Data evaluation collaboration method and system based on rule engine

Abstract

The invention discloses a data evaluation collaboration method and system based on a rule engine, and relates to the technical field of data processing. The method comprises the following steps: analyzing and identifying a first target information group of a first evaluation mechanism in multiple evaluation mechanisms to obtain first evaluation data of the first evaluation mechanism on a target user; reading a predetermined integration strategy, and performing integration fusion on the first evaluation data according to the predetermined integration strategy to obtain target integration data; displaying the target integrated data through a front-end user interface of the target user to obtain target effective evaluation data; and analyzing the target effective evaluation data in combination with a back-end processing center to generate a target evaluation report. The technical problem that in the prior art, due to the fact that data evaluation lacks a unified standard, the evaluation efficiency and the accuracy of the evaluation result are low is solved, unified analysis of evaluation data of multiple parties is achieved through the rule engine, and the technical effect of improving the evaluation efficiency and accuracy is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data evaluation collaborative method and system based on a rule engine. Background Art

[0002] With the development of modern data processing technology, data evaluation plays a key role in many fields. However, most existing evaluation systems rely on customized programming, which leads to high development costs, poor flexibility, and difficulty in adapting to diverse business needs. In addition, the process of processing evaluation results by traditional systems usually requires manual intervention, which is error-prone and inefficient. When these systems process multi-party evaluation data, they lack unified parsing and integration standards, resulting in inconsistent evaluation results and affecting the accuracy and efficiency of data evaluation. Summary of the invention

[0003] The present application provides a data evaluation collaborative method and system based on a rule engine, which solves the technical problem in the prior art that data evaluation lacks a unified standard, resulting in low evaluation efficiency and accuracy of evaluation results.

[0004] In view of the above problems, the present application provides a data evaluation collaboration method and system based on a rule engine.

[0005] In a first aspect of the present application, a data evaluation collaboration method based on a rule engine is provided, the method comprising: The first target information group of the first evaluation agency among the multiple evaluation agencies is parsed and identified to obtain the first evaluation data of the target user by the first evaluation agency; the predetermined integration strategy is read, and the first evaluation data is integrated and merged according to the predetermined integration strategy to obtain target integrated data; the target integrated data is displayed through the front-end user interface of the target user to obtain target effective evaluation data; the target effective evaluation data is analyzed in conjunction with the back-end processing center to generate a target evaluation report.

[0006] A second aspect of the present application provides a data evaluation collaboration system based on a rule engine, the system comprising: A parsing and identification module, the parsing and identification module is used to parse and identify the first target information group of the first evaluation agency among the multiple evaluation agencies, and obtain the first evaluation data of the target user by the first evaluation agency; a fusion module, the fusion module is used to read a predetermined integration strategy, and integrate and fuse the first evaluation data according to the predetermined integration strategy to obtain target integrated data; a display module, the display module is used to display the target integrated data through the front-end user interface of the target user to obtain target effective evaluation data; an analysis module, the analysis module is used to analyze the target effective evaluation data in conjunction with the back-end processing center to generate a target evaluation report.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, the first target information group of the first evaluation agency among the multiple evaluation agencies is parsed and identified to obtain the first evaluation data of the target user by the first evaluation agency. Next, the predetermined integration strategy is read, and the first evaluation data is integrated and fused according to the predetermined integration strategy to obtain the target integrated data. Further, the target integrated data is displayed through the front-end user interface of the target user to obtain the target effective evaluation data. Finally, the target effective evaluation data is analyzed in conjunction with the back-end processing center to generate a target evaluation report. The technical problem that the lack of unified standards for data evaluation in the prior art leads to low evaluation efficiency and accuracy of evaluation results is solved, and the unified parsing of multi-party evaluation data is realized through the rule engine, thereby achieving the technical effect of improving evaluation efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of a data evaluation collaborative method flow based on a rule engine provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a data evaluation collaborative system based on a rule engine provided in an embodiment of the present application.

[0010] Explanation of the reference numerals: parsing and identification module 11 , fusion module 12 , display module 13 , analysis module 14 . DETAILED DESCRIPTION

[0011] The present application solves the technical problem in the prior art that data evaluation lacks a unified standard, resulting in low evaluation efficiency and accuracy of evaluation results, by providing a data evaluation collaborative method and system based on a rule engine.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than 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 this application.

