A digital application system and method based on low-code process engine

Abstract

The present invention provides a digital application system and method based on a low-code process engine, which belongs to the field of data processing technology, and specifically includes: determining the associated input data of different output data based on business needs, and using it as associated input data, and determining the data processing logic of the associated input data of different output data according to the associated input data of different output data, determining the historical similar scenarios matching the data processing logic through the data processing logic, and determining the data processing reliability of different data processing logics and the operating reliability of business needs according to the operating data under the historical similar scenarios; when the operating reliability of the business needs meets the requirements, obtaining matching low-code modules of different data processing logics, and recommending the matching low-code modules to users through the low-code process engine, thereby further improving the reliability of the application.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular, relates to a digital application system and method based on a low-code process engine. Background Art

[0002] Under the premise of rapid development of digitalization and intelligence, more and more companies are facing the pressure of digital transformation. However, since the threshold for writing software codes for related digital software is relatively high, how to lower the threshold for writing software codes and reduce the difficulty of digital transformation of enterprises has become a technical problem that needs to be solved urgently.

[0003] In order to solve the above technical problems, the existing technical solutions integrate data processing logic to form a low-code module, and automatically generate relevant digital systems according to business processing requirements and low-code modules. Specifically, the invention patents CN202310899398.6 "A method and system for automatic mining and reuse of low-code templates" and CN202310671511.5 "Industrial data processing method, device and equipment for low-code development platform" both provide similar processing methods, but there are the following technical problems:

[0004] Due to the large number of low-code modules, enterprise users are not familiar enough with the code logic of different low-code modules. At the same time, there are large differences in the input parameters and output parameters of different low-code modules. Therefore, if recommendations cannot be made based on the matching of different low-code modules with the current business scenarios, it may lead to difficulty in meeting the processing efficiency and accuracy.

[0005] In response to the above technical problems, the present invention provides a digital application system and method based on a low-code process engine. Summary of the invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a digital application method based on a low-code process engine is provided.

[0008] A digital application method based on a low-code process engine, characterized by comprising:

[0009] S1 obtains the business needs of the user, and determines the input data and output data of the business needs according to the business needs, determines the similarity between the business needs and the historical business needs processed by the low-code process engine through the input data and output data of the business needs, and when it is determined based on the similarity that the low-code process engine can meet the construction of the business needs, proceeds to the next step;

[0010] S2 determines the input data associated with different output data based on the business requirements, uses the associated input data as the associated input data, and determines the data processing logic of the associated input data of different output data according to the associated input data of different output data;

[0011] S3 determines the historical similar scenarios matched by the data processing logic through the data processing logic, and determines the data processing reliability of different data processing logics and the operational reliability of business requirements according to the operational data under the historical similar scenarios. When the operational reliability of the business requirements meets the requirements, proceed to the next step;

[0012] S4 obtains matching low-code modules for different data processing logics, and recommends the matching low-code modules to users through the low-code process engine.

[0013] The beneficial effects of the present invention are:

[0014] 1. In the present invention, whether the low-code process engine can meet the construction of business needs is determined based on similar situations, thereby realizing the similarity between historical business needs and business needs, and realizing the identification of the inability of the low-code process engine to build business needs, avoiding the waste of user usage time due to the inability of the low-code process engine to meet business needs, and improving the efficiency of processing business needs.

[0015] 2. In the present invention, the data processing reliability of different data processing logics and the operational reliability of business needs are determined based on the operating data under similar historical scenarios, thereby realizing the evaluation of the data processing reliability and the operational reliability of business needs from the operating data of the data processing logic, avoiding the occurrence of technical problems such as processing reliability not meeting the requirements due to insufficient reliability of the software system built with the low-code module.

[0016] A further technical solution is that the business needs of the user are determined by utilizing the low-code process engine to parse the description of the business needs of the user.

