parameter sequence according to the index parameter calculation information;
and aggregating each basic parameter sequence and a plurality of index parameters corresponding to the basic parameter sequence into one parameter sequence to be decided.
Optionally, the decision is made on the parameter sequence to be decided according to the simulation decision flow, so as to obtain a simulation decision result, which includes:
and for a plurality of parameters in each parameter sequence to be decided, sequentially calling each decision node of the simulation decision flow to execute a decision, and obtaining the simulation decision result.
As a second aspect of the present application, there is provided a decision flow simulation apparatus, wherein the apparatus includes a modification module, an acquisition module, a processing module, and a decision module;
the modification module is used for responding to the decision flow update instruction and modifying the corresponding production decision flow so as to determine a simulation decision flow;
the acquisition module is used for responding to the decision flow simulation instruction and acquiring historical flow data according to the historical time period indicated by the decision flow simulation instruction; wherein the production decision stream is run during the historical time period;
the processing module is used for determining a parameter sequence to be decided corresponding to the historical flow data based on the simulation decision flow;
the decision module is used for making a decision on the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result.
As a third aspect of the present application, there is provided an electronic apparatus, wherein the electronic apparatus includes:
one or more processors;
and a memory having one or more computer programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method for decision flow simulation described in the first aspect of the present application.
As a fourth aspect of the present application, there is provided a computer readable medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the method for simulating a decision flow according to the first aspect of the present application.
In the decision flow simulation method provided by the embodiment of the application, corresponding production decision flows are modified in response to a decision flow update instruction so as to determine simulation decision flows; responding to a decision flow simulation instruction, and acquiring historical flow data of a production decision flow decision according to a historical time period of the production decision flow operation indicated by the decision flow simulation instruction; determining a parameter sequence to be decided corresponding to the historical flow data based on the simulation decision flow; and making a decision on the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result, thereby achieving the effect of performing simulation test on the production running process of the simulation decision flow, improving the effectiveness of the simulation decision flow and enabling the simulation decision flow which completes the simulation test to be well adapted to the production running process.
Drawings
The application is further described below with reference to the accompanying drawings:
FIG. 1 is a flow chart of one implementation of a decision flow simulation method provided by embodiments of the present application;
FIG. 2 is a flow chart of yet another implementation of a decision flow simulation method provided by an embodiment of the present application;
FIG. 3 is a flow chart of another implementation of the decision flow simulation method provided by the embodiments of the present application;
FIG. 4 is a flow chart of yet another implementation of a decision flow simulation method provided by an embodiment of the present application;
FIG. 5 is a flow chart of another implementation of the decision flow simulation method provided by embodiments of the present application;
FIG. 6a is a schematic diagram of a flow width table implementation provided in an embodiment of the present application;
FIG. 6b is a schematic diagram of an index-wide table implementation provided in the examples of the present application;
FIG. 6c is a schematic diagram of a wide-format implementation provided by an example of the present application;
FIG. 7 is a schematic block diagram of an implementation of a decision flow simulation apparatus provided in an embodiment of the present application;
FIG. 8 is a schematic diagram of a decision stream editing page provided in an embodiment of the present application;
FIG. 9 is a schematic diagram of a trusted solution simulation technology architecture provided in an embodiment of the present application;
FIG. 10 is a block diagram of one embodiment of an electronic device provided herein;
fig. 11 is a block diagram of a computer readable medium provided herein.
Description of the reference numerals
101: processor 102: memory device
103: I/O interface 104: bus line
201: modification module 202: acquisition module
203: the processing module 204: decision module
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The examples in the embodiments are intended to be used for explaining the present application and are not to be construed as limiting the present application.
Reference in the specification to "one embodiment" or "an example" means that a particular feature, structure, or characteristic described in connection with the embodiment itself may be included in at least one embodiment disclosed herein. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.
