Low-code-based online review method and device and medium

Abstract

The invention discloses an online review method and device based on low codes and a medium, and relates to the technical field of online review. The method comprises the steps of obtaining historical project review data, performing deep mining on the review data through a machine learning algorithm, learning a first review index, introducing experts in a corresponding field, and constructing an expert database; constructing an import plate, a review plate and a result plate through a low-code development platform; importing a to-be-reviewed project through an import module, wherein the to-be-reviewed project comprises a project name, a type and expected review time; under the condition that the user does not select, analyzing the to-be-reviewed item through a natural language processing technology, and configuring a review mode through a review module according to the type and the review period; and summarizing the scores of the to-be-reviewed items through a result summarizing section, and generating a review report. According to the method, the efficiency and convenience of expert review are improved through a low-code development platform, and the requirement for rapid, objective and high-quality expert review is met.

Description

Technical Field

[0001] The present application relates to the field of online review technology, and in particular to a low-code-based online review method, device and medium. Background Art

[0002] Traditional expert review methods often rely on manual operations and paper documents, which are inefficient and prone to errors. With the continuous development of information technology, online review systems have gradually become mainstream, and their application in various industries is becoming more and more extensive, but most of the existing online review systems are customized. For example, in scenarios such as government and corporate project reviews and procurement reviews, traditional review methods often rely on offline meetings, paper documents or simple online forms, which have problems such as long review cycles, untimely information synchronization, inconsistent review standards, and difficulty in quantifying review results, which further leads to long development cycles and high costs. At the same time, as business needs continue to change, it is difficult to adapt to changing review needs.

[0003] Through the above analysis, the problems and defects of the prior art are as follows:

[0004] The expert review methods in the prior art often rely on manual operations and paper documents, and the existing online review has a long review cycle for professional content. Summary of the invention

[0005] The embodiments of the present application provide a low-code-based online review method, device and medium, which can solve the problem that the expert review method in the prior art often relies on manual operation and paper documents, and the existing online review has a long review cycle for professional content.

[0006] On the first aspect, an embodiment of the present application provides an online review method based on low code, the method comprising: obtaining historical project review data, deeply mining the review data through a machine learning algorithm, learning a first review indicator, and introducing experts in the corresponding field to build an expert database; constructing an import section, a review section and a result section through a low-code development platform, the review section including expert review and system review; importing projects to be reviewed through the import section, the projects to be reviewed including project name, type and expected review time; if the user has not made a selection, analyzing the projects to be reviewed through natural language processing technology, and configuring the review method through the review section according to the type and review cycle; summarizing project scores through the result summary section to generate a review report.

[0007] In one implementation of the present application, experts in corresponding fields are introduced, specifically including: collecting expert names, contact information, fields and authenticated account information to obtain an expert database, where the fields are used to match project names and types; updating the expert database according to a preset cycle, including adding new experts, deleting invalid experts, and updating expert contact information; grouping experts, and supporting random extraction and targeted extraction.

[0008] In one implementation of the present application, an import section, a review section and a result section are constructed through a low-code development platform, specifically including: setting rule import, template import, and single item addition import through a low-code development platform; assigning a unique identifier to each project to be reviewed and associating it with an expert library.

[0009] In one implementation of the present application, the method also includes: the correspondence between experts and the projects to be reviewed is one-to-many, many-to-one or many-to-many; a function of whether the project to be reviewed is anonymous is provided, and anonymity includes anonymity before evaluation; based on expert review, a second review indicator is collected, and the review indicators include online result feedback, show of hands voting, online scoring, expert research resolution, and a custom configuration interface is provided for the second review indicator; based on system review, the format of the uploaded materials is converted, the data is standardized, and evaluation is performed according to the review indicators.

[0010] In one implementation of the present application, project scores are summarized and a review report is generated, specifically including: designing a result summary interface, and displaying the evaluation results of expert review opinions through visualization tools provided by the low-code platform; automatically summarizing review opinions and generating a review report based on configuration indicators; providing an export function for the review report to record the evaluation results.