[0017] A further technical solution is that the similar situations include the similarity between the historical business demand and the business desire and the historical business demand whose similarity is greater than a preset similarity.

[0018] A further technical solution is that the matching low-code module of the data processing logic is determined according to the data processing methods corresponding to different data processing times of the data processing logic, and specifically, the corresponding low-code module is determined according to the data processing methods for different data processing times.

[0019] On the other hand, the present invention provides a digital application system based on a low-code process engine, which adopts a digital application method based on a low-code process engine, and is characterized by specifically including:

[0020] Demand building judgment module, processing logic splitting module, reliability assessment module, recommendation module;

[0021] The demand building judgment module is responsible for obtaining the business needs of users, and determining the input data and output data of the business needs according to the business needs, determining the similarity between the business needs and the historical business needs processed by the low-code process engine through the input data and output data of the business needs, and determining whether the low-code process engine can meet the construction of the business needs based on the similarity;

[0022] The processing logic splitting module is responsible for determining the associated input data of different output data based on the business requirements, and using it as the associated input data, and determining the data processing logic of the associated input data of different output data according to the associated input data of different output data;

[0023] The reliability evaluation module is responsible for determining the historical similar scenarios of the matching of the data processing logic through the data processing logic, and determining the data processing reliability of different data processing logics and the operating reliability of business requirements based on the operating data under the historical similar scenarios;

[0024] The recommendation module is responsible for obtaining matching low-code modules for different data processing logics, and recommending the matching low-code modules to users through the low-code process engine.

[0025] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0028] Figure 1 It is a flowchart of a digital application method based on a low-code process engine;

[0029] Figure 2The present invention is a flow chart of a method for determining data processing reliability of data processing logic. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0031] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0032] Example 1

[0033] To solve the above problems, according to one aspect of the present invention, Figure 1 According to one aspect of the present invention, a digital application method based on a low-code process engine is provided, which is characterized by specifically including:

[0034] S1 obtains the business needs of the user, and determines the input data and output data of the business needs according to the business needs, determines the similarity between the business needs and the historical business needs processed by the low-code process engine through the input data and output data of the business needs, and when it is determined based on the similarity that the low-code process engine can meet the construction of the business needs, proceeds to the next step;

[0035] Furthermore, the business needs of the user are determined by utilizing the low-code process engine to parse the description of the business needs of the user.

[0036] Specifically, the similarity between the business requirement and the historical business requirements processed by the low-code process engine is determined through the input data and output data of the business requirement, specifically including:

[0037] Determine the similarity of the output data of the historical business requirements according to the output data of the business requirements and the similarity quantity of the output data of the historical business requirements;

[0038] Determining the similarity of the input data of the historical business requirements according to the similarity quantity of the input data of the business requirements and the input data of the historical business requirements;

[0039] The similarity between the business demand and the historical business demand of the low-code process engine is determined through the input data similarity and the output data similarity.

[0040] It should be noted that the similar situations include the similarity between the historical business demand and the business desire and the historical business demand with a similarity greater than a preset similarity.

[0041] In one possible embodiment, determining that the low-code process engine can meet the business needs based on the similar situations specifically includes:

[0042] Determine the similarity between the business demand and different historical business demands according to the similarity, determine the historical business demands whose similarity is greater than a preset similarity based on the similarity, and use them as similar business demands;

[0043] The comprehensive similarity between the business requirements and similar business requirements is determined by the number of similar business requirements and the similarity of different similar business requirements. Based on the comprehensive similarity, it is determined whether the low-code process engine can meet the construction of the business requirements.

[0044] It can be understood that determining whether the low-code process engine can meet the construction of the business needs based on the comprehensive similarity specifically includes:

[0045] When the comprehensive similarity is within the preset similarity range, it is determined that the low-code process engine can meet the construction of the business needs.

