Component intelligent data output fusion method based on specific rule

Abstract

The invention discloses an intelligent component data output fusion method based on a specific rule, and the method specifically comprises the steps: adding a rule condition component through the visual configuration of a rule condition, generating data with a unified standard through the added rule condition component, converting the data into JSON, and storing the data into a database; a rule condition component in the database is called to carry out multi-source data processing, the processed data is input into an output processing module, and the output processing module finally generates a data list, converts the data list into JSON and stores the JSON into the database or encrypts and stores the JSON into a vueStore front-end state management library; and an output operation result fusion module is configured, a value output by the output operation result fusion module is obtained, rendering of component contents is carried out, data of different dimensions and styles of unique styles are displayed, and meanwhile, obtained output results can also be used as parameters of the components in some interaction events, so that application requirements of different scenes are met.

Description

Technical Field

[0001] The present application relates to the fields of data processing, data mining and analysis, and to the integration, conversion, analysis and fusion of different types of data to extract valuable information, and mainly to a method for intelligent data output fusion of components based on specific rules. Background Art

[0002] In today's digital and information age, the scale and complexity of data are growing explosively, and the types of data are becoming more and more diverse. These data come from different sources, forming a massive amount of multi-source heterogeneous data. At the same time, different application systems output certain types of data in different forms. In this context, traditional data processing methods seem to be unable to cope with the challenges brought by multi-source heterogeneous data. They can usually only process data of a single type or source, and it is difficult to cope with the challenges brought by multi-source heterogeneous data. In order to extract valuable information from these rich but complex data, intelligent data processing technology has emerged.

[0003] Intelligent data processing fusion aims to integrate and analyze different types of data from multiple data sources to discover the patterns, relationships and trends hidden in them. However, there are great differences in data quality, accuracy, semantics and format between different data sources, which leads to inconsistencies and conflicts between data, and often cannot well reflect the specific usefulness of the data. In order to solve these problems, intelligent data output processing fusion based on specific rules can be used to standardize the preprocessing, conversion, fusion, and output process of data, which can effectively realize the organic fusion of different types of data, thereby providing an accurate and comprehensive data foundation for applications such as decision support, predictive analysis, and intelligent recommendation.

[0004] The existing technology mainly displays data directly or processes related data manually. The output processing of data is not rich enough, and the output processing of visual data is not performed according to certain rules and conditions. As a result, it is difficult to obtain the required output results when the project is more complex. For example, there is a table data, in which a field content contains multiple formats (Chinese, numbers, unit symbols, html, audio, video structure, etc.), and then the content in it needs to be batch converted or merged into the required content data according to different rules and conditions. Many current project products cannot do this, or the processing is not perfect, or only hand-written code can be used, and there is no unified output conversion. In addition, if multiple steps are required to process the data output results, the value processed in each step is left for the use of subsequent steps, and the steps are linked and integrated with each other. Currently, many products have not yet uniformly generated a standard output processing result, which greatly increases the subsequent maintenance cost. Summary of the invention

[0005] According to the existing technology, the data is mainly displayed directly or the relevant data is processed manually. The output processing of data is not rich enough, and the output processing of visual data is not performed according to certain rules and conditions. At the same time, some sensitive data has security risks, and a better output processing fusion method is not defined. It is very troublesome and costly in use. In order to solve the above problems, the present invention proposes a method for intelligent data output fusion of components based on specific rules.

[0006] According to one aspect of the present invention, a method for intelligent data output fusion of components based on specific rules is proposed, comprising:

[0007] S1. Use the rule condition visualization configuration to add rule condition components, generate unified standard data ruleConditionLists by adding good rule condition components, and convert them into JSON and save them in the database;

[0008] S2. Call the rule condition component in the database to process multi-source data, and input the processed data into the output processing module, which includes: setting the output step list, selecting the output processing function and selecting the data source; the output processing module finally generates the data list outputResultStepLists and converts it into JSON and saves it in the database or encrypts and stores it in the vueStore front-end state management library;

[0009] S3. Configuring the output operation result fusion module, specifically including: obtaining JSON data from the database for data parsing, filtering the parsed data according to the rule condition component, standardizing the filtered data, fusing the data using a weighted average data fusion algorithm, extracting features, identifying patterns and predicting trends on the fused data using a machine learning algorithm, and rendering component content using the fused data.

[0010] Furthermore, the rule condition component includes precondition content, comparison format content and postcondition content, wherein the comparison format content includes a common format, a numerical format, a string format and a date format, and the condition module includes copy, paste and send functions.

[0011] In the visual configuration of rule conditions, users can add multiple groups of rule conditions, and each group of conditions is a combination of multiple conditions of intersection or union.

[0012] The uniform standard data ruleConditionLists are generated through the configured conditions and then converted into JSON and saved in the database. Users can copy and paste them into other components at will.

[0013] Furthermore, the processing functions include string interception, string concatenation, string replacement, string conversion to pinyin, string conversion to numbers, uppercase and lowercase Chinese conversion to numbers, numbers to thousands separators, numbers to uppercase and lowercase Chinese, number addition, subtraction, multiplication and division, number combination operations, date and time conversion, string encryption and custom processing function methods.