[0011] In one implementation of the present application, if the user has not made a selection, the review method is configured through the review section according to the type and review cycle, and the review is conducted, specifically including: if matched to expert review, and if matched to multiple experts, online experts have a higher priority than offline experts based on the expected review time; a review invitation is sent to the selected expert, containing basic project information and deadline; if matched to expert review and system review, the scores of the expert review and system review are automatically summarized to calculate the comprehensive score of the project.

[0012] In one implementation of the present application, after aggregating the project scores through the result summary section and generating a review report, the method also includes: real-time monitoring and analysis of the review data through a data analysis tool, and providing a user feedback channel to collect the project name, type and review cycle of the projects to be reviewed, as well as the user's customized selections, and establish a database.

[0013] In one implementation of the present application, the method further includes: integrating real-time communication tools to enable real-time communication between experts; and providing notification and reminder functions to notify users when review rules are changed.

[0014] In the second aspect, an embodiment of the present application also provides an online review device based on low code, the device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain historical project review data, conduct in-depth mining of the review data through a machine learning algorithm, learn a first review indicator, and introduce experts in the corresponding field to build an expert database; construct an import section, a review section and a result section through a low-code development platform, the review section including expert review and system review; import projects to be reviewed through the import section, the projects to be reviewed include project name, type and expected review time; if the user has not made a selection, analyze the projects to be reviewed through natural language processing technology, and configure the review method through the review section according to the type and review cycle; summarize the project scores through the result summary section to generate a review report.

[0015] On the third aspect, the embodiment of the present application also provides a low-code-based online review non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are set to: obtain historical project review data, conduct in-depth mining of the review data through a machine learning algorithm, learn the first review indicator, and introduce experts in the corresponding field to build an expert database; construct an import section, a review section and a result section through a low-code development platform, and the review section includes expert review and system review; import projects to be reviewed through the import section, and the projects to be reviewed include project name, type and expected review time; if the user has not made a selection, analyze the projects to be reviewed through natural language processing technology, and configure the review method through the review section according to the type and review cycle; summarize the project scores through the result summary section to generate a review report.

[0016] The embodiments of the present application provide a low-code-based online review method, device and medium, which improve the efficiency and convenience of expert review through a low-code development platform, meet the needs of fast, objective and high-quality expert review, lower the development threshold of the review system, improve the flexibility and scalability of the system, and ensure the accuracy and objectivity of the review results through automated processing and online real-time monitoring; quickly build a review system through a low-code development platform, reduce development costs and time, and improve review efficiency; reduce labor costs: through automated and intelligent processing, reduce the travel, time and other costs of experts, and experts can conduct reviews at any time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A flowchart of a low-code-based online review method provided in an embodiment of the present application;

[0019] Figure 2 A business architecture diagram of a low-code-based online review method provided in an embodiment of the present application;

[0020] Figure 3 A schematic diagram of a configuration review method of a low-code-based online review method provided in an embodiment of the present application;

[0021] Figure 4 A schematic diagram of the internal structure of a low-code-based online review device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0023] The embodiments of the present application provide a low-code-based online review method, device and medium, which solve the problem that the expert review method in the prior art often relies on manual operation and paper documents, and the existing online review has a long review cycle for professional content.

[0024] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0025] Figure 1 A flowchart of a low-code-based online review method provided in an embodiment of the present application. Figure 1 As shown, an online review method based on low code provided in an embodiment of the present application specifically includes the following steps:

[0026] Step 10: Obtain historical project review data, conduct in-depth mining of the review data through machine learning algorithms, learn the first review indicator, and introduce experts in the corresponding fields to build an expert database.

[0027] In this step, you can use data crawling tools or API interfaces to obtain historical project review data from designated sources, and use statistical analysis and visualization methods such as scatter plots, histograms, and box plots to explore the distribution, trends, and relationships of the data; for example, use project type, reviewer experience, and review time as features, perform text vectorization on the review opinions, and then input them into the machine learning model. Select the random forest algorithm as the classification model, use the training data set to train the model, and extract the first review indicator from the trained model through cross-validation, including the degree of influence of project type, reviewer experience, review time, etc. on the project review results.

