Business approval method and system based on different crowds

Abstract

The invention discloses a business approval method and system based on different crowds, and the method comprises the steps: initiating an approval request based on creators, the creators comprising static crowds and dynamic crowds, and the approval request comprising different business information and to-be-approved contents of different crowds; providing corresponding preset examination and approval templates based on different business information, distributing a plurality of examination and approval nodes for each examination and approval template, and formulating a corresponding examination and approval rule for each examination and approval node; and matching the to-be-approved content with the corresponding approval rule, and determining an approval result according to a matching result. According to the method, activity approval can be initiated for different crowds, and the problems that in the prior art, a manual processing mode is low in processing efficiency and cannot be effectively controlled are solved.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and particularly relates to a business approval method and system based on different populations. Background Art

[0002] A workflow approval system for sending data extraction based on different populations is a system for approving and automatically executing various internal operation activities of a company, used to record, track, approve, and execute activity tasks. The general situation of some operation activities is that the applicant applies for the activity content through a communication system such as DingTalk, and after being approved by the relevant person in charge, it is manually executed by the relevant person in charge. The manual processing efficiency of the executor is low, and it is difficult to conduct approval statistics and risk control. Activities applied for during non-working hours cannot be processed in a timely manner. It can be seen that the manual processing mode in the prior art has the defects of low processing efficiency and limited processing time. Summary of the Invention

[0003] (I) Object of the Invention

[0004] The object of the present invention is to provide a business approval method and system based on different populations, which can realize the initiation and processing of activity approvals for different populations, and solve the problems of low processing efficiency and ineffective control existing in the manual processing mode in the prior art.

[0005] (II) Technical Solution

[0006] To solve the above problems, the first aspect of the present invention provides a business approval method based on different populations, which includes:

[0007] Based on the approval request initiated by the creator, the creator includes static populations and dynamic populations, and the approval request includes different business information and content to be approved for different populations;

[0008] Provide corresponding preset approval templates based on different business information,

[0009] Allocate multiple approval nodes for each approval template, and formulate corresponding approval rules for each approval node;

[0010] Match the content to be approved with the corresponding approval rules, and determine the approval result according to the matching result.

[0011] Further, the determining the approval result according to the matching result includes:

[0012] If at any node, the content to be approved does not match the corresponding approval rule, the approval fails;

[0013] If at all nodes, the content to be approved matches the corresponding approval rules, the approval passes and the approval result is sent to a third-party interface.

[0014] Further, the business information of the static population includes: personal ID number information, personal mobile phone number information, personal attribute information, and personal behavior information.

[0015] Further, the business information of the dynamic population includes: domestic service business and driving service business.

[0016] Further, the static population and the dynamic population are determined through Redis cache retrieval.

[0017] Further, data transmission is performed between the approval nodes based on the MQ message queue.

[0018] Further, the personal attribute information includes registration time, registration source, referral type, account balance, total recharge amount, total recharge times, first order completion time, last order placement time, historical completed order volume, historical cancelled order volume, historical used coupon order volume, and last login time.

[0019] Further, the personal behavior information includes: order placement volume, completed order volume, cancelled order volume, used coupon order number, recharge amount, referred user number, proxy purchase order number, proxy delivery order number, and queued order number.

[0020] In addition, a second aspect of the present invention provides a business approval system based on different populations, and the system includes:

[0021] An approval request initiation module, configured to initiate an approval request based on a creator, where the creator includes a static population and a dynamic population, and the approval request includes different business information and content to be approved for different populations;

[0022] A template providing module, configured to provide corresponding preset approval templates based on different business information;

[0023] An approval rule formulation module, configured to allocate multiple approval nodes for each approval template and formulate corresponding approval rules for each approval node;

[0024] An approval result determination module, configured to match the content to be approved with the corresponding approval rules and determine the approval result according to the matching result.

[0025] Further, the approval result determination module is used for:

[0026] If at any node, the content to be approved does not match the corresponding approval rule, the approval fails;

[0027] If at all nodes, the content to be approved matches the corresponding approval rules, the approval passes and the approval result is sent to a third-party interface.