At present, there is a serious drawback in the technical field of decision flow: after the decision flow is put into production operation, when the service requirement is changed, the service party needs to adjust the decision flow, but if the decision flow is directly adjusted on the production engine and the adjusted decision flow is directly put into production operation again, once the problem is generated, the problem is irreversible, so that the adjusted decision flow is preferably subjected to simulation test first, and then the adjusted decision flow subjected to simulation test is formally put into production operation. However, the current decision flow engine cannot perform simulation test on the production running process of the adjusted decision flow, does not have a real simulation function, and cannot effectively ensure that the decision flow subjected to the simulation test can adapt to the production running process.
In view of this, the inventor of the present application proposes that although the simulation test of the production operation process cannot be performed on the adjusted decision flow, the simulation test of the adjusted decision flow by using the historical flow data can achieve the same effect, so when the business requirement changes and the production decision flow put into production operation needs to be adjusted, the production decision flow can be obtained and modified to obtain the simulation decision flow, and then the simulation test of the simulation decision flow is performed by using the historical flow data.
Accordingly, as a first aspect of the embodiments of the present application, a method for simulating a decision flow is provided, as shown in fig. 1, where the method may include the following steps:
in step S110, corresponding production decision flows are modified in response to the decision flow update instruction to determine simulation decision flows;
in step S120, in response to the decision flow simulation instruction, historical pipeline data is obtained according to the historical time period indicated by the decision flow simulation instruction;
in step S130, determining a to-be-decided parameter sequence corresponding to the historical pipeline data based on the simulation decision flow;
in step S140, the to-be-decided parameter sequence is decided according to the simulation decision flow, so as to obtain a simulation decision result.
The decision flow simulation method provided by the embodiment of the application can be applied to a decision flow simulation device. After the decision flow is put into production operation, when the service requirement is changed, the service party needs to adjust the decision flow, at the moment, the decision flow simulation device receives a decision flow update instruction input by a user from the outside, responds to the decision flow update instruction, and starts to execute the decision flow simulation method provided by the embodiment of the application, acquires the production decision flow indicated by the decision flow update instruction, and modifies the production decision flow to obtain the simulation decision flow.
The embodiment of the application does not limit how to trigger the decision flow simulation instruction, for example, the user may send the decision flow simulation instruction to the decision flow simulation device in a manner of creating a simulation task, and the user may select the simulation decision flow and the historical time period when creating the simulation task.
The historical time period indicated by the decision flow simulation instruction may be a time period that the production decision flow has been running, so that historical flow data (or may be referred to as historical production data) that the production decision flow has actually decided in the historical time period may be obtained.
Based on the simulation decision flow, carrying out some statistical processing on the historical flow data to obtain a parameter sequence to be decided. It will be appreciated that the sequence of parameters to be decided consists of a plurality of parameters. And the simulation decision flow sequentially makes decisions on the multiple parameters, and finally a simulation decision result can be obtained.
In the decision flow simulation method provided by the embodiment of the application, corresponding production decision flows are modified in response to a decision flow update instruction so as to determine simulation decision flows; responding to a decision flow simulation instruction, and acquiring historical flow data of a production decision flow decision according to a historical time period of the production decision flow operation indicated by the decision flow simulation instruction; determining a parameter sequence to be decided corresponding to the historical flow data based on the simulation decision flow; and making a decision on the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result, thereby achieving the effect of performing simulation test on the production running process of the simulation decision flow, improving the effectiveness of the simulation decision flow and enabling the simulation decision flow which completes the simulation test to be well adapted to the production running process.
Further, by presetting a termination simulation condition, it is determined whether the current simulation test can be ended. Accordingly, in some embodiments, as shown in fig. 2, after the decision is made on the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result (i.e. step S140), the method may further include the following steps:
in step S150, synchronizing the running production decision flow in the production engine into the simulation decision flow when the simulation decision result meets a preset termination simulation condition;
in step S160, when the simulation decision result does not meet the preset termination simulation condition, the simulation decision flow is modified, and based on the modified simulation decision flow, a to-be-decided parameter sequence corresponding to the historical pipeline data is determined.