[0046] In another possible embodiment, determining that the low-code process engine can meet the business needs based on the similar situation specifically includes:

[0047] Determine the similarity between the business demand and different historical business demands based on the similarity, and determine whether the low-code process engine has a historical business demand with a similarity greater than a preset similarity based on the similarity; if so, proceed to the next step and use it as a similar business demand; if not, determine that the low-code process engine cannot meet the construction of the business demand;

[0048] Determine whether there is a similar business requirement with completely identical input data and output data through the input data and output data of the similar business requirement. If so, determine whether the low-code process engine can meet the construction of the business requirement. If not, proceed to the next step.

[0049] Determine the comprehensive similarity between the business demand and similar business demands through the number of similar business demands and the similarity of different similar business demands, and determine whether the low-code process engine can meet the construction of the business demand based on the comprehensive similarity; if so, proceed to the next step; if not, determine that the low-code process engine cannot meet the construction of the business demand;

[0050] The data processing logic between the output data and the associated input data is determined based on the association relationship between the different output data and the associated input data, and the historical similarity scenarios for matching different output data are determined based on the data processing logic between the different output data and the associated input data. When there is output data that does not have a historical similarity scenario, it is determined that the low-code process engine cannot meet the construction of the business needs.

[0051] In another possible embodiment, determining that the low-code process engine can meet the business needs based on the similar situation specifically includes:

[0052] Determine the similarity between the business demand and different historical business demands according to the similarity, determine the historical business demands whose similarity is greater than a preset similarity based on the similarity, and use them as similar business demands;

[0053] Determine the comprehensive similarity between the business demand and similar business demands through the number of similar business demands and the similarity of different similar business demands, and determine whether the low-code process engine can meet the construction of the business demand based on the comprehensive similarity; if so, proceed to the next step; if not, determine that the low-code process engine cannot meet the construction of the business demand;

[0054] Determine the data processing logic between the output data and the associated input data according to the association relationship between the different output data and the associated input data, determine the historical similarity scenarios of the matching of different output data according to the data processing logic between the different output data and the associated input data, and determine the data processing similarity of different output data according to the different historical similar scenarios and the data processing logic between the output data and the associated input data and the number of historical similar scenarios;

[0055] The comprehensive data processing similarity is determined by the data processing similarity of different output data, and based on the comprehensive data similarity, it is determined whether the low-code process engine can meet the business needs.

[0056] S2 determines the input data associated with different output data based on the business requirements, uses the associated input data as the associated input data, and determines the data processing logic of the associated input data of different output data according to the associated input data of different output data;

[0057] It should be noted that the determination of the input data associated with different output data based on the business requirements specifically includes:

[0058] The output data is determined according to the business requirement, and the associated input data is determined according to the input data associated with the output result of the output data.

[0059] Furthermore, the data processing logic includes data processing times associated with input data, data processing methods for different data processing times, and associations between different data processing times.

[0060] S3 determines the historical similar scenarios matched by the data processing logic through the data processing logic, and determines the data processing reliability of different data processing logics and the operational reliability of business requirements according to the operational data under the historical similar scenarios. When the operational reliability of the business requirements meets the requirements, proceed to the next step;

[0061] In one possible embodiment, Figure 2 As shown, the method for determining the data processing reliability of the data processing logic is:

[0062] Determine the similarity between different historical similar scenarios and the data processing logic according to the data processing times of the associated input data in different historical similar scenarios, the data processing methods of different data processing times, and the association relationship between different data processing times;

[0063] Dividing the historical similar scenes into different similar scene intervals according to the similarity between the historical similar scenes and the data processing logic, and determining the number of data processing failures and the number of output exceptions in different similar scene intervals according to the operation data under different historical similar scenes in different similar scene intervals;

[0064] The reference weight values ​​of different similar scene intervals are determined by the similarity ranges of different similar scene intervals, and the data processing reliability of the data processing logic is determined in combination with the number of data processing failures and the number of output exceptions of different similar scene intervals.

