Automatic approval decision method and device

Abstract

The present invention provides an automatic approval decision-making method and device, which relates to the field of data processing and can be applied to the financial field and other fields. The method comprises: constructing one or more feature sets according to the features of the approval object; matching the approval elements of the data to be approved with the feature sets to obtain matching results, analyzing the matching results through a preset accuracy estimation model to obtain the approval accuracy; comparing the approval accuracy with a preset threshold, and converting the feature set into a standard data set according to the comparison result; and performing approval processing on the standard data set through a preset approval strategy to obtain the approval result.

Description

Technical Field

[0001] The present invention relates to the field of data processing and can be applied to the financial field and other fields, and in particular to an automatic approval decision-making method and device. Background Art

[0002] Approval is involved in every aspect of daily life and work, such as home purchase qualification approval, examination registration materials approval, financial reimbursement approval, item collection approval, etc. The existing process approval systems are mainly divided into two categories: one is a manual approval system, and the other is a simple automatic approval system.

[0003] In the manual approval system, each approval link or most of the approval links require human participation to complete the approval, and then flow to the next approval link. However, there are many manual approval links, and the overall approval time is long; when there are many approval factors, it is easy to produce inaccurate or even wrong approval conclusions.

[0004] In a simple automatic approval system, the approval conclusion can be automatically drawn based on some simple preset conditions. However, the automatic approval judgment conditions are simple to set, and the approval quality is difficult to guarantee in scenarios with many approval factors and frequent changes. Summary of the invention

[0005] The purpose of the present invention is to provide an automatic approval decision-making method and device, which overcomes the shortcomings of low approval efficiency and single approval strategy in existing approval technical solutions, enables automatic approval to flexibly configure approval strategies and cope with complex approval scenarios.

[0006] To achieve the above-mentioned purpose, the present invention provides an automatic approval decision-making method, which includes: constructing one or more feature sets according to the characteristics of the approval object; matching the approval elements of the data to be approved with the feature sets to obtain matching results, and analyzing the matching results through a preset accuracy estimation model to obtain the approval accuracy; comparing the approval accuracy with a preset threshold, and converting the feature set into a standard data set according to the comparison result; and performing approval processing on the standard data set through a preset approval strategy to obtain the approval result.

[0007] In the above automatic approval decision method, preferably, matching the approval elements of the data to be approved with the feature set to obtain a matching result includes: serializing the approval elements of the data to be approved to generate an approval object; and matching the feature set through the approval object to obtain a matching result.

[0008] In the above-mentioned automatic approval decision method, preferably, constructing one or more feature sets according to the characteristics of the approval object includes: constructing feature information according to the descriptive information of the approval object characteristics, constructing one or more feature sets according to the feature information category; and defining the priority of the feature set according to a preset priority parameter.

[0009] In the above-mentioned automatic approval decision method, preferably, analyzing the matching result through a preset accuracy estimation model to obtain the approval accuracy includes: constructing the accuracy estimation model through one or more accuracy items; wherein the accuracy item includes one or more accuracy items, and the accuracy item is used to evaluate whether the preset conditions of the matching result are met; the accuracy item obtains an accuracy value according to whether the preset conditions of the accuracy item are met; and the accuracy estimation model obtains the approval accuracy according to the accuracy value.

[0010] In the above-mentioned automatic approval decision-making method, preferably, approving the standard data set through a preset approval strategy to obtain an approval result includes: constructing an approval policy item according to a preset approval process and a corresponding approval conclusion, and constructing an approval policy through the approval policy item; approving the standard data set through the preset approval strategy to obtain an approval conclusion; and obtaining an approval result based on the approval conclusion.

[0011] The present invention also provides an automatic approval decision device, which includes a priority grouping module, a conditional decision module and an approval decision module; the priority grouping module is used to construct one or more feature sets according to the features of the approval object; the conditional decision module is used to match the approval elements of the data to be approved with the feature set to obtain a matching result, and analyze the matching result through a preset accuracy estimation model to obtain the approval accuracy; and compare the approval accuracy with a preset threshold, and convert the feature set into a standard data set according to the comparison result; the approval decision module is used to perform approval processing on the standard data set through a preset approval strategy to obtain an approval result.