The simulation decision result meets the preset termination simulation condition, which indicates that the current simulation decision result meets the expectation, namely that the simulation decision flow is qualified, the current simulation test can be ended, the simulation decision flow can be put into production operation, and the production decision flow running in the production engine is synchronized into the simulation decision flow.
And (3) the simulation decision result does not meet the preset termination simulation condition, namely that the current simulation decision result does not meet the expected, namely that the simulation decision flow is not qualified yet, the simulation decision flow needs to be modified again, the simulation operation is performed again, and specifically, based on the modified simulation decision flow, the steps S130-S140 are executed until the simulation decision result of the re-simulation operation meets the preset termination simulation condition.
In the embodiment of the application, in order to further improve the effectiveness of the simulation decision flow, the simulation decision flow can be used for respectively deciding a plurality of parameter sequences to be decided, and whether the current simulation test can be ended or not is determined by counting a plurality of simulation decision results.
Correspondingly, in some embodiments, the simulation decision results are multiple, each simulation decision result corresponds to one of the parameter sequences to be decided, and each simulation decision result is one of passing, failing and abnormal results; the preset termination simulation conditions may include:
the number of simulation decision results to be passed is larger than a first preset threshold value in proportion to the number of the simulation decision results, and/or the number of simulation decision results to be abnormal results is smaller than a second preset threshold value in proportion to the number of the simulation decision results.
Wherein, as described above, a parameter sequence to be decided is composed of a plurality of parameters, when deciding any parameter, if an error occurs, the simulation decision result of the abnormal result can be directly output, and when deciding each parameter, the simulation decision result which passes or does not pass is output.
The limitation of the preset termination simulation condition in the embodiment of the present application is not limited thereto, for example, the preset termination simulation condition may be that the proportion of the simulation decision result for passing is located in a first preset interval, and/or the proportion of the simulation decision result for the abnormal result is located in a second preset interval, and/or the proportion of the simulation decision result for the abnormal result is smaller than a second preset threshold.
For example, assuming that the simulation decision flow is used to make decisions on whether to pass the user's credit card application, each sequence of parameters to be decided will correspond to a credit card application request, with the simulation decision result either passing, not passing, or being an exception result. Based on the service requirement, the passing rate of the credit card application needs to be controlled in a certain preset interval, and the current simulation decision flow is formally put into production operation only when the passing proportion of all simulation decision results is located in the preset interval.
In some embodiments, the production decision stream and the simulation decision stream each include a plurality of decision nodes and parameter conditions corresponding to each of the decision nodes.
As shown in fig. 3, the modification of the corresponding production decision stream in response to the decision stream update instruction to determine the simulation decision stream (i.e., step S110) may include the following steps:
in step S210, in response to the decision flow update instruction, acquiring a production decision flow corresponding to the decision flow update instruction from the production engine;
in step S220, according to the decision flow update instruction, the corresponding decision nodes and/or parameter conditions in the production decision flow are modified to determine a simulation decision flow.
The decision node is similar to a decision rule, and the parameter condition comprises a numerical relation between a preset specified parameter and a corresponding threshold value, or certain operations corresponding to the specified parameter. When the production decision flow is modified, the threshold corresponding to part or all of the decision nodes can be modified, and operations such as replacement, deletion, addition and the like can be performed on part or all of the decision nodes.
For example, a decision node in a production decision stream is matrix calculated based on a parameter, and the decision node may be modified such that the decision node becomes decision tree calculated based on a parameter.
In some embodiments, the parameter conditions include an index parameter condition or a base parameter condition; as shown in fig. 4, the determining the to-be-decided parameter sequence corresponding to the historical pipeline data based on the simulation decision flow (i.e. step S130) may include the following steps:
in step S310, generating a base parameter sequence according to the historical pipeline data;
in step S320, a plurality of index parameter calculation information corresponding to the index parameter conditions in the simulation decision flow is obtained;
in step S330, the parameter sequence to be decided is determined according to the basic parameter sequence and the calculation information of the index parameters.