[0013] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a data evaluation collaboration method based on a rule engine, which is applied to a data evaluation collaboration system based on a rule engine, wherein the data evaluation collaboration system based on a rule engine is associated with multiple evaluation agencies, wherein the method includes: The first target information group of the first evaluation agency among the multiple evaluation agencies is parsed and identified to obtain first evaluation data of the target user by the first evaluation agency.

[0015] The multi-party evaluation agencies share multi-party information through cloud information sharing technology. One evaluation agency is selected from the multi-party evaluation agencies as the first evaluation agency, and the first target information group about the target user provided by the first evaluation agency is identified and determined. The first target information group contains data in various forms, such as text information, evaluation forms, indicator values, etc., covering the preliminary evaluation results of the target user by the evaluation agency. Through in-depth analysis of the first target information group, that is, disassembly and processing of the data, the data is converted from an unstructured or semi-structured state (such as natural language description, PDF file, etc.) to structured data that can be recognized by the system; on the basis of the analysis, data related to the evaluation is identified from the first target information group, including the target user's score, grade, evaluation, feedback, etc. After the analysis and identification are completed, the data obtained and sorted from the first evaluation agency becomes the first evaluation data.

[0016] Further, including: The first target information group includes first text information, first table information and first picture information; first data is obtained based on the first text information; second data is obtained by performing text recognition on the first table information; third data is obtained by identifying preset key points of the first picture; the first data, the second data and the third data constitute the first evaluation data.

[0017] Specifically, the first target information group includes first text information, first table information and first picture information; wherein the first text information refers to the plain text content from the first evaluation agency, which may include written reports, descriptive text or evaluations of the target user; the first table information refers to structured table data, which usually contains a variety of indicators and values, reflecting the quantitative information collected during the evaluation process, such as scores, measurement results, etc.; the first picture information may include images of the target user, captured medical images, scanned images, etc.; for plain text information, it can be parsed through natural language processing (NLP) technology to extract useful evaluation data from it, for example, from a text describing the health status of the target user, extract key health-related indicators, or classify and score certain specific keywords, and the structured data generated by the processed text information is called the first data; for table information The processing of the grid information can be done through OCR (optical character recognition) technology, which can identify and extract the text and values ​​in the table. OCR technology can convert the table in the form of an image into operable digital data; the second data is generated by performing text recognition and data extraction on the information in the table. The second data contains the specific value and corresponding explanation of each field in the table; the first picture information is processed by image recognition, and the information related to the evaluation can be extracted according to the preset key points (such as specific areas, signs, symbols, etc. in the picture); the structured data generated by identifying the key points in the picture is called the third data; the first data, the second data and the third data are integrated to form a complete first evaluation data. The first evaluation data brings together different types of evaluation information, covering text, table and image information, providing a rich and comprehensive foundation for subsequent data fusion and analysis.

[0018] A predetermined integration strategy is read, and the first evaluation data is integrated and fused according to the predetermined integration strategy to obtain target integrated data.

[0019] The predetermined integration strategy is a set of rules set when processing data from multiple assessment agencies to guide how to integrate and merge data from different sources and types. These strategies may include data cleaning rules, data conversion rules, data matching rules, etc. The integration strategy may be formulated based on multiple factors, including business rules, industry standards, customer needs, data types and weights of different assessment agencies, etc.

[0020] According to the cleaning rules in the predetermined integration strategy, the first evaluation data is cleaned, including removing duplicate data, processing missing values, correcting erroneous data, etc.; the first evaluation data is converted into a unified format and standard for subsequent data integration; according to the matching rules in the predetermined integration strategy, data from different sources are matched and associated; on the basis of data cleaning, conversion and matching, according to the fusion rules in the predetermined integration strategy, the first evaluation data is fused to obtain the target integrated data.

[0021] The target integrated data is displayed through the front-end user interface of the target user to obtain target effective evaluation data.

[0022] The front-end user interface is the main platform for interaction between the system and users. It is usually a graphical interface through which users can view, input and operate assessment data. The target user is usually the user of the assessment object or the relevant assessment party, such as the assessed individual, doctor, rehabilitation therapist or manager. Through the front-end interface, the target user can intuitively see the assessment data and results related to him or her.