[0014] Furthermore, the data sources include manually input content, data selection, page preset data, inherited page parameters, routing parameters, current component object values, page component values, identification field values, component field values, data request result values ​​and page cookie values.

[0015] Users can copy and paste these configured components and paste them directly to other components for normal use or with slight modifications, which greatly improves work efficiency and reduces development costs.

[0016] Furthermore, the data standardization process specifically includes normalization and standardization, and the specific formula of the normalization is expressed as:

[0017]

[0018] Among them, x' represents the normalized data, x represents the input data, y represents the standardized data, minx represents the minimum value in the data set, maxx represents the maximum value in the data set, μ represents the mean of the data set, and σ represents the standard deviation of the data set.

[0019] Normalization is used to scale data to a specific range, which ensures that data of different scales are compared and processed within the same range. Standardization is to process data into a standard normal distribution to facilitate subsequent data fusion.

[0020] Furthermore, the weighted average data fusion algorithm assigns different weights to each data source according to its reliability or importance. The specific formula is as follows:

[0021]

[0022] Among them, N represents the maximum number of data sets, x i represents the i-th data in the data set, w i represents the i-th weight of the i-th data in the data set, and

[0023] The weighted average method can adjust the weights of each data source to reflect its importance or reliability, and can find the optimal weight combination through optimization algorithms (such as gradient descent, genetic algorithm, etc.) to maximize the performance indicators of the fusion results (such as accuracy, stability, etc.).

[0024] According to a second aspect of the present invention, a computer-readable storage medium is provided, on which one or more computer programs are stored. When the one or more computer programs are executed by a computer processor, the above method is implemented.

[0025] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0026] The present invention can solve the problems of data integration and consistency in the project, the association and display of multi-dimensional data, and ensure that when the data is constantly changing, the visualization components can respond to the latest data in a timely and accurate manner, and realize real-time dynamic display. At the same time, it can adapt to different user needs and scenarios, improve performance and efficiency, enhance interactivity and user experience, and more importantly, ensure the security of certain data and the non-disclosure of user privacy during the fusion and output of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated into and constitute a part of this specification. The accompanying drawings illustrate the embodiments and are used together with the description to explain the principles of the present invention. It will be easy to recognize other embodiments and many expected advantages of the embodiments because they become better understood by reference to the following detailed description. The elements of the drawings are not necessarily to scale with each other. The same reference numerals refer to corresponding similar parts.

[0028] Figure 1 A schematic diagram of a system for intelligent data output fusion of components based on specific rules according to an embodiment of the present invention is shown;

[0029] Figure 2 A schematic flow chart of a method for intelligent data output fusion of components based on specific rules according to a specific embodiment of the present invention is shown. DETAILED DESCRIPTION

[0030] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It should also be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0031] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0032] Figure 1 A method for intelligent data output fusion of components based on specific rules according to the present invention is shown, such as Figure 1 As shown:

[0033] S1. Use the rule condition visualization configuration to add rule condition components, generate unified standard data ruleConditionLists by adding good rule condition components, and convert them into JSON and save them in the database;

[0034] S2. Call the rule condition component in the database to process multi-source data, and input the processed data into the output processing module, which includes: setting the output step list, selecting the output processing function and selecting the data source; the output processing module finally generates the data list outputResultStepLists and converts it into JSON and saves it in the database or encrypts and stores it in the vueStore front-end state management library;

[0035] S3. Configuring the output operation result fusion module, specifically including: obtaining JSON data from the database for data parsing, filtering the parsed data according to the rule condition component, standardizing the filtered data, fusing the data using a weighted average data fusion algorithm, extracting features, identifying patterns and predicting trends on the fused data using a machine learning algorithm, and rendering component content using the fused data.

[0036] Furthermore, the processing functions include string interception, string concatenation, string replacement, string conversion to pinyin, string conversion to numbers, uppercase and lowercase Chinese conversion to numbers, numbers to thousands separators, numbers to uppercase and lowercase Chinese, number addition, subtraction, multiplication and division, number combination operations, date and time conversion, string encryption and custom processing function methods.

[0037] Furthermore, the data sources include manually input content, data selection, page preset data, inherited page parameters, routing parameters, current component object values, page component values, identification field values, component field values, data request result values ​​and page cookie values.

[0038] The data standardization process specifically includes normalization and standardization. The specific formula of the normalization is expressed as:

[0039]

[0040] Among them, x' represents the normalized data, x represents the input data, y represents the standardized data, minx represents the minimum value in the data set, maxx represents the maximum value in the data set, μ represents the mean of the data set, and σ represents the standard deviation of the data set.