The history stream data belongs to original data, and the basic parameter sequence may be a parameter sequence obtained by performing some basic statistical processing on the history stream data, for example, the basic parameter sequence may be a sequence of incoming data, where the incoming data generally includes information such as identification information, work information, income information, academic information, social security information, and the like of a user.
The index parameter refers to a parameter which can be obtained by further calculation on the basis of the basic parameter, and the corresponding index parameter calculation information can also be called as necessary data such as asset packs, and refers to codes, formulas and the like which are required to be used for calculating the index parameter.
According to the index parameter calculation information and the basic parameter sequence, the index parameter can be calculated. And the basic parameter sequence and the index parameters calculated based on the basic parameter sequence form a parameter sequence to be decided. Accordingly, in some embodiments, as shown in fig. 5, the determining the parameter sequence to be decided according to the base parameter sequence and the calculated information of the plurality of index parameters (i.e. step S330) may include the following steps:
in step S410, calculating a plurality of index parameters corresponding to each of the base parameter sequences according to the plurality of index parameter calculation information;
in step S420, each of the base parameter sequences and the corresponding plurality of index parameters are aggregated into one to-be-decided parameter sequence.
The data storage form of the index parameter, the base parameter sequence and the parameter sequence to be decided is not particularly limited in the embodiment of the present application. For example, the index parameter may be stored in an index wide table; the basic parameter sequence can also be called as flow data and can be stored in a flow width table; the index broad table and the flow broad table are aggregated to obtain a large broad table, wherein the stored parameter sequence to be decided is the data of each column (or each row) in the flow broad table can be a basic parameter sequence, and a plurality of index parameters corresponding to the basic parameter sequence are attached to the tail end of the current row (or the current column) to form a parameter sequence to be decided.
An example of aggregation of the index wide table and the running wide table to obtain a large wide table is described with reference to fig. 6a, 6b, and 6 c. As shown in FIG. 6a, a flow width representation is provided for an embodiment of the present application. As shown in fig. 6b, an indication of the width of an index is provided in an embodiment of the present application. As shown in fig. 6c, a large-width representation is provided for an embodiment of the present application.
It can be seen that each row of parameters in fig. 6a, such as 001, zhang san, 3424011998, 20000, 2023-10-11, hangzhou branch, is a basic parameter sequence, and the types of basic parameters include serial number, user name, identification number, borrowing amount, borrowing time, and borrowing branch.
Calculating the total borrowing amount of the same identity card number in the past six months based on the running water width table shown in fig. 6a, wherein the total borrowing amount is the index parameter f1; calculating the borrowing, separating and duplication removing quantity of the same identity card number in the past six months, wherein the borrowing, separation and duplication removing quantity is the index parameter f2. The index parameters f1 and f2 are stored in an index wide table shown in fig. 6 b. It can be seen that the parameters f1 and f2 in each row of parameters in fig. 6b are index parameters, and the parameter serial number and the identification card number are index parameters serving as index parameters.
According to the index parameters in the index wide table shown in fig. 6b, after the corresponding index parameters f1 and f2 are attached to the basic parameter sequence of the flow wide table shown in fig. 6a, a large wide table shown in fig. 6c is obtained, and each row of parameters in the large wide table, such as 001, zhang san, 3424011998, 20000, 2023-10-11, hangzhou branch, 20000 and 1, are all a to-be-decided parameter sequence, and the types of to-be-decided parameters include flow number, user name, identity card number, borrowing amount, borrowing time, borrowing branch, f1 and f2.
In some embodiments, the step of deciding the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result (i.e. step S140) may include the following steps: and for a plurality of parameters in each parameter sequence to be decided, sequentially calling each decision node of the simulation decision flow to execute a decision, and obtaining the simulation decision result.