[0023] Specifically, the target integrated data is converted into a format that can be recognized and displayed by the front-end user interface; the target integrated data is filtered and sorted according to user needs so that users can quickly find key information; the target integrated data is converted into intuitive and easy-to-understand visual information using visualization methods such as charts and graphs; the preprocessed target integrated data is loaded into the front-end user interface, and based on the user's operations, the target effective evaluation data is confirmed and extracted, which are the judgments or decisions made by the user based on the target integrated data.

[0024] Further, including: Acquire second evaluation data, where the second evaluation data refers to data obtained by parsing and identifying a second target information group for evaluating the target user by a second evaluation agency, wherein the second evaluation agency refers to any one of the multiple evaluation agencies that is different from the first evaluation agency; extract a first indicator among predetermined evaluation indicators; match parameters corresponding to the first indicator in the first evaluation data and the second evaluation data in turn, and record them as first indicator parameters and second indicator parameters, respectively; obtain a first decision result of the target user on the first indicator according to a user decision mechanism in the predetermined integration strategy; according to the first decision result, use the first indicator parameter or the second indicator parameter as a first integration parameter of the first indicator; and construct the target effective evaluation data based on the first integration parameter.

[0025] Select a second evaluation agency different from the first evaluation agency from multiple evaluation agencies; obtain the second target information group generated after the second evaluation agency evaluates the target user; parse and identify the second target information group to obtain the second evaluation data. The predetermined evaluation index is a series of parameters or indicators pre-set by the system during the evaluation process, which are used to unify the evaluation standards; select a specific indicator from the predetermined evaluation indicators as the current processing object, recorded as the first indicator; search for specific parameters corresponding to the first indicator in the first evaluation data and the second evaluation data in turn, for example, the first indicator may be the user's heart rate, and the values ​​related to the heart rate will be extracted from the first evaluation data and the second evaluation data respectively; the parameters extracted from different evaluation agencies are recorded as the first indicator parameters (from the first evaluation data) and the second indicator parameters (from the second evaluation data).

[0026] The predetermined integration strategy includes a decision-making mechanism involving users, which allows the target user to choose or judge between multiple parameters. For example, if there are differences in the evaluation results of different evaluation agencies, the user can participate in the decision-making and choose which result is more credible or applicable. The first decision result of the target user on the first indicator is obtained through the front-end user interface or other interactive methods. The first decision result refers to the user selecting the optimal parameter of the first indicator through the decision-making mechanism; according to the user's first decision result, one is selected from the first indicator parameter or the second indicator parameter as the final first integrated parameter. Repeat the above steps, perform similar processing on each indicator in the predetermined evaluation indicators, and obtain a series of integrated parameters; based on these integrated parameters, the target effective evaluation data is formed. The target effective evaluation data contains the integrated results of all predetermined evaluation indicators and is an important basis for subsequent decision-making and analysis.

[0027] Furthermore, it also includes: Determine whether the first indicator belongs to the predetermined basic feature category; if so, activate the user decision mechanism in the predetermined integration strategy, and obtain the first integration parameter of the first indicator according to the user decision mechanism; if not, determine whether the first indicator belongs to the predetermined test feature range; if so, activate the fuzzy decision mechanism in the predetermined integration strategy, and take the average of the first indicator parameter and the second indicator parameter as the first integration parameter of the first indicator according to the fuzzy decision mechanism.

[0028] Specifically, it is determined whether the first indicator belongs to the predetermined basic feature category. These basic features are usually relatively simple, direct and easy to evaluate indicators, such as age, gender, etc. If the first indicator belongs to the predetermined basic feature category, the user decision mechanism in the predetermined integration strategy is activated, and the first integrated parameter of the first indicator is obtained according to the user decision mechanism, that is, the user is allowed to participate in the decision and select the most suitable parameter as the final integrated parameter. If the first indicator does not belong to the predetermined basic feature category, it is further determined whether it belongs to the predetermined test feature range. These test features may involve more complex evaluation criteria or require more professional judgments, such as blood pressure, and its results are not unique and can only be used as a range reference; if the first indicator belongs to the predetermined test feature range, the fuzzy decision mechanism in the predetermined integration strategy is activated. The fuzzy decision allows a certain degree of flexibility and takes a compromise value between multiple data instead of strictly selecting a parameter; under the fuzzy decision mechanism, the mean of the first indicator parameter (from the first evaluation data) and the second indicator parameter (from the second evaluation data) is taken as the final first integrated parameter.