[0065] It can be understood that the reference weight value of the similar scene interval is related to the similarity range of the similar scene interval, wherein the larger the similarity range of the similar scene interval is, the larger the reference weight value is.

[0066] In another possible embodiment, the method for determining the data processing reliability of the data processing logic is:

[0067] S31 determines the similarity between different historical similar scenarios and the data processing logic according to the data processing times of the associated input data in different historical similar scenarios, the data processing methods of different data processing times, and the association relationship between different data processing times;

[0068] S32 divides the historical similar scenes into different similar scene intervals according to the similarity between the historical similar scenes and the data processing logic, and determines the number of data processing failures and the number of output anomalies in different similar scene intervals according to the operation data of different historical similar scenes in different similar scene intervals, and determines the degree of data processing anomaly in different similar scene intervals according to the number of data processing failures and the number of output anomalies in different similar scene intervals;

[0069] S33 determines the benchmark weight values ​​of different similar scene intervals through the similarity ranges of different similar scene intervals, takes the similar scene intervals whose data processing anomaly degree does not meet the requirements as the processing anomaly interval, and determines the problem evaluation amount of the data processing logic according to the benchmark weight values ​​of the different processing anomaly intervals and the data processing anomaly degree, and judges whether the problem evaluation amount of the data processing logic meets the requirements. If so, proceed to the next step; if not, determine the operation reliability of the business demand through the problem evaluation amount of the data processing logic;

[0070] S34 determines the similar scenarios to be screened based on the basic weight value, and determines the data processing reliability of the data processing logic according to the number of data processing failures and output exceptions in the screened similar scenarios, the number of data processing failures and output exceptions in historical similar scenarios, and the problem evaluation amount of the data processing logic.

[0071] In another possible embodiment, the method for determining the data processing reliability of the data processing logic is:

[0072] S31 determines the similarity between different historical similar scenarios and the data processing logic according to the data processing times of the associated input data in different historical similar scenarios, the data processing methods of different data processing times, and the association relationship between different data processing times;

[0073] S32 divides the historical similar scenes into different similar scene intervals according to the similarity between the historical similar scenes and the data processing logic, and determines the number of data processing failures and the number of output anomalies in different similar scene intervals according to the operation data of different historical similar scenes in different similar scene intervals, and determines the degree of data processing anomaly in different similar scene intervals according to the number of data processing failures and the number of output anomalies in different similar scene intervals;

[0074] S33 determines the base weight values ​​of different similar scene intervals according to the similarity ranges of different similar scene intervals, determines the screening similar scenes based on the base weight values, and judges whether there are screening similar scenes whose data processing abnormality does not meet the requirements. If so, determines the operation reliability of the business requirements according to the number of screening similar scenes whose data processing abnormality does not meet the requirements and the data processing abnormality. If not, proceeds to the next step.

[0075] S34: taking the similar scenario intervals in which the data processing anomaly degree does not meet the requirements as the processing anomaly intervals, and determining the problem evaluation amount of the data processing logic according to the reference weight values ​​of different processing anomaly intervals and the data processing anomaly degrees, and judging whether the problem evaluation amount of the data processing logic meets the requirements, if so, proceeding to the next step, if not, determining the operation reliability of the business requirements through the problem evaluation amount of the data processing logic;

[0076] S35 determines the data processing reliability of the data processing logic based on the number of data processing failures and output exceptions in similar scenarios, the number of data processing failures and output exceptions in similar historical scenarios, and the problem evaluation amount of the data processing logic.

[0077] It should be noted that the method for determining the operational reliability of the business requirement is:

[0078] Determine the data processing logics in different reliability intervals according to the data processing reliabilities of different data processing logics, and determine the operation reliability of different reliability intervals according to the number of data processing logics in different reliability intervals and the data processing reliabilities of different data processing logics;

[0079] The operational reliability of the business requirement is determined according to the operational reliability of different reliability intervals.