[0012] In the above-mentioned automatic approval decision device, preferably, the conditional decision module includes a matching unit, and the matching unit is used to serialize the approval elements of the approval data to generate an approval object; and obtain a matching result by matching the feature set through the approval object.

[0013] In the above-mentioned automatic approval decision-making device, preferably, the priority grouping module includes a feature set unit and a priority definition unit; the feature set unit is used to construct feature information based on the description information of the approval object characteristics, and to construct one or more feature sets based on the feature information category; the priority definition unit is used to define the priority of the feature set according to preset priority parameters.

[0014] In the above-mentioned automatic approval decision device, preferably, the conditional decision module includes an accuracy estimation unit, and the accuracy estimation unit is used to construct the accuracy estimation model through one or more accuracy items; wherein the accuracy item includes one or more accuracy items, and the accuracy item is used to evaluate whether the preset conditions of the matching results are met; the accuracy item obtains an accuracy value according to whether the preset conditions of the accuracy item are met; and the accuracy estimation model obtains the approval accuracy according to the accuracy value.

[0015] In the above-mentioned automatic approval decision device, preferably, the approval decision module includes: constructing approval policy items according to preset approval processes and corresponding approval conclusions, and constructing approval policies through the approval policy items; approving the standard data set through the preset approval policy to obtain approval conclusions; and obtaining approval results based on the approval conclusions.

[0016] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0017] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above method.

[0018] The beneficial technical effect of the present invention is that by adopting priority grouping and combining conditional decision-making and other modules, approval of complex scenarios can be achieved, such as situations where there are many approval factors. Approval can be completed automatically and efficiently. For situations where the accuracy is not enough for automatic approval, manual approval can be transferred to ensure the quality of approval. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:

[0020] Figure 1 A flowchart of an automatic approval decision-making method provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of feature set grouping provided by an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of a feature set matching process provided by an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of a process for obtaining approval results provided in an embodiment of the present invention;

[0024] Figure 5A schematic diagram of the connection of an automatic approval decision-making device provided by an embodiment of the present invention;

[0025] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The following will describe the implementation methods of the present invention in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that as long as there is no conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other, and the technical solutions formed are all within the protection scope of the present invention.

[0027] In addition, the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be performed in an order different from that shown here.

[0028] Please refer to Figure 1 As shown, the present invention provides an automatic approval decision method, the method comprising:

[0029] S101 constructs one or more sets of feature sets according to the features of the approval object;

[0030] S102: matching the approval factors of the data to be approved with the feature set to obtain a matching result, and analyzing the matching result through a preset accuracy estimation model to obtain approval accuracy;

[0031] S103 compares the approval accuracy with a preset threshold, and converts the feature set into a standard data set according to the comparison result; for example, when the comparison result is greater than the preset threshold, it means that the accuracy meets the requirements, and subsequent approval can be carried out at this time;

[0032] S104: Approving the standard data set according to a preset approval strategy to obtain an approval result.

[0033] In the above embodiment, constructing one or more sets of feature sets according to the features of the approval object may include: constructing feature information according to the description information of the features of the approval object, constructing one or more sets of feature sets according to the feature information categories; and defining the priority of the feature sets according to the preset priority parameters. Figure 2 As shown, in actual work, a set describing the features of the approval object can be constructed, which can contain indefinite descriptive information. This set is the feature set. After defining multiple groups of feature sets in sequence or adjusting the priority of the groups, multiple interfaces such as priority, specified group, and specified feature set are provided to access the feature sets.

[0034] Please refer to Figure 3 As shown, in one embodiment of the present invention, matching the approval factors of the data to be approved with the feature set to obtain a matching result includes:

[0035] S301 serializes the approval elements of the approval data to generate approval objects;

[0036] S302 obtains a matching result by matching the feature set with the approval object.

[0037] Specifically, in actual work, the relevant elements of the approval object can be serialized for matching and identification, and then the serialized approval object is matched with a specific feature set to obtain a matching result.