[0028] (3) Beneficial Effects

[0029] The above technical solution of the present invention has the following beneficial technical effects: The present invention provides a business approval method and system based on different populations. The method is based on the approval request initiated by the creator, where the creator includes static populations and dynamic populations. The populations are accurately classified and positioned according to different dimensions and dimension values to achieve more targeted approval processing. Corresponding preset approval templates are provided based on different business information, multiple approval nodes are assigned to each approval template, and corresponding approval rules are formulated for each approval node; the approval nodes determine the flow of approval and are the core of the approval process, and an automatic approval process is realized through preset conditions and rules. Finally, the content to be approved is matched with the corresponding approval rules, and the approval result is determined according to the matching result. After the approval is passed, each business logic is automatically processed, such as sending coupons, text messages, advertising displays, etc. For example, sending coupons: accurately matching the population to the target users; the approver checks the specific amount and other information of the coupon through the details to determine whether it can be sent; after the approval is passed, the coupon sending interface is called to automatically send text messages to the target users. The present invention realizes many advantages such as improving work efficiency, effectively managing approval content, and automatically executing after the approval is passed. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flowchart of a business approval method based on different populations of the present invention;

[0031] Figure 2 is a flowchart of the business approval method of an embodiment of the present invention;

[0032] Figure 3 Schematic diagram of a business approval system based on different populations of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0034] As Figure 1 shown, the first aspect of the present invention provides a business approval method based on different populations, including:

[0035] S1. Based on the approval request initiated by the creator, the creator includes static groups and dynamic groups, and the approval request includes different business information and content to be approved for different groups. Creating groups is a fundamental and important function: The creator includes static groups and dynamic groups. The people in the static group are fixed and unchanged. By matching big data statistics, a fixed user set is generated. The business information of the static group includes: personal ID number information, personal mobile phone number information, personal attribute information, and personal behavior information. For the static group, the ID or mobile phone number can be directly specified, or only the attribute conditions or behavior conditions of the user can be added. The static group queries the corresponding user set according to the screening conditions created by the creator, and the approval action only takes effect on the users in the group. For example, for Group 1 in the static group, a text message approval is initiated. After the approval is passed, the text message is sent to the users defined in the group. Personal attribute information includes registration time, registration source, recommended type, account balance, total recharge amount, total recharge times, first order completion time, last order placement time, historical completed order volume, historical cancelled order volume, historical coupon usage order volume, and last login time. The completed order volume is the number of orders completed per day, and the historical completed order volume is the total number of orders completed by the user from registration to the present. The same applies to cancelled orders and historical cancelled order volumes. Personal behavior information includes: order placement volume, completed order volume, cancelled order volume, coupon usage order number, recharge amount, recommended user number, proxy purchase order number, proxy delivery order number, and queued order number. The queued order number refers to the completed queued orders. The dynamic group is queried in real time during use. The business information of the dynamic group includes: domestic service business and driving service. Similarly, a user set that meets the requirements is matched according to user attributes and user behavior. The groups are accurately classified and positioned according to different dimensions and dimension values to achieve more targeted approval processing. Targeted means screening out target users through the group. For example, if the operation wants to increase the order placement volume of users, a group of users who have not placed orders for n days can be created to send coupons to a certain batch of inactive users to stimulate consumption. Through data analysis and mining: Using data analysis tools and technologies to deeply explore user behavior data, understand user characteristics and preferences, so as to carry out refined operations targeted. The initiator launches activities according to specific groups. The static group and the dynamic group are determined through Redis cache retrieval and screened and matched through the user behavior and user attributes created by the group. The static group queries and matches the attributes and behaviors of users in the big data summary through sql, such as the order placement volume of users per day and for life, etc., to define users; the dynamic group calls the interface in real time through the action corresponding to the specific business interface for query and matching.

[0036] S2. Provide corresponding preset approval templates based on different business information.