The connection relationship between the decision nodes is not limited to a sequential connection, but may be determined when a user creates a production decision stream, for example, multiple decision nodes may execute decisions concurrently, may execute decisions sequentially in a certain order, or even, a simulation decision result may be determined according to a decision result of a last decision node, or may be determined jointly according to some or all decision results of all decision nodes, or the like.
As a second aspect of the embodiments of the present application, a decision flow simulation apparatus is provided, where, as shown in fig. 7, the apparatus may include a modification module 201, an acquisition module 202, a processing module 203, and a decision module 204;
the modification module 201 is configured to modify a corresponding production decision stream in response to a decision stream update instruction, so as to determine a simulation decision stream;
the obtaining module 202 is configured to respond to a decision flow simulation instruction, and obtain historical pipeline data according to a historical time period indicated by the decision flow simulation instruction;
the processing module 203 is configured to determine a to-be-decided parameter sequence corresponding to the historical pipeline data based on the simulation decision flow;
the decision module 204 is configured to make a decision on the parameter sequence to be decided according to the simulation decision flow, so as to obtain a simulation decision result.
In some embodiments, the simulation decision results are multiple, each simulation decision result corresponds to one of the parameter sequences to be decided, and each simulation decision result is one of pass, fail and abnormal results; the preset termination simulation conditions comprise:
the number of simulation decision results to be passed is larger than a first preset threshold value in proportion to the number of the simulation decision results, and/or the number of simulation decision results to be abnormal results is smaller than a second preset threshold value in proportion to the number of the simulation decision results.
In some embodiments, the production decision flow and the simulation decision flow each include a plurality of decision nodes and parameter conditions corresponding to each of the decision nodes;
the modification module 201 is configured to:
responding to a decision flow updating instruction, and acquiring a production decision flow corresponding to the decision flow updating instruction from a production engine;
and modifying corresponding decision nodes and/or parameter conditions in the production decision flow according to the decision flow updating instruction so as to determine a simulation decision flow.
In some embodiments, the parameter conditions include an index parameter condition or a base parameter condition; the processing module 203 is configured to:
generating a basic parameter sequence according to the historical flow data;
acquiring a plurality of index parameter calculation information corresponding to a plurality of index parameter conditions in the simulation decision flow;
and determining the parameter sequence to be decided according to the basic parameter sequence and the index parameter calculation information.
In some embodiments, the processing module 203 is configured to:
calculating a plurality of index parameters corresponding to each basic parameter sequence according to the index parameter calculation information;
and aggregating each basic parameter sequence and a plurality of index parameters corresponding to the basic parameter sequence into one parameter sequence to be decided.
In some embodiments, the decision module 204 is configured to: and for a plurality of parameters in each parameter sequence to be decided, sequentially calling each decision node of the simulation decision flow to execute a decision, and obtaining the simulation decision result.
The decision flow simulation method provided in the present application is described in detail below with reference to fig. 8 in conjunction with a specific embodiment.
As a specific implementation, the decision flow simulation device at least comprises a decision platform component, a production engine, a simulation engine, a background component, a big data platform component, a calculation component and a decision pre-component.
Fig. 8 is a schematic diagram of a decision flow editing page according to an embodiment of the present application. The user first generates a decision flow editing page on a decision platform component, as shown in fig. 8, the decision flow in the decision flow editing page comprises a plurality of decision tools, and the decision tools comprise a hit type rule packet, a score type rule packet, a credit calculation, a decision model, a sub-decision flow and a conditional gateway, wherein each decision tool is used as a decision node, and three decision nodes of the hit type rule packet, the score type rule packet and the credit calculation are executed in parallel with two decision nodes of the decision model and the sub-decision flow and are executed in advance before the conditional gateway. The decision node of the "conditional gateway" comprises a plurality of parameter conditions, namely a scoring card when the index A >100, a decision matrix when the index A >200, a decision table when the index A >300 and a decision tree when the index A > 400. It can also be seen that the decision flow shown in fig. 8 can output three different simulation decision results, namely "automatically pass" (i.e. pass), "automatically reject" (i.e. not pass), and "manually audit" (i.e. abnormal result).