[0038] In one embodiment of the present invention, analyzing the matching result through a preset accuracy estimation model to obtain the approval accuracy includes: constructing the accuracy estimation model through one or more accuracy items; wherein the accuracy item includes one or more accuracy items, and the accuracy item is used to evaluate whether the preset conditions of the matching result are met; the accuracy item obtains an accuracy value according to whether the preset conditions of the accuracy item are met; the accuracy estimation model obtains the approval accuracy according to the accuracy value. Specifically, in actual work, the accuracy item is used to describe the specific information when the accuracy is achieved. The accuracy item is composed of one or more accuracy items, and the accuracy item itself represents a "precision value"; based on the current feature set, the estimated approval accuracy is calculated, the highest accuracy value is 100, and the lowest accuracy value is 0, for example: accuracy = F (feature set).

[0039] Please refer to Figure 4 As shown, in one embodiment of the present invention, the approval process of the standard data set by the preset approval strategy to obtain the approval result includes:

[0040] S401 constructs approval policy items according to the preset approval process and the corresponding approval conclusions, and constructs the approval policy through the approval policy items;

[0041] S402 approves the standard data set through a preset approval strategy to obtain an approval conclusion;

[0042] S403 obtains the approval result according to the approval conclusion.

[0043] Specifically, in actual work, the approval policy item is the basic unit that determines the approval process, approval conclusion and other core approval information. The approval strategy is composed of approval policy items and is used to comprehensively describe the automatic approval results. By reading, parsing and transforming the current feature set, a standardized feature set is obtained. The standardized feature set can be directly used to draw approval conclusions, and approval results can be obtained based on the approval conclusions.

[0044] Please refer to Figure 5 As shown, the present invention also provides an automatic approval decision device, which includes a priority grouping module, a conditional decision module and an approval decision module; the priority grouping module is used to construct one or more feature sets according to the features of the approval object; the conditional decision module is used to match the approval elements of the data to be approved with the feature set to obtain a matching result, and analyze the matching result through a preset accuracy estimation model to obtain the approval accuracy; and compare the approval accuracy with a preset threshold, and convert the feature set into a standard data set according to the comparison result; the approval decision module is used to perform approval processing on the standard data set through a preset approval strategy to obtain an approval result.

[0045] In the above embodiment, the priority grouping module includes a feature set unit and a priority definition unit; the feature set unit is used to construct feature information according to the description information of the approval object features, and to construct one or more feature sets according to the feature information category; the priority definition unit is used to define the priority of the feature set according to the preset priority parameter. The approval decision module includes constructing an approval policy item according to the preset approval process and the corresponding approval conclusion, and constructing an approval policy through the approval policy item; approving the standard data set through the preset approval policy to obtain the approval conclusion; and obtaining the approval result according to the approval conclusion. In actual work, the priority grouping module can expand more identifiers according to actual needs and give corresponding groupings to achieve approval requirements for different purposes; and the approval decision module can be further subdivided into multiple approval units, which are used to add additional attributes to the approval process and conclusion, such as adding descriptive information, approval amount, etc. in the approval conclusion; relevant technical personnel in this field can select the corresponding functional unit according to actual needs, and the present invention does not make any limitation here.

[0046] In one embodiment of the present invention, the conditional decision module may include a matching unit, which is used to serialize the approval elements of the approval data to generate an approval object; the matching result is obtained by matching the feature set with the approval object; further, the conditional decision module may also include an accuracy estimation unit, which is used to construct the accuracy estimation model through one or more accuracy items; wherein the accuracy item includes one or more accuracy items, and the accuracy item is used to evaluate whether the preset conditions of the matching result are met; the accuracy item obtains the accuracy value according to whether the preset conditions of the accuracy item are met; the accuracy estimation model obtains the approval accuracy according to the accuracy value.

[0047] The beneficial technical effect of the present invention is that by adopting priority grouping and combining conditional decision-making and other modules, approval of complex scenarios can be achieved, such as situations where there are many approval factors. Approval can be completed automatically and efficiently. For situations where the accuracy is not enough for automatic approval, manual approval can be transferred to ensure the quality of approval.

[0048] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0049] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above method.

[0050] like Figure 6 As shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily have to include Figure 6 In addition, the electronic device 600 may also include Figure 6 For components not shown, reference may be made to the prior art.

[0051] like Figure 6 As shown, the central processor 100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processor 100 receives inputs and controls the operations of various components of the electronic device 600.

[0052] The memory 140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 100 may execute the program stored in the memory 140 to implement information storage or processing.