[0041] The weighted average data fusion algorithm assigns different weights to each data source according to its reliability or importance. The specific formula is as follows:

[0042]

[0043] Among them, N represents the maximum number of data sets, x i represents the i-th data in the data set, w i represents the i-th weight of the i-th data in the data set, and

[0044] The output operation result fusion module is triggered only when it is running in real time. When the page component is rendered, the JSON data stored in S1 and S2 is obtained from the database and the data is parsed. Then, based on the parsed data, check whether the component has configured the corresponding output processing steps. If not, the original data is directly displayed. If yes, the conditions configured by S1 are first performed, and the conditional method getActionConditionResult is used to determine whether the conditions are met. If they are met, the corresponding output steps are processed according to the JSON data stored in S2, and the value of the output result outputResult is obtained through the function calcActionOutputResult. Then, according to the obtained value, the component content is rendered to display data of different dimensions and unique styles. At the same time, the output results obtained can also be used as parameters for some interactive events of the component to meet the application requirements of different scenarios.

[0045] In this embodiment, based on the smart factory, it is necessary to fuse data from multiple sensors to monitor the status of the production line and perform fault detection. The specific steps include:

[0046] Data collection: Collect data from multiple sensors such as temperature sensors, vibration sensors, current sensors, etc. Clean the data and convert its format.

[0047] Data standardization: Convert all sensor data into a unified timestamp format. Ensure the time synchronization of all data. Normalize temperature data: Normalized vibration data:

[0048] Rule definition: Add rule condition components to the rule condition visualization configuration, where you can define temperature thresholds (such as exceeding 80°C to trigger an alarm), vibration thresholds (such as exceeding 10mm / s to trigger an alarm), and current thresholds (such as exceeding 120% of the rated current to trigger an alarm).

[0049] Data fusion: Integrate the data of all sensors into the output processing module. Apply rules to make conditional judgments, such as triggering an alarm and recording an event when the temperature exceeds 80°C. Calculate the average value, maximum value, etc. of multiple sensor data.

[0050] The weight is defined as w in the fusion algorithm T =0.5, w V =0.3, w I =0.2;

[0051] The comprehensive index after fusion is: y = 0.5·T'+0.3·V'+0.2·I';

[0052] Intelligent processing: Use machine learning algorithms to analyze historical data and predict possible failures. Monitor production line status in real time and display key indicators through dashboards.

[0053] Output and application: Generate daily production reports, including key indicators such as temperature, vibration, and current. Automatically send alarms to notify relevant personnel when an abnormality occurs. Automatically adjust production line parameters based on the fused data to optimize production efficiency, render component content, and optimize component content in real time.

[0054] In summary, the component intelligent data output fusion method based on specific rules integrates and processes data from multiple data sources by defining and applying a series of rules to generate more valuable information. This method can improve the accuracy and reliability of data, support real-time monitoring and intelligent management, and is widely used in industrial automation, Internet of Things, smart cities and other fields.

[0055] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0056] The modules involved in the embodiments of the present application may be implemented by software or by hardware.

[0057] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A method for intelligent data output fusion of components based on specific rules, characterized in that: The following steps are involved: S1. Use the rule condition visualization configuration to add rule condition components, generate unified standard data ruleConditionLists by adding good rule condition components, and convert them into JSON and save them in the database; S2. Call the rule condition component in the database to process multi-source data, and input the processed data into the output processing module, which includes: setting the output step list, selecting the output processing function and selecting the data source; the output processing module finally generates the data list outputResultStepLists and converts it into JSON and saves it in the database or encrypts and stores it in the vueStore front-end state management library; S3. Configuring the output operation result fusion module, specifically including: obtaining JSON data from the database for data parsing, filtering the parsed data according to the rule condition component, standardizing the filtered data, fusing the data using a weighted average data fusion algorithm, extracting features, identifying patterns and predicting trends on the fused data using a machine learning algorithm, and rendering component content using the fused data.

2. The method according to claim 1, characterized in that The rule condition component includes precondition content, comparison format content and postcondition content, wherein the comparison format content includes a common format, a numerical format, a string format and a date format, and the condition module includes copy, paste and send functions.

3. The method according to claim 1, characterized in that The processing functions include string interception, string concatenation, string replacement, string conversion to pinyin, string conversion to numbers, uppercase and lowercase Chinese conversion to numbers, numbers to thousands separators, numbers to uppercase and lowercase Chinese, number addition, subtraction, multiplication and division, number combination operations, date and time conversion, string encryption and custom processing function methods.

4. The method according to claim 1, characterized in that: The data sources include manually input content, data selection, page preset data, inherited page parameters, routing parameters, current component object values, page component values, identification field values, component field values, data request result values ​​and page cookie values.

5. The method according to claim 1, characterized in that The data standardization process specifically includes normalization and standardization. The specific formula of the normalization is expressed as: Among them, x' represents the normalized data, x represents the input data, y represents the standardized data, minx represents the minimum value in the data set, maxx represents the maximum value in the data set, μ represents the mean of the data set, and σ represents the standard deviation of the data set.

6. The method according to claim 1, characterized in that The weighted average data fusion algorithm assigns different weights to each data source according to its reliability or importance. The specific formula is as follows: Among them, N represents the maximum number of data sets, x i represents the i-th data in the data set, w i represents the i-th weight of the i-th data in the data set, and 7. A computer program product, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

8. A computing system, characterized in that: The method comprises a processor and a memory, wherein the processor is configured to execute the method according to any one of claims 1 to 6.

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