[0037] S3. Assign multiple approval nodes to each approval template and formulate corresponding approval rules for each approval node. Each node can be configured with specific cities, city attributes, or approvers and carbon copy recipients across the country, or it can be configured to skip cities.

[0038] Each approval node can be configured with different approvers according to the city. If a city does not configure an approver, the node will use the default national approver. The approval node determines the direction of the approval and is the core of the approval process. The approval nodes are based on MQ message queues for data transmission. The group of people and cities initiated when the approval is created are recorded in the approval table. After the approval is successful, some services are completed by directly calling the business interface, and some are completed by asynchronously calling the business interface through MQ messages.

[0039] S4, matching the content to be approved with the corresponding approval rules, and determining the approval result according to the matching result. The approver of any node can view the submitted detailed content by clicking on the details during the review process. Determining the approval result according to the matching result includes:

[0040] S41, if at any node, the content to be approved does not match the corresponding approval rule, then the approval is rejected;

[0041] S42, if at all nodes, the content to be approved and the corresponding approval rules match, then the approval is passed and the approval result is sent to the third-party interface. Approval means that the specified conditions or standards are met, and approval rejection means that the conditions are not met or there is a risk. After approval, each business logic is automatically processed, such as sending coupons, text messages, advertising displays, etc. Approval business management is the top-level application embodiment of the entire system. The current approval categories include sending text messages, sending coupons, sending advertisements, sending in-site letters, sending push, extracting data, batch issuing U points and U coins, etc. According to business needs, more approval types can be added flexibly and quickly. For detailed approval process, please refer to Figure 2 shown.

[0042] In a preferred embodiment, in the above step S41, for the i-th approval node, its score S i The calculation is as follows:

[0043]

[0044] In formula (1), M is the total number of rule sub-conditions contained in the node, and W is ij is the weight of the jth sub-condition C ij is the matching value of the content to be approved on the jth sub-condition (0-1, 0 for no match, 1 for complete match), f(C ij ) is a nonlinear conversion function (used to clarify the critical point between matching and mismatching, and convert the continuous matching value C ij(0-1) conversion to a binary score of "close to 0 or 1" to avoid the "fuzzy scoring" caused by traditional linear weighting. For example, "55% match" and "60% match" may represent essential differences in business, and a significant distinction can be made through a non-linear function. The calculation formula is:

[0045]

[0046] f(C ij ) is used to strengthen the boundary conditions (k is the slope factor, for example, k = 10 can be defaulted), α is the penalty coefficient (α≥0), which is used to impose additional penalties on sub-conditions with low match values, and β is the gain coefficient (β≥0), which is used to exponentially reward sub-conditions with high match values;

[0047] If the score S of a certain node i i <T i , T i is the threshold of node i, then a veto is directly triggered and the approval fails;

[0048] In the above, the type of each rule sub-condition can be flexibly set. For example, in the scenario of approving the qualifications of a designated driver, the sub-conditions can include driving age, complaint rate in the past 30 days, and service area coverage rate, etc. Another example is in the scenario of approving user coupon issuance, the sub-conditions can include the success rate of historical coupon usage and account balance, etc.

[0049] In another preferred embodiment, in the above step S42, after all nodes pass, the global total score S is calculated total :

[0050]

[0051] In formula (3), N is the total number of approval nodes, and W i is the global weight of the i-th node

[0052] If S total ≥T total (T total is the global threshold), then the approval passes, otherwise, the approval fails.

[0053] Of course, the above way of matching the content to be approved with the corresponding approval rules is only exemplary. Those skilled in the art can also choose other matching methods, such as the cosine similarity matching method, etc. Such a change in the matching method does not constitute any limitation to the present invention.

[0054] As Figure 3 shown, the second aspect of the present invention provides a business approval system based on different populations, and the system includes:

[0055] The approval request initiation module 21 is used to initiate an approval request based on the creator, where the creator includes a static population and a dynamic population, and the approval request includes different business information and content to be approved for different populations;

[0056] The template providing module 22 is used to provide corresponding preset approval templates based on different business information;

[0057] The approval rule formulation module 23 is used to allocate multiple approval nodes for each approval template and formulate corresponding approval rules for each approval node;

[0058] The approval result determination module 24 is used to match the content to be approved with the corresponding approval rules and determine the approval result according to the matching result.