[0029] Further, determining whether the first indicator belongs to a predetermined test feature range includes, if not, displaying the first indicator parameter and the second indicator parameter in a predetermined form as the first integrated parameter.

[0030] If the first indicator belongs neither to the predetermined basic feature category nor to the predetermined test feature range, the first indicator parameter and the second indicator parameter are displayed as a first integrated parameter in a predetermined form (such as a table, a chart, a text, etc.), which means that in this case, a single integrated parameter is not directly calculated, but the parameters provided by the two evaluation agencies are presented to the user at the same time so that they can make a judgment based on the specific situation.

[0031] In conjunction with the back-end processing center, the target effective evaluation data is analyzed to generate a target evaluation report.

[0032] The back-end processing center is used to further analyze the target effective evaluation data displayed on the front-end, and then extract useful information and patterns from these data to generate a target evaluation report. The target evaluation report is the final output result of the analysis process and is used to display the evaluation results of the target users.

[0033] Furthermore, the backend processing center includes a Drools rule engine and a template engine, and the rule engine-based data evaluation collaborative method includes: The target effective evaluation data is analyzed according to a preset rule base in the Drools rule engine to obtain a target evaluation result; and the target evaluation result is templated according to the template engine to obtain the target evaluation report.

[0034] The back-end processing center includes the Drools rule engine and the template engine. The Drools rule engine is a rule-based reasoning engine that can perform logical judgment and reasoning on data according to a predefined rule base. During the data evaluation process, the Drools rule engine is responsible for automatically analyzing the evaluation data and drawing specific evaluation conclusions; the template engine is used to output the analyzed evaluation results according to the preset template format and generate a structured report. The template engine is used to organize data into a readable evaluation report that conforms to a specific format for easy display and understanding.

[0035] The preset rule base in the Drools rule engine is a set of predefined rules. These rules are designed according to business logic or domain-specific evaluation criteria. Each rule specifies how to process data under specific conditions and generate evaluation results. For example, in health assessment, the rule base may contain standard range definitions for indicators such as blood pressure and heart rate. If a user's blood pressure exceeds the standard range, it will be judged as a high blood pressure risk according to the rule base, and the corresponding evaluation conclusion will be output. In the collaborative process of data evaluation, the Drools rule engine automatically analyzes and calculates the target effective evaluation data according to the preset rule base. The working principle of the rule engine is based on the reasoning mechanism of premise-condition-action. For example, if a certain indicator meets a certain condition (such as a value exceeds a certain threshold), the corresponding action will be performed. After analysis by the rule engine, the target evaluation result is generated, which directly reflects the current status of the target user.

[0036] The template engine is responsible for formatting the evaluation results output by the Drools rule engine according to the pre-designed template. The template is a standardized evaluation report structure, which usually includes evaluation indicator display (specific values ​​and evaluation conclusions of each evaluation item), summary and suggestions (overall evaluation conclusions generated based on the analysis results, as well as possible improvement suggestions or further action plans), charts and data visualization (charts generated based on the evaluation results). The evaluation results processed by the template engine generate a complete target evaluation report. The target evaluation report is a structured and standardized document that can clearly present a comprehensive evaluation of the target user.

[0037] The synergy of the Drools rule engine and the template engine in the backend processing center enables the system to efficiently and automatically analyze the evaluation data and present the analysis results in a structured form. In this way, the system can not only provide accurate evaluation results, but also generate easy-to-understand evaluation reports, providing intuitive decision support for users and related personnel.

[0038] Furthermore, the back-end processing center also includes a database, wherein the database is used to archive and back up the target effective evaluation data.