[0028] As an optional embodiment, introducing experts in corresponding fields can specifically include: Step 101: collecting expert names, contact information, fields and authenticated account information to obtain an expert database, where the fields are used to match project names and types; Step 102: updating the expert database according to a preset cycle, including adding new experts, deleting invalid experts, and updating expert contact information; Step 103: grouping experts, and supporting random extraction and directional extraction.

[0029] In this step, the main purpose of the expert database, the required professional fields and the scope of professional knowledge are clarified, and relevant information of experts is collected through internal surveys, external recruitment, and professional associations, including the expert's name, professional field, work experience, educational background, and research results. The collected expert information is evaluated and experts are contacted regularly; experts are grouped according to their fields, and within each group, a random extraction mechanism is implemented to ensure that each extraction is random; targeted extraction is to extract experts based on specific conditions or requirements, including the expert's professional field, experience level, and past work performance.

[0030] Step 20: Figure 2 As shown, the import section, review section and result section are constructed through the low-code development platform. The review section includes expert review and system review.

[0031] As an optional embodiment, constructing an import section, a review section and a result section through a low-code development platform may specifically include: Step 201: setting rule import, template import, and single item addition import through a low-code development platform; Step 202: assigning a unique identifier to each project to be reviewed and associating it with an expert library.

[0032] In this step, rules are imported, for example, each row represents a supplier, and each column represents a field (name, contact information, address). For example, the name cannot be empty, and the contact information must be a valid email or phone number. Template import provides an Excel template for users to fill in and upload for import. Single item addition import uses the form submission function provided by the platform to implement the logic of adding single data. When the user fills in the form and submits it, the platform automatically inserts the data into the database table. A unique identifier is assigned to each project to be reviewed, and it is associated with the expert database for the purpose of tracing the project.

[0033] As an optional embodiment, the method may further include: Step 203: the corresponding relationship between the experts and the reviewed items is one-to-many, many-to-one or many-to-many;

[0034] Step 204: providing an anonymous function for the project to be reviewed, including anonymity before evaluation;

[0035] Step 205: Based on the expert review, the second review indicator is collected, and the review indicator includes online result feedback, voting by show of hands, online scoring, expert research and resolution, and a custom configuration interface is provided for the second review indicator.

[0036] In this step, the online result transmission method: the form template required for collection needs to be configured, and the data can be transmitted back according to the results; the voting method: that is, a single expert expresses the review results through two opinions of whether to pass or not, and supports custom assignment of whether or not, and common assignments are "pass / fail" and "agree / disagree", etc. The evaluation result generation rule can select the resolution ratio, and can determine whether the result is hit by greater than or equal to plus a percentage (for example, >=50%, that is, more than half of the pass is passed). The voting supports the configuration of a veto resolution; the online scoring method: that is, through the method of custom score + score summary method + resolution method, the review is carried out, and the hit target project is finally selected. First, it supports custom score ranges, and the common ranges are "0-100 points" and "0-10 points". Secondly, the score summary method is configured, supporting "taking the total score", "taking the highest value", "taking the tail average method (removing one highest value and one lowest value to obtain the average value)", "taking the average value", "removing the high / low average value (removing the scores with large deviation values ​​to obtain the average value)". The resolution method supports selection by quantity, that is, taking the top number or the percentage of the top ranking, and supports selection by score, that is, determining the hit items by adding numbers greater than or equal to. It supports configuring the minimum score value and vetoing with one vote; expert research resolution: that is, through the low-code form configuration tool, configure the expert research form, send it to the expert review center, and the experts will review according to the form content, including the collection and uploading of text, pictures, videos and other elements, and verify according to the form verification rules (such as format verification, time validity period verification, watermark tool verification, etc.). It also supports setting the leader of the expert group, who can veto with one vote.

[0037] Step 206: Based on the system review, the format of the uploaded materials is converted, the data is standardized, and an evaluation is performed according to the review indicators.