[0080] In another possible embodiment, the method for determining the operational reliability of the business requirement is:

[0081] Determine whether there is a data processing logic whose data processing reliability does not meet the requirements through the data processing reliability of different data processing logics. If so, determine that the operation reliability of the business requirement does not meet the requirements. If not, proceed to the next step.

[0082] Determine the data processing logics in different reliability intervals according to the data processing reliabilities of different data processing logics, and determine the operation reliability of different reliability intervals according to the number of data processing logics in different reliability intervals and the data processing reliabilities of different data processing logics;

[0083] Determine whether there is a reliability interval in which the operation reliability does not meet the requirements. If so, determine that the operation reliability of the business demand does not meet the requirements. If not, proceed to the next step.

[0084] The operational reliability of the business requirement is determined according to the operational reliability of different reliability intervals.

[0085] S4 obtains matching low-code modules for different data processing logics, and recommends the matching low-code modules to users through the low-code process engine.

[0086] Specifically, the matching low-code module of the data processing logic is determined according to the data processing method corresponding to different data processing times of the data processing logic, and specifically, the corresponding low-code module is determined according to the data processing method according to different data processing times.

[0087] Example 2

[0088] On the other hand, the present invention provides a digital application system based on a low-code process engine, which adopts a digital application method based on a low-code process engine, and is characterized by specifically including:

[0089] Demand building judgment module, processing logic splitting module, reliability assessment module, recommendation module;

[0090] The demand building judgment module is responsible for obtaining the business needs of users, and determining the input data and output data of the business needs according to the business needs, determining the similarity between the business needs and the historical business needs processed by the low-code process engine through the input data and output data of the business needs, and determining whether the low-code process engine can meet the construction of the business needs based on the similarity;

[0091] The processing logic splitting module is responsible for determining the associated input data of different output data based on the business requirements, and using it as the associated input data, and determining the data processing logic of the associated input data of different output data according to the associated input data of different output data;

[0092] The reliability evaluation module is responsible for determining the historical similar scenarios of the matching of the data processing logic through the data processing logic, and determining the data processing reliability of different data processing logics and the operating reliability of business requirements based on the operating data under the historical similar scenarios;

[0093] The recommendation module is responsible for obtaining matching low-code modules for different data processing logics, and recommending the matching low-code modules to users through the low-code process engine.

[0094] Through the above embodiments, the present application achieves the following technical effects:

[0095] 1. In the present invention, whether the low-code process engine can meet the construction of business needs is determined based on similar situations, thereby realizing the similarity between historical business needs and business needs, and realizing the identification of the inability of the low-code process engine to build business needs, avoiding the waste of user usage time due to the inability of the low-code process engine to meet business needs, and improving the efficiency of processing business needs.

[0096] 2. In the present invention, the data processing reliability of different data processing logics and the operational reliability of business needs are determined based on the operating data under similar historical scenarios, thereby realizing the evaluation of the data processing reliability and the operational reliability of business needs from the operating data of the data processing logic, avoiding the occurrence of technical problems such as processing reliability not meeting the requirements due to insufficient reliability of the software system built with the low-code module.