[0039] The back-end processing center also includes a database, which is responsible for archiving and backing up the target effective evaluation data. Archiving refers to saving the evaluation data to the database to ensure that the data can be stored for a long time and maintain integrity. Backup is to prevent data loss or damage, and the evaluation data is copied and stored in a safe location. The target effective evaluation data is the result of collection through the front-end interface and analysis and processing by the Drools rule engine. After the evaluation process is completed, this data still has important reference value, so it needs to be stored and backed up for subsequent queries.

[0040] In summary, the embodiments of the present application have at least the following technical effects: First, the first target information group of the first evaluation agency among the multiple evaluation agencies is parsed and identified to obtain the first evaluation data of the target user by the first evaluation agency. Next, the predetermined integration strategy is read, and the first evaluation data is integrated and fused according to the predetermined integration strategy to obtain the target integrated data. Further, the target integrated data is displayed through the front-end user interface of the target user to obtain the target effective evaluation data. Finally, the target effective evaluation data is analyzed in conjunction with the back-end processing center to generate a target evaluation report. The technical problem that the lack of unified standards for data evaluation in the prior art leads to low evaluation efficiency and accuracy of evaluation results is solved, and the unified parsing of multi-party evaluation data is realized through the rule engine, thereby achieving the technical effect of improving evaluation efficiency and accuracy.

[0041] Embodiment 2 is based on the same inventive concept as the data evaluation collaborative method based on the rule engine in the above embodiment. Figure 2 As shown, the present application provides a data evaluation collaborative system based on a rule engine, wherein the system includes: A parsing and identifying module 11, wherein the parsing and identifying module 11 is used to parse and identify a first target information group of a first evaluation agency among the multiple evaluation agencies, and obtain first evaluation data of a target user by the first evaluation agency; a fusion module 12, wherein the fusion module 12 is used to read a predetermined integration strategy, and integrate and fuse the first evaluation data according to the predetermined integration strategy, and obtain target integrated data; a display module 13, wherein the display module 13 is used to display the target integrated data through a front-end user interface of the target user, and obtain target effective evaluation data; an analysis module 14, wherein the analysis module 14 is used to analyze the target effective evaluation data in conjunction with a back-end processing center, and generate a target evaluation report.

[0042] Furthermore, the analysis and identification module 11 is used to perform the following method: The first target information group includes first text information, first table information and first picture information; first data is obtained based on the first text information; second data is obtained by performing text recognition on the first table information; third data is obtained by identifying preset key points of the first picture; the first data, the second data and the third data constitute the first evaluation data.

[0043] Furthermore, the display module 13 is used to execute the following method: Acquire second evaluation data, where the second evaluation data refers to data obtained by parsing and identifying a second target information group for evaluating the target user by a second evaluation agency, wherein the second evaluation agency refers to any one of the multiple evaluation agencies that is different from the first evaluation agency; extract a first indicator among predetermined evaluation indicators; match parameters corresponding to the first indicator in the first evaluation data and the second evaluation data in turn, and record them as first indicator parameters and second indicator parameters, respectively; obtain a first decision result of the target user on the first indicator according to a user decision mechanism in the predetermined integration strategy; according to the first decision result, use the first indicator parameter or the second indicator parameter as a first integration parameter of the first indicator; and construct the target effective evaluation data based on the first integration parameter.

[0044] Furthermore, the display module 13 is used to execute the following method: Determine whether the first indicator belongs to the predetermined basic feature category; if so, activate the user decision mechanism in the predetermined integration strategy, and obtain the first integration parameter of the first indicator according to the user decision mechanism; if not, determine whether the first indicator belongs to the predetermined test feature range; if so, activate the fuzzy decision mechanism in the predetermined integration strategy, and take the average of the first indicator parameter and the second indicator parameter as the first integration parameter of the first indicator according to the fuzzy decision mechanism.

[0045] Furthermore, the display module 13 is used to execute the following method: If not, the first indicator parameter and the second indicator parameter are displayed in a predetermined form as the first integrated parameter.

[0046] Furthermore, the analysis module 14 is used to perform the following method: The back-end processing center includes a Drools rule engine and a template engine; the target effective evaluation data is analyzed according to a preset rule base in the Drools rule engine to obtain a target evaluation result; the target evaluation result is templated according to the template engine to obtain the target evaluation report.