On the decision flow editing page, a user creates a decision flow in a dragging mode, then deploys the decision flow to a production engine by clicking an online button, business flow data in the production process is also accessed to the production engine through an interface, the production engine makes a decision on the current business flow data based on the deployed decision flow, and the decision result and the current business flow data are both sent to a background component.
After the decision flow is put into production operation, when the service requirement is changed, the service party needs to adjust the corresponding production decision flow, a user selects the production decision flow on a management page of a background component, a simulation decision flow editing page is generated by clicking a simulation button, and the production decision flow is modified, for example, a decision node is added between two decision nodes, a certain decision node is deleted, and a certain decision node is clicked to generate a condition configuration page so as to modify a threshold value corresponding to index parameters in the decision node. And after the simulation decision stream is obtained, the simulation decision stream is sent to a simulation engine.
The user selects the simulation decision flow on the management page of the background component to establish a simulation task, and simultaneously selects a historical time period as simulation data time, wherein the historical time period is a time period in which the production decision flow has been operated. The background component sends the necessary data (index parameter calculation information) such as the asset package and the like corresponding to the index parameter condition of the simulation task and the simulation decision flow to the calculation component.
The computing component obtains the corresponding historical flow data in the historical time period from the big data platform component according to the historical time period, and generates flow data (basic parameter sequence) according to the historical flow data and stores the flow data in the flow width table. The calculation component calculates index data (index parameters) according to the necessary data such as asset packs and the like and the stream data and stores the index data (index parameters) into an index wide table. And the calculation component stores the running water wide table and the index wide table into the big data platform component and notifies the background component.
The background component informs the decision pre-component, the decision pre-component obtains the flow width table and the index width table from the big data platform component and aggregates the flow width table and the index width table to form a big width table, and the big width table data (to-be-decided parameter sequence) are read out one by one from the big width table and sent to the simulation engine.
And the simulation engine makes a decision on the large-width table data based on the simulation decision flow, and a simulation decision result is obtained and sent to the background component.
The entire simulation test process is transparent to the user, since various visual interfaces can be generated on the background component. And after all simulation decision results are obtained, the background component can also judge whether the simulation decision results meet the preset termination simulation conditions or not, and display the judgment results. Under the condition that the simulation decision result meets the preset termination simulation condition, the user can synchronize the simulation decision flow to the production engine by clicking the synchronous button, so that modification, simulation test and online production of the production decision flow are completed.
The decision flow simulation method provided in the present application is described in detail below with reference to fig. 9 in conjunction with another specific embodiment.
Fig. 9 shows a decision flow simulation architecture according to an embodiment of the present application. As another specific implementation mode, the decision flow simulation device can also be a system, and at least can comprise a decision flow management platform, a credit background, a simulation decision engine, a message queue module, an index calculation module and a big data platform.
Firstly, a checker adds a production decision stream into a simulation on a decision stream management platform to obtain a simulation decision stream.
Further, the checker pulls the simulation decision flow from the decision flow management platform on the trust background, creates a simulation task and then starts the simulation task.
The credit background sends the relevant index calculation script (i.e. index parameter calculation information) and the historical pipelining time period to the index calculation module.
The index calculation module acquires the historical flow data from the big data platform based on the historical flow time period, invokes calculation resources of the big data platform, and calculates index parameters according to the index parameter calculation information and the historical flow data. And the calculation result, namely a large-width table (comprising a parameter sequence to be decided) synthesized by the index parameters and the stream data, is sent to a message queue module.
The simulation decision engine reads the parameter sequence to be decided from the message queue one by one to make decisions.
As a third aspect of the embodiments of the present application, there is provided an electronic device, wherein, as shown in fig. 10, the electronic device includes:
one or more processors 101;
a memory 102 having one or more computer programs stored thereon, which when executed by the one or more processors 101, cause the one or more processors 101 to implement the method for simulating a decision flow provided by the first aspect of the embodiments of the present application.