[0038] Step 30: Import the projects to be reviewed through the import section. The projects to be reviewed include project name, type and expected review time.

[0039] Step 40: If the user does not make a selection, the items to be reviewed are analyzed using natural language processing technology, and the review method is configured through the review section based on the type and review cycle.

[0040] This step also includes a situation such as Figure 3 As shown, users can configure it themselves.

[0041] As an optional embodiment, when the user has not made a selection, the review method is configured through the review section according to the type and review cycle, and the review is conducted, which may specifically include: Step 401: If matched to expert review, and matched to multiple experts, based on the expected review time, the online expert has a higher priority than the offline expert; Step 402: Sending a review invitation to the selected expert, including basic project information and deadline; Step 403: If matched to expert review and system review, automatically summarizing the scores of the expert review and system review, and calculating the comprehensive score of the project.

[0042] Step 50: Summarize the scores of the items to be reviewed through the result summary section and generate a review report.

[0043] As an optional embodiment, the scores of the projects to be reviewed are summarized through the result summary section to generate a review report, which may specifically include: Step 501: designing a result summary interface, and displaying the evaluation results of expert review opinions through the visualization tools provided by the low-code platform; Step 502: automatically summarizing the review opinions according to the configuration indicators, and generating a review report; Step 503: providing an export function for the review report to record the evaluation results.

[0044] As an optional embodiment, after aggregating the scores of the projects to be reviewed through the result summary section and generating a review report, the method may also include: real-time monitoring and analysis of the review data through a data analysis tool, and providing a user feedback channel to collect the project name, type and review cycle of the projects to be reviewed, as well as the user's customized selection, and establish a database.

[0045] As an optional embodiment, the method may further include: integrating real-time communication tools to enable real-time communication between experts; providing notification and reminder functions to notify users when review rules are changed.

[0046] In this step, the communication between experts is monitored throughout the process.

[0047] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides an online review device based on low code, whose structure is as follows Figure 4 shown.

[0048] Figure 4 A schematic diagram of the internal structure of a low-code online review device provided in an embodiment of the present application. Figure 4 As shown, the device includes:

[0049] at least one processor 401;

[0050] and, a memory 402 communicatively coupled to the at least one processor;

[0051] Among them, the memory 402 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 401 so that at least one processor 401 can: obtain historical project review data, conduct in-depth mining of the review data through a machine learning algorithm, learn the first review indicator, and introduce experts in the corresponding field to build an expert database; construct an import section, a review section and a result section through a low-code development platform, and the review section includes expert review and system review; import projects to be reviewed through the import section, and the projects to be reviewed include project name, type and expected review time; if the user has not made a selection, analyze the projects to be reviewed through natural language processing technology, and configure the review method through the review section according to the type and review cycle; summarize the project scores through the result summary section and generate a review report.

[0052] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium based on low-code online review, storing computer executable instructions, wherein the computer executable instructions are set to: obtain historical project review data, conduct in-depth mining of the review data through a machine learning algorithm, learn the first review indicator, and introduce experts in the corresponding field to build an expert database; construct an import section, a review section and a result section through a low-code development platform, and the review section includes expert review and system review; import projects to be reviewed through the import section, and the projects to be reviewed include project name, type and expected review time; if the user does not make a selection, analyze the projects to be reviewed through natural language processing technology, and configure the review method through the review section according to the type and review cycle; summarize the project scores through the result summary section to generate a review report.

[0053] Each embodiment in this application 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 IoT device and 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 embodiments.

[0054] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0055] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0056] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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.

[0057] 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.

[0058] 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.

[0059] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0060] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0061] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0062] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0063] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A low-code-based online review method, characterized in that: The method comprises: Obtain historical project review data, conduct in-depth mining of the review data through machine learning algorithms, learn the first review indicator, and introduce experts in the corresponding fields to build an expert database; Building an import section, a review section and a result section through a low-code development platform, wherein the review section includes expert review and system review; Import the projects to be reviewed through the import section, where the projects to be reviewed include project name, type and expected review time; If the user does not make a selection, the project to be reviewed is analyzed by natural language processing technology, and the review method is configured through the review section according to the type and review cycle; The scores of the items to be reviewed are summarized through the result summary section to generate a review report.