[0047] Furthermore, the analysis module 14 is used to perform the following method: The back-end processing center also includes a database, wherein the database is used to archive and back up the target effective evaluation data.

[0048] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying 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.

[0049] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0050] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A data evaluation collaborative method based on a rule engine, characterized in that: The data evaluation collaboration method based on the rule engine is applied to a data evaluation collaboration system based on the rule engine. The data evaluation collaboration system based on the rule engine is associated with multiple evaluation agencies. The data evaluation collaboration method based on the rule engine includes: Parsing and identifying a first target information group of a first evaluation agency among the multiple evaluation agencies to obtain first evaluation data of the first evaluation agency on the target user; Reading a predetermined integration strategy, and integrating and fusing the first evaluation data according to the predetermined integration strategy to obtain target integrated data; The target integrated data is displayed through the front-end user interface of the target user to obtain target effective evaluation data; In conjunction with the back-end processing center, the target effective evaluation data is analyzed to generate a target evaluation report.

2. The data evaluation collaborative method based on rule engine according to claim 1 is characterized in that: include: The first target information group includes first text information, first table information and first picture information; Obtaining first data according to the first text information; Performing text recognition on the first table information to obtain second data; Identify preset key points of the first image to obtain third data; The first data, the second data and the third data constitute the first evaluation data.

3. The data evaluation collaborative method based on rule engine according to claim 1 is characterized in that: include: Acquire second evaluation data, where the second evaluation data refers to data obtained by parsing and identifying a second target information group for evaluating the target user by a second evaluation agency, wherein the second evaluation agency refers to any one of the multiple evaluation agencies that is different from the first evaluation agency; Extracting a first indicator among predetermined evaluation indicators; Matching parameters corresponding to the first indicator in the first evaluation data and the second evaluation data in sequence, recording them as first indicator parameters and second indicator parameters respectively; Obtaining a first decision result of the target user on the first indicator according to a user decision mechanism in the predetermined integration strategy; According to the first decision result, taking the first indicator parameter or the second indicator parameter as a first integrated parameter of the first indicator; The target validity evaluation data is constructed based on the first integration parameter.

4. The data evaluation collaborative method based on rule engine according to claim 3 is characterized in that: Also includes: Determining whether the first indicator belongs to the predetermined basic feature category; If yes, activating the user decision mechanism in the predetermined integration strategy, and obtaining the first integration parameter of the first indicator according to the user decision mechanism; If not, determining whether the first indicator belongs to a predetermined test feature range; If yes, activate the fuzzy decision mechanism in the predetermined integration strategy, and take the average of the first indicator parameter and the second indicator parameter as the first integration parameter of the first indicator according to the fuzzy decision mechanism.

5. The data evaluation collaborative method based on rule engine according to claim 4 is characterized in that: Determine whether the first indicator belongs to a predetermined test feature range, and then include, if not, displaying the first indicator parameter and the second indicator parameter in a predetermined form as the first integrated parameter.

6. The data evaluation collaborative method based on rule engine according to claim 1 is characterized in that: The backend processing center includes a Drools rule engine and a template engine, and the rule engine-based data evaluation collaborative method includes: Analyze the target effective evaluation data according to the preset rule base in the Drools rule engine to obtain a target evaluation result; The target assessment result is templated according to the template engine to obtain the target assessment report.

7. The data evaluation collaborative method based on rule engine according to claim 6 is characterized in that: The back-end processing center also includes a database, wherein the database is used to archive and back up the target effective evaluation data.

8. Data evaluation collaborative system based on rule engine, characterized by: The system for implementing the rule engine-based data evaluation collaborative method according to any one of claims 1 to 7 comprises: A parsing and identifying module, the parsing and identifying module is used to parse and identify a first target information group of a first evaluation agency among the multiple evaluation agencies, and obtain first evaluation data of the first evaluation agency on the target user; A fusion module, the fusion module is used to read a predetermined integration strategy, and integrate and fuse the first evaluation data according to the predetermined integration strategy to obtain target integrated data; A display module, the display module is used to display the target integrated data through the front-end user interface of the target user to obtain target effective evaluation data; The analysis module is used to analyze the target effective evaluation data in conjunction with the back-end processing center to generate a target evaluation report.

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