[0059] Further, the approval result determination module 24 is used to:

[0060] If at any node, the content to be approved does not match the corresponding approval rule, the approval fails;

[0061] If at all nodes, the content to be approved matches the corresponding approval rules, the approval passes and the approval result is sent to the third - party interface.

[0062] The embodiments of the present invention have been described above with reference to the embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. The scope of the present invention is defined by the appended claims and their equivalents. Without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and these substitutions and modifications should fall within the scope of the present invention. Although the embodiments of the present invention have been described in detail, it should be understood that various changes, substitutions, and alterations can be made to the embodiments of the present invention without departing from the spirit and scope of the present invention. Obviously, the above embodiments are merely examples for clear illustration and are not limitations on the embodiments. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to enumerate all the embodiments here. And the obvious changes or variations derived therefrom are still within the protection scope of the present invention. Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code. The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows 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 the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks. These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the functions specified in one process Figure 1One process or multiple processes and / or boxes Figure 1 Steps of the functions specified in one box or multiple boxes. Those of ordinary skill in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM for short), or a random access memory (RAM for short), etc. The steps in the methods of the embodiments of the present invention can be adjusted, combined, and deleted according to actual needs. The modules in the systems of the embodiments of the present invention can be combined, divided, and deleted according to actual needs.

Claims

1. A business approval method based on different populations, characterized in that, Including: Based on the creator initiating an approval request, the creator includes a static population and a dynamic population, and the approval request includes different business information and content to be approved for different populations; Providing corresponding preset approval templates based on different business information; Allocating multiple approval nodes for each approval template and formulating corresponding approval rules for each approval node; Matching the content to be approved with the corresponding approval rules and determining the approval result according to the matching result.

2. The business approval method based on different populations according to claim 1, wherein The determining the approval result according to the matching result includes: If at any node, the content to be approved does not match the corresponding approval rule, the approval fails; If at all nodes, the content to be approved matches the corresponding approval rules, the approval passes and the approval result is sent to the third-party interface.

3. The business approval method based on different populations according to claim 1, wherein The business information of the static population includes: personal ID number information, personal mobile phone number information, personal attribute information, and personal behavior information.

4. The business approval method based on different populations according to claim 1, characterized in that The business information of the dynamic population includes: domestic service business and driving service business.

5. The business approval method based on different populations according to claim 1, wherein The static population and the dynamic population are determined through Redis cache retrieval.

6. The business approval method based on different populations according to claim 1, wherein Data transmission is carried out between the approval nodes based on the MQ message queue.

7. The business approval method based on different populations according to claim 3, wherein The personal attribute information includes registration time, registration source, referral type, account balance, total recharge amount, total recharge times, first order completion time, last order placement time, historical completed order volume, historical cancelled order volume, historical coupon-using order volume, and last login time.

8. The business approval method based on different populations according to claim 3, wherein The personal behavior information includes: order placement volume, completed order volume, cancelled order volume, coupon-using order number, recharge amount, referred user number, proxy purchase order number, proxy delivery order number, and queued order number.

9. A business approval system based on different populations, characterized in that The system includes: An approval request initiation module, used to initiate an approval request based on the creator, the creator includes a static population and a dynamic population, and the approval request includes different business information and content to be approved for different populations; A template providing module, used to provide corresponding preset approval templates based on different business information; An approval rule formulation module, used to allocate multiple approval nodes for each approval template and formulate corresponding approval rules for each approval node; An approval result determination module, used to match the content to be approved with the corresponding approval rules and determine the approval result according to the matching result.

10. The business approval system based on different populations according to claim 9, characterized in that, The approval result determination module is used for: If at any node, the content to be approved does not match the corresponding approval rule, the approval fails; If at all nodes, the content to be approved matches the corresponding approval rules, the approval passes and the approval result is sent to the third-party interface.

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