2. According to the low-code-based online review method of claim 1, it is characterized in that: The experts in the corresponding fields are introduced, including: Collect the expert's name, contact information, field and authenticated account information to obtain an expert database, where the field is used to match the project name and type; Updating the expert database according to a preset period, including adding new experts, deleting invalid experts, and updating expert contact information; Experts are grouped and both random and directed extraction are supported.

3. According to the low-code-based online review method of claim 1, it is characterized in that: The construction of the import section, the review section and the result section through the low-code development platform specifically includes: Set up rule import, template import, and single item addition import through the low-code development platform; A unique identifier is assigned to each project to be reviewed and associated with the expert database.

4. According to the low-code-based online review method of claim 3, it is characterized in that: The method further comprises: The corresponding relationship between the experts and the projects to be reviewed is one-to-many, many-to-one or many-to-many; Provide an anonymous function for the project to be reviewed, including anonymity before evaluation; Based on the expert review, a second review indicator is collected, the review indicator includes online result feedback, voting by show of hands, online scoring, expert research and resolution, and a custom configuration interface is provided for the second review indicator; Based on the system review, the format of the uploaded materials is converted, the data is standardized, and an evaluation is performed according to the review indicators.

5. According to the low-code-based online review method of claim 1, it is characterized in that: The scores of the items to be reviewed are summarized through the result summary section to generate a review report, including: Design a result summary interface and display the evaluation results of the expert review opinions through the visualization tools provided by the low-code platform; According to the configuration indicators, the review opinions are automatically summarized and a review report is generated; Provide an export function for the review report to record the evaluation results.

6. According to the low-code-based online review method of claim 1, it is characterized in that: If the user does not make a selection, the review mode is configured through the review section according to the type and review cycle, and the review is performed, specifically including: If matched to the expert review, and matched to multiple experts, the online expert has a higher priority than the offline expert according to the expected review time; Send review invitations to selected experts, including basic project information and deadlines; If a match is found between expert review and system review, the scores of the expert review and system review will be automatically summarized to calculate the comprehensive score of the project.

7. According to the low-code-based online review method of claim 1, it is characterized in that: After summarizing the project scores through the result summary panel and generating a review report, the method further includes: Use data analysis tools to monitor and analyze review data in real time and provide user feedback channels. The project name, type and review period of the project to be reviewed, as well as the user's customized selection, are collected to establish a database.

8. According to the low-code-based online review method of claim 3, it is characterized in that: The method further comprises: Integrate real-time communication tools to enable real-time communication between experts; Provide notification and reminder functions to inform users when the review rules change.

9. A low-code-based online review device, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Obtain the company's historical project review data, conduct in-depth mining of the review data through machine learning algorithms, learn review indicators, and introduce experts in corresponding fields to build an expert database; Building an import section, a review section and a result section through a low-code development platform, wherein the review section includes expert review and system review; Import the projects to be reviewed through the import section, where the projects to be reviewed include project name, type and expected review time; If the user does not make a selection, the project to be reviewed is analyzed by natural language processing technology, and the review method is configured through the review section according to the type and review cycle; The project scores are summarized through the result summary section to generate a review report.

10. A non-volatile computer storage medium based on low-code online review, storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Obtain historical project review data, conduct in-depth mining of the review data through machine learning algorithms, learn review indicators, and introduce experts in corresponding fields to build an expert database; Building an import section, a review section and a result section through a low-code development platform, wherein the review section includes expert review and system review; Import the projects to be reviewed through the import section, where the projects to be reviewed include project name, type and expected review time; If the user does not make a selection, the project to be reviewed is analyzed by natural language processing technology, and the review method is configured through the review section according to the type and review cycle; The project scores are summarized through the result summary section to generate a review report.

Images