[0053] The input unit 120 provides input to the CPU 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0054] The memory 140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 600 through the central processor 100.

[0055] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0056] The communication module 110 is a transmitter / receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter / receiver) 110 is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0057] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby realizing a common telecommunication function. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the central processor 100, so that the sound can be recorded on the local machine through the microphone 132, and the sound stored on the local machine can be played through the speaker 131.

[0058] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more groups of computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0062] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An automatic approval decision method, characterized in that: The method comprises: Construct one or more sets of feature sets based on the features of the approval object; Matching the approval factors of the data to be approved with the feature set to obtain a matching result, and analyzing the matching result through a preset accuracy estimation model to obtain approval accuracy; Comparing the approval accuracy with a preset threshold, and converting the feature set into a standard data set according to the comparison result; Performing approval processing on the standard data set through a preset approval strategy to obtain an approval result; Analyzing the matching results through a preset accuracy estimation model to obtain approval accuracy includes: Constructing the accuracy estimation model through one or more accuracy items; The precision entry includes one or more precision items, and the precision items are used to evaluate whether the preset conditions of the matching result are met; The precision entry obtains a precision value according to whether the preset condition of the precision item is satisfied; The accuracy estimation model obtains the approval accuracy according to the accuracy value; Constructing one or more sets of feature collections based on the features of the approval object includes: constructing feature information based on the description information of the features of the approval object, and constructing one or more sets of feature collections based on the feature information category; defining the priority of the feature collection according to a preset priority parameter; and after defining or adjusting the priority of the groups of feature collections in sequence, providing interfaces for subsequent access by priority, specified group, and specified feature collection.

2. The automatic approval decision-making method according to claim 1 is characterized in that: Matching the approval elements of the data to be approved with the feature set to obtain a matching result includes: Serialize the approval elements of the approval data to generate approval objects; A matching result is obtained by matching the approval object with the feature set.

3. The automatic approval decision-making method according to claim 1 is characterized in that: Approving the standard data set by using a preset approval strategy to obtain an approval result includes: Constructing approval policy items according to the preset approval process and the corresponding approval conclusions, and constructing the approval policy through the approval policy items; Approving the standard data set through a preset approval strategy to obtain an approval conclusion; The approval result is obtained according to the approval conclusion.

4. An automatic approval decision-making device, characterized in that: The device comprises a priority grouping module, a condition decision module and an approval decision module; The priority grouping module is used to construct one or more sets of feature sets according to the features of the approval object; The conditional decision module is used to match the approval factors of the data to be approved with the feature set to obtain a matching result, analyze the matching result through a preset accuracy estimation model to obtain the approval accuracy; and compare the approval accuracy with a preset threshold, and convert the feature set into a standard data set according to the comparison result; The approval decision module is used to perform approval processing on the standard data set through a preset approval strategy to obtain an approval result; The conditional decision module includes an accuracy estimation unit, which is used to construct the accuracy estimation model through one or more accuracy items; wherein the accuracy item includes one or more accuracy items, and the accuracy item is used to evaluate whether the preset conditions of the matching result are met; the accuracy item obtains an accuracy value according to whether the preset conditions of the accuracy item are met; the accuracy estimation model obtains the approval accuracy according to the accuracy value; The priority grouping module includes a feature set unit and a priority definition unit; the feature set unit is used to construct feature information according to the description information of the approval object feature, and to construct one or more feature sets according to the feature information category; The priority definition unit is used to define the priority of the feature set according to preset priority parameters, and after defining or adjusting the priority of multiple groups of feature sets in sequence, provide priority, specified group, and specified feature set interfaces for subsequent access.

5. The automatic approval decision-making device according to claim 4 is characterized in that: The conditional decision module includes a matching unit, which is used to perform serialization processing on the approval elements of the approval data to generate an approval object; and obtain a matching result by matching the feature set through the approval object.

6. The automatic approval decision-making device according to claim 5, characterized in that: The approval decision module includes: constructing approval policy items according to preset approval processes and corresponding approval conclusions, and constructing approval policies through the approval policy items; approving the standard data set through the preset approval policy to obtain approval conclusions; and obtaining approval results according to the approval conclusions.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for causing a computer to execute the method according to any one of claims 1 to 3.

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