The electronic device may also include one or more I/O interfaces 103 coupled between the processor 101 and the memory 102 configured to enable information interaction of the processor 101 with the memory 102.
Wherein the processor 101 is a device having data processing capabilities, including but not limited to a Central Processing Unit (CPU) or the like; memory 102 is a device with data storage capability including, but not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically charged erasable programmable read-only memory (EEPROM), FLASH memory (FLASH); an I/O interface (read/write interface) is connected between the processor and the memory, and can implement information interaction between the processor and the memory, which includes, but is not limited to, a data Bus (Bus), and the like.
In some embodiments, processor 101, memory 102, and I/O interface 103 are connected to each other via bus 104, and thus to other components of the computing device.
As a fourth aspect of the embodiments of the present application, as shown in fig. 11, there is provided a computer readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the decision flow simulation method provided in the first aspect of the embodiments of the present application.
Those skilled in the art will appreciate that implementing all or part of the processes in the methods of the embodiments described above may be accomplished by computer programs to instruct related hardware. Accordingly, the computer program may be stored in a non-volatile computer readable storage medium, which when executed, performs the method of any of the above embodiments. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The foregoing is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto, and it should be apparent to those skilled in the art that the present application includes but is not limited to the accompanying drawings and what is described in the above specific embodiments. Any modifications which do not depart from the functional and structural principles of the present application are intended to be included within the scope of the claims.
Claims (10)
1. A method of decision flow simulation, the method comprising:
responding to the decision flow updating instruction, and modifying the corresponding production decision flow to determine a simulation decision flow;
responding to a decision flow simulation instruction, and acquiring historical flow data according to a historical time period indicated by the decision flow simulation instruction; wherein the production decision stream is run during the historical time period;
determining a parameter sequence to be decided corresponding to the historical flow data based on the simulation decision flow;
and deciding the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result.
2. The method according to claim 1, wherein after deciding the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result, the method further comprises:
synchronizing the running production decision flow in a production engine into the simulation decision flow under the condition that the simulation decision result meets the preset termination simulation condition;
and under the condition that the simulation decision result does not meet the preset termination simulation condition, modifying the simulation decision flow, and determining a parameter sequence to be decided corresponding to the historical flow data based on the modified simulation decision flow.
3. The method of claim 2, wherein the plurality of simulation decision results are provided, each simulation decision result corresponds to one of the parameter sequences to be decided, and each simulation decision result is one of pass, fail and exception results; the preset termination simulation conditions comprise:
the number of simulation decision results to be passed is larger than a first preset threshold value in proportion to the number of the simulation decision results, and/or the number of simulation decision results to be abnormal results is smaller than a second preset threshold value in proportion to the number of the simulation decision results.
4. The method of claim 1, wherein the production decision flow and the simulation decision flow each comprise a plurality of decision nodes and parameter conditions corresponding to each of the decision nodes;
the response to the decision flow update instruction modifies the corresponding production decision flow to determine a simulation decision flow, including:
responding to a decision flow updating instruction, and acquiring a production decision flow corresponding to the decision flow updating instruction from a production engine;
and modifying corresponding decision nodes and/or parameter conditions in the production decision flow according to the decision flow updating instruction so as to determine a simulation decision flow.
5. The method of claim 4, wherein the parameter condition comprises an index parameter condition or a base parameter condition; the determining the parameter sequence to be decided corresponding to the historical flow data based on the simulation decision flow comprises the following steps:
generating a basic parameter sequence according to the historical flow data;
acquiring a plurality of index parameter calculation information corresponding to a plurality of index parameter conditions in the simulation decision flow;
and determining the parameter sequence to be decided according to the basic parameter sequence and the index parameter calculation information.
6. The method of claim 5, wherein determining the sequence of parameters to be decided based on the base sequence of parameters and the plurality of index parameter calculation information comprises:
calculating a plurality of index parameters corresponding to each basic parameter sequence according to the index parameter calculation information;
and aggregating each basic parameter sequence and a plurality of index parameters corresponding to the basic parameter sequence into one parameter sequence to be decided.