[0097] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0098] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0099] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A digital application method based on a low-code process engine, characterized in that: Specifically include: Obtain the business needs of the user, and determine the input data and output data of the business needs according to the business needs, determine the similarity between the business needs and the historical business needs processed by the low-code process engine through the input data and output data of the business needs, and when it is determined based on the similarity that the low-code process engine can meet the construction of the business needs, proceed to the next step; Determine the input data associated with different output data based on the business requirements, use the associated input data as the associated input data, and determine the data processing logic of the associated input data of different output data according to the associated input data of different output data; Determine the historical similar scenarios that the data processing logic matches through the data processing logic, and determine the data processing reliability of different data processing logics and the operational reliability of business requirements based on the operational data under the historical similar scenarios. When the operational reliability of the business requirements meets the requirements, proceed to the next step. Obtain matching low-code modules for different data processing logics, and recommend the matching low-code modules to users through the low-code process engine; Determine the similarity between the business requirement and the historical business requirements processed by the low-code process engine through the input data and output data of the business requirement, specifically including: Determine the similarity of the output data of the historical business requirements according to the output data of the business requirements and the similarity quantity of the output data of the historical business requirements; Determining the similarity of the input data of the historical business requirements according to the similarity quantity of the input data of the business requirements and the input data of the historical business requirements; Determine the similarity between the business demand and the historical business demand of the low-code process engine through the input data similarity and the output data similarity; Determining the input data associated with different output data based on the business requirements specifically includes: Determine the output data according to the business requirement, and determine the associated input data according to the input data associated with the output result of the output data; The data processing logic includes the data processing times of associated input data, data processing methods for different data processing times, and the association relationship between different data processing times; The method for determining the data processing reliability of the data processing logic is: Determine the similarity between different historical similar scenarios and the data processing logic according to the data processing times of the associated input data in different historical similar scenarios, the data processing methods of different data processing times, and the association relationship between different data processing times; Dividing the historical similar scenes into different similar scene intervals according to the similarity between the historical similar scenes and the data processing logic, and determining the number of data processing failures and the number of output exceptions in different similar scene intervals according to the operation data under different historical similar scenes in different similar scene intervals; The reference weight values ​​of different similar scene intervals are determined by the similarity ranges of different similar scene intervals, and the data processing reliability of the data processing logic is determined in combination with the number of data processing failures and the number of output exceptions of different similar scene intervals.

2. The digital application method based on the low-code process engine according to claim 1, characterized in that: The user's business needs are determined by utilizing the low-code process engine to parse the description of the user's business needs.

3. The digital application method based on the low-code process engine according to claim 1, characterized in that: The similar situations include the similarity between the historical business demand and the business demand and the historical business demand whose similarity is greater than a preset similarity.

4. The digital application method based on the low-code process engine according to claim 1, characterized in that: Determining that the low-code process engine can meet the business needs based on the similar situations specifically includes: Determine the similarity between the business demand and different historical business demands according to the similarity, determine the historical business demands whose similarity is greater than a preset similarity based on the similarity, and use them as similar business demands; The comprehensive similarity between the business requirements and similar business requirements is determined by the number of similar business requirements and the similarity of different similar business requirements. Based on the comprehensive similarity, it is determined whether the low-code process engine can meet the construction of the business requirements.

5. The digital application method based on the low-code process engine according to claim 4, characterized in that: Determining whether the low-code process engine can meet the construction of the business requirements based on the comprehensive similarity specifically includes: When the comprehensive similarity is within the preset similarity range, it is determined that the low-code process engine can meet the construction of the business needs.

6. A digital application system based on a low-code process engine, adopting a digital application method based on a low-code process engine as described in any one of claims 1 to 5, characterized in that: Specifically include: Demand building judgment module, processing logic splitting module, reliability assessment module, recommendation module; The demand building judgment module is responsible for obtaining the business needs of users, and determining the input data and output data of the business needs according to the business needs, determining the similarity between the business needs and the historical business needs processed by the low-code process engine through the input data and output data of the business needs, and determining whether the low-code process engine can meet the construction of the business needs based on the similarity; The processing logic splitting module is responsible for determining the associated input data of different output data based on the business requirements, and using it as the associated input data, and determining the data processing logic of the associated input data of different output data according to the associated input data of different output data; The reliability evaluation module is responsible for determining the historical similar scenarios of the matching of the data processing logic through the data processing logic, and determining the data processing reliability of different data processing logics and the operating reliability of business requirements based on the operating data under the historical similar scenarios; The recommendation module is responsible for obtaining matching low-code modules for different data processing logics, and recommending the matching low-code modules to users through the low-code process engine.

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