7. The method of claim 4, wherein deciding the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result, comprising:
and for a plurality of parameters in each parameter sequence to be decided, sequentially calling each decision node of the simulation decision flow to execute a decision, and obtaining the simulation decision result.
8. The device is characterized by comprising a modification module, an acquisition module, a processing module and a decision module;
the modification module is used for responding to the decision flow update instruction and modifying the corresponding production decision flow so as to determine a simulation decision flow;
the acquisition module is used for responding to the decision flow simulation instruction and acquiring historical flow data according to the historical time period indicated by the decision flow simulation instruction; wherein the production decision stream is run during the historical time period;
the processing module is used for determining a parameter sequence to be decided corresponding to the historical flow data based on the simulation decision flow;
the decision module is used for making a decision on the parameter sequence to be decided according to the simulation decision flow to obtain a simulation decision result.
9. An electronic device, the electronic device comprising:
one or more processors;
memory having stored thereon one or more computer programs which, when executed by the one or more processors, cause the one or more processors to implement the decision flow simulation method of any of claims 1 to 7.
10. A computer readable medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the decision flow simulation method according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311798844.0A CN117787676A (en) | 2023-12-25 | 2023-12-25 | Decision flow simulation method, device, electronic equipment and computer readable medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311798844.0A CN117787676A (en) | 2023-12-25 | 2023-12-25 | Decision flow simulation method, device, electronic equipment and computer readable medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117787676A true CN117787676A (en) | 2024-03-29 |
Family
ID=90387649
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311798844.0A Pending CN117787676A (en) | 2023-12-25 | 2023-12-25 | Decision flow simulation method, device, electronic equipment and computer readable medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117787676A (en) |
-
2023
- 2023-12-25 CN CN202311798844.0A patent/CN117787676A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108509212B (en) | Application program update test method and device, terminal equipment and storage medium | |
| US9459980B1 (en) | Varying cluster sizes in a predictive test load while testing a productive system | |
| CN107451040A (en) | Localization method, device and the computer-readable recording medium of failure cause | |
| CN112363941A (en) | Interface testing method and device, computer equipment and storage medium | |
| CN112395177A (en) | Interactive processing method, device and equipment of service data and storage medium | |
| CN110490594A (en) | Business data processing method, device, computer equipment and storage medium | |
| CN110991871A (en) | Risk monitoring method, device, equipment and computer readable storage medium | |
| CN110009347B (en) | Block chain transaction information auditing method and device | |
| CN109544014B (en) | Anti-fraud method and device based on historical data playback | |
| CN108460068A (en) | Method, apparatus, storage medium and the terminal that report imports and exports | |
| CN113220540A (en) | Service management method, device, computer equipment and storage medium | |
| CN103440460A (en) | Application system change validation method and system | |
| CN111459796B (en) | Automated testing method, apparatus, computer device and storage medium | |
| CN111106953B (en) | Method and device for analyzing abnormal root cause | |
| CN112817847A (en) | Data processing task testing method and device, electronic equipment and storage medium | |
| CN112506802A (en) | Test data management method and system | |
| CN117787676A (en) | Decision flow simulation method, device, electronic equipment and computer readable medium | |
| CN107682173B (en) | Automatic fault positioning method and system based on transaction model | |
| CN108932279A (en) | A kind of application page processing method and processing device | |
| CN114239296A (en) | Data processing method and device and electronic equipment | |
| CN109240906B (en) | Database configuration information adaptation method and device, computer equipment and storage medium | |
| CN111339089A (en) | Data storage and acquisition method and device applied to block chain | |
| CN115564322B (en) | Service processing method and device, electronic equipment and storage medium | |
| CN110765193A (en) | Method and device for exporting block chain data | |
| CN112380204B (en) | Data quality evaluation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |