Complex form custom real-time verification system and method based on data framework technology

Abstract

The invention relates to the technical field of information, in particular to a user-defined real-time verification system and method for a complex form based on a data framework technology, and aims to solve the problems that hierarchical verification, machine learning and multi-dimensional analysis cannot be performed on user input and efficient, accurate and real-time form data verification cannot be realized in the prior art. Layered verification, machine learning and multi-dimensional analysis are carried out through the real-time verification engine module, efficient, accurate and real-time form data verification is achieved, user experience and system performance are improved, unnecessary calculation and back-end calling are avoided through a layered verification mechanism, verification accuracy is improved through a machine learning model, and the user experience is improved. The real-time verification characteristic shortens the waiting time of a user, the multidimensional verification of numerical values and formats ensures the comprehensiveness of the verification, and the flexible verification period and time interval division enhance the pertinence of the verification.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and more specifically, to a complex form customized real-time verification system and method based on data framework technology. Background Art

[0002] In modern software development, forms are the main interface for users to input data, and their complexity and diversity are increasing. Traditional form validation methods often cannot meet complex business needs, especially in scenarios that require real-time feedback of validation results, support for custom validation rules, and processing of cross-field validation logic.

[0003] The patent application with reference publication number CN118377557A discloses a custom form verification system and control method based on an asynchronous verification library; including marking verification elements and dividing verification areas, obtaining the attribute status of input values, extracting verification features from verification elements, formulating custom verification rules in the Joi verification library, verifying input values, determining whether to enter error correction mode, inputting error correction values ​​into error correction boxes, obtaining error correction coefficients in verification areas, and generating verification display sheets; compared with the prior art, it is possible to accurately extract verification features from forms, and formulate custom verification rules based on the verification features, thereby avoiding the limitations of forms when being verified under specific verification rules, improving the pertinence and applicability of form verification, and also being able to perform corresponding verification display operations on different verification results, effectively improving the verification efficiency of forms; However, the above-mentioned reference patent achieves fast and accurate form verification and targeted display by accurately dividing the verification area, extracting verification features and using the Joi library to customize rules, thereby improving verification efficiency and applicability. However, it cannot perform layered verification, machine learning and multi-dimensional analysis on user input, and cannot achieve efficient, accurate and real-time form data verification. It cannot determine whether the form needs to be optimized through comprehensive evaluation of multiple indicators, and cannot monitor key performance indicators in real time. It cannot automatically trigger the adjustment mechanism according to the monitoring situation and cannot guarantee stable operation of the system.

[0004] To this end, we propose a complex form customized real-time verification system and method based on data framework technology to address the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a complex form customized real-time verification system and method based on data framework technology, which solves the problems that the prior art cannot perform layered verification, machine learning and multi-dimensional analysis on user input, cannot achieve efficient, accurate and real-time form data verification, cannot determine whether the form needs to be optimized through multi-indicator comprehensive evaluation, and cannot monitor key performance indicators in real time, cannot automatically trigger the adjustment mechanism according to the monitoring situation, and cannot ensure the stable operation of the system.

[0006] The purpose of the present invention is achieved through the following technical solutions: A complex form custom real-time verification system based on data framework technology, including a form management platform, a data model definition module, a verification rule configuration module, a real-time verification engine module, a form data analysis module, and a performance monitoring and optimization module; Data model definition module, which defines the form structure and default values, supports basic and complex data initialization, validation rule setting, and data formatting and parsing; Verification rule configuration module, which configures the verification logic of form items, supports basic rules, complex function definitions and asynchronous backend verification; The real-time verification engine module verifies user input immediately, provides feedback on whether the user input meets the requirements based on the verification results, and provides clear error prompts; The form data analysis module analyzes the submitted form data and optimizes the form based on the analysis results; The performance monitoring and optimization module monitors system performance indicators in real time and automatically takes measures to make adjustments based on the monitoring situation.

[0007] As a preferred implementation of the present invention, the specific process of defining and managing the form by the data model definition module is as follows: The form structure defines the properties of each field in the form. The definition of each field includes the following elements: Field type: specifies the data type of the field. The field type determines the type of data that users can enter and how it is presented on the interface; Field label: used to display the name or description of the field in the user interface; Default value: the initial value of the field, displayed when the user does not enter any value; Required: Specifies whether the field is required. If it is set to required, users must fill in the field before submitting the form. Data initialization refers to setting initial values ​​for form fields. The data initialization process for basic and complex types is as follows: Basic types include strings, numbers, and Boolean values. For these types, you can directly assign values ​​to the defaultValue attribute; Complex types include arrays and objects. For array types, you can use an array containing initial values ​​to initialize them. Validation rules are used to ensure that the data entered by the user complies with the predetermined format and requirements. The validation rules are as follows: Required field: specifies that a field must be filled in and cannot be left blank; String length limit: limit the minimum and maximum length of the string; Numerical range limit: limit the minimum and maximum values ​​of the value; Regular expression matching: Use regular expressions to verify the format of a string; Custom validation function: allows developers to write custom validation functions to meet specific validation requirements; Data formatting and parsing are used to convert data between the user interface and the data storage. Data formatting is to convert data into a user-friendly format for display, and data parsing is to convert the string input by the user into a data type that the program can process.

[0008] As a preferred implementation of the present invention, the specific process of the verification rule configuration module performing verification logic configuration on the form item is as follows: Verification logic configuration: associate verification rules with form items. One form item can correspond to multiple verification rules. When the user submits the form, the system will execute these rules in sequence and decide whether to allow the form submission based on the verification results. Basic rule verification: Basic rules are predefined validation rules. Basic rules include: checking whether the field value is empty, checking whether the length of the string is within the specified range, checking whether the value is within the specified range, and using regular expressions to check whether the string conforms to a specific format; Complex function definition: For some complex validation logic, custom functions can be used to implement them. These functions accept the value of the form item as input and return a Boolean value to indicate whether the validation is passed; Asynchronous backend verification: Asynchronous backend means that the front end communicates with the backend server through asynchronous requests. Some verification logic requires access to backend data to complete.

[0009] As a preferred embodiment of the present invention, the specific process of the real-time verification engine module verifying the user input is as follows: Get the user's form input data, classify the form input data according to the configured verification logic, and divide it into basic rules, complex functions, and asynchronous backends for verification. First, use the basic rules for preliminary verification, generate a verification cycle, and divide the verification cycle JZ into multiple verification time periods {jz1, jz2, ..., jzn}, that is, JZ={jz1, jz2, ..., jzn}; Obtain the field value deviation value of the form input data in multiple verification periods, the field deviation value represents the absolute value of the difference between the actual input value and the expected value, and calculate the arithmetic mean of the multiple field value deviation values ​​obtained, and record the arithmetic mean of the multiple field value deviation values ​​as the average field value deviation value PZD; The string length deviation value of the form input data in multiple verification periods is obtained. The string length deviation value represents the absolute value of the difference between the actual input string length value and the preset length value. The arithmetic mean of the multiple string length deviation values ​​obtained is calculated, and the arithmetic mean of the multiple string length deviation values ​​is recorded as the average string length deviation value PZF.

[0010] As a preferred embodiment of the present invention, the numerical deviation rate of the form input data in multiple inspection periods is obtained, and the numerical deviation rate represents the ratio of the absolute value of the difference between the actual input value and the preset value to the preset value, thereby constructing a set A of numerical deviation rates, and the mean of the difference between the maximum subset and the minimum subset in the set A is recorded as the numerical deviation rate difference SPC; The string format deviation rate of the form input data in multiple verification periods is obtained. The string format deviation rate indicates the degree of match between the input string and the preset format. A set B of string format deviation rates is constructed based on this, and the mean of the difference between the largest subset and the smallest subset in set B is recorded as the string format deviation rate difference ZGC.

[0011] As a preferred embodiment of the present invention, the average field value deviation value PZD, the average string length deviation value PZF, the numerical deviation rate difference SPC, and the string format deviation rate difference ZGC are obtained, and the average field value deviation value PZD, the average string length deviation value PZF, the numerical deviation rate difference SPC, and the string format deviation rate difference ZGC are combined to construct a basic rule verification feature matrix JT; The constructed feature matrix JT is used as the input of the machine learning model, and the label vector u is used as the output of the machine learning model. The label vector u represents the verification status of the form input data for basic rule verification. The output of the label vector u is 0 or 1. 0 represents that the form input data has not passed the preliminary verification of the basic rules, and 1 represents that the form input data has passed the preliminary verification of the basic rules. The label vector u is used as the prediction target, and the sum of the prediction errors of all training data is minimized as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the training is stopped to obtain a machine learning model that predicts the label vector u. Collect users' real-time form input data and build the basic rule verification feature matrix JT 实时 , the verification status of the form input data for basic rule verification is obtained through the trained machine learning model; If the label vector u output by the machine learning model is 0, it means that the form input data has not passed the preliminary verification of the basic rules; If the label vector u output by the machine learning model is 1, it means that the form input data has passed the preliminary verification of the basic rules.

[0012] As a preferred embodiment of the present invention, after the form input data passes the preliminary verification of the basic rules, a complex function is used to perform a secondary verification, obtain the Boolean value within the verification period, and compare the obtained Boolean value with the preset Boolean value: If the obtained Boolean value is consistent with the preset Boolean value, it means that the form input data has passed the secondary verification of the complex function; If the obtained Boolean value is inconsistent with the preset Boolean value, it means that the form input data has not passed the secondary verification of the complex function; After the form input data passes the front-end verification of basic rules and complex functions, the form input data is submitted to the back-end for asynchronous back-end verification, the Boolean value within the verification cycle is obtained, and the obtained Boolean value is compared with the preset Boolean value: If the obtained Boolean value is consistent with the preset Boolean value, it means that the form input data has passed the asynchronous backend verification; If the obtained Boolean value is inconsistent with the preset Boolean value, it means that the form input data has not passed the asynchronous backend verification.

[0013] As a preferred embodiment of the present invention, the specific process of the form data analysis module analyzing the form data and optimizing the form is as follows: Obtain the submitted form data, generate a collection cycle, divide the collection cycle into m consecutive sub-cycles, and mark the midpoint time of each sub-cycle to obtain m midpoint times; Taking m midpoint moments as the base point, expand forward and backward by equal lengths, mark s-1 expansion moments, and then aggregate the midpoint moments and s-1 expansion moments to obtain s detection moments; Obtain the form completion rate at s detection moments, obtain the completion rate of s detection forms, and accumulate and average the completion rates of the s detection forms to obtain the completion rate of m sub-forms; The expression of sub-expression completion rate is: ; In the formula, BD zm is the subform completion rate of the mth subcycle, BD jcmn is the completion rate of the nth test form in the mth sub-cycle; Remove the maximum and minimum values ​​of the subform completion rate, and average the remaining m-2 subform completion rates to obtain the mean form completion rate. The expression for the mean form completion rate is: ; In the formula, BD jz is the mean form completion rate, BD zp is the subform completion rate of the pth sub-period; Calculate the mean form completion rate BD through the above jzThe mean value of field completion rate ZD can be obtained by the same method. jz、 Fill in the mean time SJ jz， The form optimization evaluation factor BYP is calculated using the following formula: ; Among them, e1, e2 and e3 are all preset proportional factor coefficients, e3>e2>e1>0, and the form optimization evaluation coefficient BYP is compared with the preset form optimization evaluation coefficient threshold: If the form optimization evaluation coefficient BYP is greater than or equal to the preset form optimization evaluation coefficient threshold, no signal is generated; If the form optimization evaluation coefficient BYP is less than the preset form optimization evaluation coefficient threshold, a form optimization signal is generated and sent to the form management platform; After the form management platform receives the signal that the form needs to be optimized, it immediately takes measures to optimize the form.

[0014] As a preferred implementation of the present invention, the specific process of the performance monitoring and optimization module monitoring system performance indicators and automatically taking measures is as follows: Get the real-time performance indicator data of the system during operation. The real-time performance indicator data includes the real-time verification response time SJX, the real-time CPU usage SCS and the real-time memory usage SNS. The real-time verification response time indicates the time required from the user entering data in the form to the system completing the verification and returning the result. The real-time CPU usage indicates the percentage of CPU resources occupied in the system at any given moment. The real-time memory usage indicates the proportion of the system's used memory to the total memory at any given moment. The system performance evaluation coefficient XXP is obtained by the following formula: ; Where f1, f2 and f3 are all preset proportional factor coefficients, f3>f2>f1>0, and the system performance evaluation coefficient XXP is compared with the preset system performance evaluation coefficient threshold: If the system performance evaluation coefficient XXP is less than the preset system performance evaluation coefficient threshold, it indicates that the system is operating well and no signal is generated; If the system performance evaluation coefficient XXP is greater than or equal to the preset system performance evaluation coefficient threshold, it indicates that the system operation performance is poor, and a system operation adjustment signal is generated and sent to the form management platform; After receiving the system operation adjustment signal, the form management platform immediately takes measures to adjust the system operation.

[0015] As a preferred embodiment of the present invention, a complex form customized real-time verification method using data framework technology includes the following steps: Step 1: Build a form by setting field types, labels, default values, and required attributes, support data initialization and validation rules, and perform data formatting and parsing; Step 2: Configure the validation logic of form items by associating basic rules and custom functions, and support asynchronous backend validation; Step 3: Obtain user input, classify it according to the verification logic, and perform basic rule verification, complex function verification, and asynchronous backend verification on the user input; Step 4: Generate an optimization coefficient by calculating the completion rate, filling rate and time average, and generate an optimization signal when the coefficient is lower than the preset threshold, prompting the form management platform to optimize; Step 5: Calculate the performance evaluation coefficient by real-time monitoring and verification of response time, CPU usage, and memory usage. When the coefficient exceeds the threshold, an adjustment signal is generated to optimize system operation.

[0016] Compared with the prior art, the advantages of the present invention are: In the present invention, hierarchical verification, machine learning and multi-dimensional analysis are performed through the real-time verification engine module to achieve efficient, accurate and real-time form data verification, improve user experience and system performance, the hierarchical verification mechanism avoids unnecessary calculations and back-end calls, the machine learning model improves verification accuracy, the real-time verification feature shortens user waiting time, the multi-dimensional verification of values ​​and formats ensures the comprehensiveness of verification, and the flexible verification cycle and time period division enhance the pertinence of verification; In the present invention, the form data analysis module is used to perform multi-time point sampling and mean calculation to provide stable and reliable evaluation results. The three key indicators of completion rate, filling rate and filling time are used in combination with a preset proportional factor to comprehensively evaluate the degree of form optimization. The automated threshold trigger mechanism improves management efficiency and detects problems in a timely manner, ultimately improving user experience and data quality. In the present invention, key performance indicators are monitored in real time through a performance monitoring and optimization module, and a system performance evaluation coefficient is calculated by weighted calculation through a preset proportional factor. After the coefficient is compared with a preset threshold, an adjustment mechanism is automatically triggered, and potential performance problems can be solved without human intervention, thereby improving operation and maintenance efficiency. The configurable parameters enhance the flexibility and adaptability of the system, ensure stable operation, and improve user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system block diagram of the present invention; Figure 2 It is a schematic diagram of the method flow of the present invention; Figure 3 It is a logical flow diagram of the present invention. DETAILED DESCRIPTION

[0018] The following will combine the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all the embodiments. All other embodiments obtained by ordinary technicians in this field without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0019] Embodiment 1: Figure 1 As shown, the present invention proposes a complex form custom real-time verification system of data framework technology, including a form management platform, a data model definition module, a verification rule configuration module, a real-time verification engine module, a form data analysis module and a performance monitoring and optimization module; Data model definition module, which defines the form structure and default values, supports basic and complex data initialization, validation rule setting, and data formatting and parsing; The specific process of defining and managing the form by the data model definition module is as follows: The form structure defines the properties of each field in the form. The definition of each field includes the following elements: Field type: specifies the data type of the field (such as text, number, date, Boolean, single choice, multiple choice, drop-down list, etc.). The field type determines the type of data that users can enter and how it is presented on the interface (for example, a date type field usually displays a date picker); Field label: used to display the name or description of the field on the user interface (such as name, age, email address, etc.); Default value: The initial value of the field, displayed when the user does not enter any value (for example, setting the default value of a date field to the current date and setting the default value of a city field to the user's current city); Required: Specifies whether the field is required. If it is set to required, users must fill in the field before submitting the form. Data initialization refers to setting initial values ​​for form fields. The data initialization process for basic and complex types is as follows: Basic types include strings, numbers, and Boolean values. For these types, you can directly assign values ​​to the defaultValue property (for example, assign the string "Sample Text" to the default value of a text box, assign the number 10 to the default value of a number input box, and assign the Boolean value true to the default value of a check box); Complex types include arrays and objects. For array types, you can use an array containing initial values ​​to initialize (such as defaultValue: ["option 1", "option 2"]); for object types, you can use an object containing key-value pairs to initialize (such as defaultValue: {"name": "Zhang San", "age": 20}); Validation rules are used to ensure that the data entered by the user complies with the predetermined format and requirements. The validation rules are as follows: Required fields: specify that a field must be filled in and cannot be left blank (for example, username and password in a registration form are usually set as required items); String length limit: limit the minimum and maximum length of a string (for example, a password must be at least 8 characters long, and a username cannot exceed 20 characters); Numeric range restriction: limit the minimum and maximum values ​​of the value (for example, age must be greater than 0 and less than 120); Regular expression matching: Use regular expressions to verify the format of a string (for example, verifying an email address, phone number, etc.); Custom validation function: allows developers to write custom validation functions to meet specific validation requirements; Data formatting and parsing are used to convert data between the user interface and the data storage. Data formatting is to convert data into a user-friendly format for display (for example, formatting a date object into a string format of YYYY-MM-DD, formatting a number into a string with thousands separators, or converting a Boolean value to "yes" or "no"). Data parsing is to convert a string input by the user into a data type that the program can handle (for example, parsing a date string in the format of YYYY-MM-DD into a date object, parsing a number string with thousands separators into a number, or parsing "yes" or "no" into a Boolean value); The data model definition module enhances data consistency and accuracy by setting validation rules, while providing default values ​​and friendly data formatting to improve user experience; it simplifies development work and supports flexible data processing capabilities, making it possible to quickly build and adjust forms; in addition, the module is easy to maintain and expand, and helps improve application security, thereby greatly improving user experience and development efficiency while ensuring high-quality data input.

[0020] Verification rule configuration module, which configures the verification logic of form items, supports basic rules, complex function definitions and asynchronous backend verification; The specific process of the verification rule configuration module configuring the verification logic of the form item is as follows: Verification logic configuration: associate verification rules with form items. One form item can correspond to multiple verification rules. When the user submits the form, the system will execute these rules in sequence and decide whether to allow the form submission based on the verification results. Basic rule verification: Basic rules are predefined validation rules. Basic rules include: checking whether the field value is empty, checking whether the length of the string is within the specified range, checking whether the value is within the specified range, and using regular expressions to check whether the string conforms to a specific format; Complex function definition: For some complex validation logic, custom functions can be used to implement them. These functions accept the value of the form item as input and return a Boolean value to indicate whether the validation is passed; Asynchronous backend verification: Asynchronous backend means that the frontend communicates with the backend server through asynchronous requests. Some verification logic requires access to backend data to complete (for example, verifying whether the username already exists). The asynchronous backend verification process is as follows: After the form inputs data, the frontend sends the data to the backend; The backend performs verification according to the verification rules; The backend returns the verification result to the frontend; The validation rule configuration module ensures the accuracy and completeness of form data through basic rules, custom functions, and asynchronous backend validation. Basic rules quickly check common problems such as null values ​​and formats; custom functions implement complex logic; and asynchronous backend validation handles situations that require server resources, such as user name uniqueness. This not only improves user experience and ensures data quality, but also enhances application security and development efficiency.

[0021] The real-time verification engine module verifies user input immediately, provides feedback on whether the user input meets the requirements based on the verification results, and provides clear error prompts; The specific process of the real-time verification engine module verifying the user input is as follows: Get the user's form input data, classify the form input data according to the configured verification logic, and divide it into basic rules, complex functions, and asynchronous backends for verification. First, use the basic rules for preliminary verification, generate a verification cycle, and divide the verification cycle JZ into multiple verification time periods {jz1, jz2, ..., jzn}, that is, JZ={jz1, jz2, ..., jzn}; Obtain the field value deviation value of the form input data in multiple verification periods, the field deviation value represents the absolute value of the difference between the actual input value and the expected value, and calculate the arithmetic mean of the multiple field value deviation values ​​obtained, and record the arithmetic mean of the multiple field value deviation values ​​as the average field value deviation value PZD; Obtain string length deviation values ​​of the form input data in multiple verification periods, the string length deviation value represents the absolute value of the difference between the actual input string length value and the preset length value, and calculate the arithmetic mean of the obtained multiple string length deviation values, and record the arithmetic mean of the multiple string length deviation values ​​as the average string length deviation value PZF; Obtain the numerical deviation rate of the form input data in multiple test periods. The numerical deviation rate represents the ratio of the absolute value of the difference between the actual input value and the preset value to the preset value. This is used to construct a set A of numerical deviation rates, and the mean of the difference between the largest subset and the smallest subset in set A is recorded as the numerical deviation rate difference SPC; Obtain the string format deviation rate of the form input data in multiple verification periods. The string format deviation rate indicates the degree of matching between the input string and the preset format. Use this to construct a set B of string format deviation rates, and record the mean of the difference between the largest subset and the smallest subset in set B as the string format deviation rate difference ZGC. The calculation process of the string format deviation rate is as follows: Define preset formats: Use regular expressions to represent preset formats; Decompose regular expressions: decompose regular expressions into multiple parts; Check matches one by one: Check whether the input string matches each part one by one; Calculate the matching rate: Calculate the matching rate based on the number of matched parts and the total number of parts; The average field value deviation value PZD, the average string length deviation value PZF, the numerical deviation rate difference value SPC, and the string format deviation rate difference value ZGC are obtained, and the average field value deviation value PZD, the average string length deviation value PZF, the numerical deviation rate difference value SPC, and the string format deviation rate difference value ZGC are combined to construct a basic rule verification feature matrix JT; The constructed feature matrix JT is used as the input of the machine learning model, and the label vector u is used as the output of the machine learning model. The label vector u represents the verification status of the basic rule verification of the form input data. The output of the label vector u is 0 or 1. 0 represents that the form input data has not passed the preliminary verification of the basic rules, and 1 represents that the form input data has passed the preliminary verification of the basic rules. The label vector u is used as the prediction target, and the sum of the prediction errors of all training data is minimized as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence. The training is stopped to obtain a machine learning model that predicts the label vector u. The machine learning model type here is a logistic regression model. Collect users' real-time form input data and build the basic rule verification feature matrix JT 实时, the verification status of the form input data for basic rule verification is obtained through the trained machine learning model; If the label vector u output by the machine learning model is 0, it means that the form input data has not passed the preliminary verification of the basic rules; If the label vector u output by the machine learning model is 1, it means that the form input data has passed the preliminary verification of the basic rules; After the form input data passes the preliminary verification of the basic rules, a complex function is used to perform a secondary verification, obtain the Boolean value within the verification period, and compare the obtained Boolean value with the preset Boolean value: If the obtained Boolean value is consistent with the preset Boolean value, it means that the form input data has passed the secondary verification of the complex function; If the obtained Boolean value is inconsistent with the preset Boolean value, it means that the form input data has not passed the secondary verification of the complex function; After the form input data passes the front-end verification of basic rules and complex functions, the form input data is submitted to the back-end for asynchronous back-end verification, the Boolean value within the verification cycle is obtained, and the obtained Boolean value is compared with the preset Boolean value: If the obtained Boolean value is consistent with the preset Boolean value, it means that the form input data has passed the asynchronous backend verification; If the obtained Boolean value is inconsistent with the preset Boolean value, it means that the form input data has not passed the asynchronous backend verification; When the form input data passes the basic rules, complex functions, and asynchronous backend verification, it means that the user input meets the requirements and the form submission is allowed; When the form input data fails to pass the basic rules, complex functions, and asynchronous backend validation, it means that the user input does not meet the requirements and the form submission is not allowed; When performing basic rule verification on form input data, if the basic rule verification fails, a red border will be displayed, and the text inside the red border will indicate that the basic rule verification has failed; When performing complex function verification on form input data, if the complex function verification fails, a red border will be displayed, and the text inside the red border will indicate that the complex function verification failed; When performing asynchronous backend verification on form input data, if the asynchronous backend verification fails, a red border will be displayed, and the text inside the red border will indicate that the asynchronous backend verification failed; When the basic rules, complex functions, and asynchronous backend validation of the form input data are passed, a green border will be displayed, and the text of the green border will indicate that the validation is successful; Through the real-time verification engine module, layered verification, machine learning and multi-dimensional analysis are carried out to achieve efficient, accurate and real-time form data verification, improve user experience and system performance. The layered verification mechanism avoids unnecessary calculations and back-end calls, the machine learning model improves verification accuracy, the real-time verification feature shortens user waiting time, and the multi-dimensional verification of values ​​and formats ensures the comprehensiveness of verification. Flexible verification cycles and time period divisions enhance the targeted nature of verification.

[0022] Embodiment 2: The technical solution of the embodiment of the present invention is different from that of embodiment 1 in that: like Figure 1 and Figure 3 As shown, the form data analysis module analyzes the submitted form data and optimizes the form according to the analysis results; The specific process of the form data analysis module analyzing the form data and optimizing the form is as follows: Obtain the submitted form data, generate a collection cycle, divide the collection cycle into m consecutive sub-cycles, and mark the midpoint time of each sub-cycle to obtain m midpoint times; Taking m midpoint moments as the base point, expand forward and backward by equal lengths, mark s-1 expansion moments, and then aggregate the midpoint moments and s-1 expansion moments to obtain s detection moments; Obtain the form completion rate at s detection moments, obtain the completion rate of s detection forms, and accumulate and average the completion rates of the s detection forms to obtain the completion rate of m sub-forms; The expression of sub-expression completion rate is: ; In the formula, BD zm is the subform completion rate of the mth subcycle, BD jcmn is the completion rate of the nth test form in the mth sub-cycle; Remove the maximum and minimum values ​​of the subform completion rate, and average the remaining m-2 subform completion rates to obtain the mean form completion rate. The expression for the mean form completion rate is: ; In the formula, BD jz is the mean form completion rate, BD zp is the subform completion rate of the pth sub-period; Calculate the mean form completion rate BD through the above jz The mean value of field completion rate ZD can be obtained by the same method. jz、 Fill in the mean time SJ jz， The form optimization evaluation factor BYP is calculated using the following formula: ; Among them, e1, e2 and e3 are all preset proportional factor coefficients, e3>e2>e1>0, and the form optimization evaluation coefficient BYP is compared with the preset form optimization evaluation coefficient threshold: If the form optimization evaluation coefficient BYP is greater than or equal to the preset form optimization evaluation coefficient threshold, no signal is generated; If the form optimization evaluation coefficient BYP is less than the preset form optimization evaluation coefficient threshold, a form optimization signal is generated and sent to the form management platform; After receiving the signal that the form needs to be optimized, the form management platform immediately takes measures to optimize the form. The optimization measures are as follows: Simplify your forms by removing unnecessary fields or breaking them into multiple steps; Optimize the order of fields, put important fields first, and put related fields together; Improve field prompts and provide clear field prompts to help users understand the meaning and filling requirements of the fields; Provide default values ​​for certain fields to reduce the burden on users to fill in; The form data analysis module performs multi-time point sampling and mean calculation to provide stable and reliable evaluation results. It uses the three key indicators of completion rate, filling rate and filling time, combined with preset proportional factors, to comprehensively evaluate the degree of form optimization. The automated threshold trigger mechanism improves management efficiency and detects problems in a timely manner, ultimately improving user experience and data quality.

[0023] Performance monitoring and optimization module monitors system performance indicators in real time and automatically takes measures to make adjustments based on the monitoring situation; The specific process of the performance monitoring and optimization module monitoring system performance indicators and automatically taking measures is as follows: Get the real-time performance indicator data of the system during operation. The real-time performance indicator data includes the real-time verification response time SJX, the real-time CPU usage SCS and the real-time memory usage SNS. The real-time verification response time indicates the time required from the user entering data in the form to the system completing the verification and returning the result. The real-time CPU usage indicates the percentage of CPU resources occupied in the system at any given moment. The real-time memory usage indicates the proportion of the system's used memory to the total memory at any given moment. The system performance evaluation coefficient XXP is obtained by the following formula: ; Where f1, f2 and f3 are all preset proportional factor coefficients, f3>f2>f1>0, and the system performance evaluation coefficient XXP is compared with the preset system performance evaluation coefficient threshold: If the system performance evaluation coefficient XXP is less than the preset system performance evaluation coefficient threshold, it indicates that the system is operating well and no signal is generated; If the system performance evaluation coefficient XXP is greater than or equal to the preset system performance evaluation coefficient threshold, it indicates that the system operation performance is poor, and a system operation adjustment signal is generated and sent to the form management platform; After receiving the system operation adjustment signal, the form management platform immediately takes measures to adjust the system operation. The adjustment measures are as follows: Dynamically adjust the verification frequency according to the system load; For the same input data, cache the verification results to avoid repeated verification; Simplify verification rules and reduce unnecessary calculations; Put time-consuming verification tasks into the background for asynchronous execution; Distribute verification tasks to multiple servers for execution; The performance monitoring and optimization module monitors key performance indicators in real time, and calculates the system performance evaluation coefficient through weighted calculation using preset proportional factors. After the coefficient is compared with the preset threshold, the adjustment mechanism is automatically triggered to solve potential performance problems without human intervention, thereby improving operation and maintenance efficiency. The configurable parameters enhance the flexibility and adaptability of the system, ensuring stable operation and improving user experience.

[0024] Embodiment 3: The technical solution of the embodiment of the present invention is different from that of Embodiment 1 and Embodiment 2 in that: like Figure 2 As shown, a complex form custom real-time verification method using data framework technology includes the following steps: Step 1: Build a form by setting field types, labels, default values, and required attributes, support data initialization and validation rules, and perform data formatting and parsing; Step 2: Configure the validation logic of form items by associating basic rules and custom functions, and support asynchronous backend validation; Step 3: Obtain user input, classify it according to the verification logic, and perform basic rule verification, complex function verification, and asynchronous backend verification on the user input; Step 4: Generate an optimization coefficient by calculating the completion rate, filling rate and time average, and generate an optimization signal when the coefficient is lower than the preset threshold, prompting the form management platform to optimize; Step 5: Calculate the performance evaluation coefficient by real-time monitoring and verification of response time, CPU usage, and memory usage. When the coefficient exceeds the threshold, an adjustment signal is generated to optimize system operation. This custom real-time validation method for complex forms ensures data accuracy and consistency by setting field attributes and configuring multi-layer validation logic. It performs classification validation based on user input, calculates completion rate, filling rate and time average to generate optimization signals and improve user experience. At the same time, it monitors system performance indicators in real time and automatically adjusts to optimize operating efficiency, thereby achieving efficient data management and system performance optimization.

[0025] The above are only preferred specific implementation modes of the present invention; however, the protection scope of the present invention is not limited thereto; any technician familiar with the technical field within the technical scope disclosed by the present invention; any equivalent replacement or change based on the technical solution and improved concept of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A complex form custom real-time verification system based on data framework technology, characterized in that: It includes form management platform, data model definition module, verification rule configuration module, real-time verification engine module, form data analysis module and performance monitoring and optimization module; Data model definition module, which defines the form structure and default values, supports basic and complex data initialization, validation rule setting, and data formatting and parsing; Verification rule configuration module, which configures the verification logic of form items, supports basic rules, complex function definitions and asynchronous backend verification; The real-time verification engine module verifies user input immediately, provides feedback on whether the user input meets the requirements based on the verification results, and provides clear error prompts; The form data analysis module analyzes the submitted form data and optimizes the form based on the analysis results; The performance monitoring and optimization module monitors system performance indicators in real time and automatically takes measures to make adjustments based on the monitoring situation.

2. According to the complex form self-defined real-time verification system of the data framework technology of claim 1, it is characterized by: The specific process of defining and managing the form by the data model definition module is as follows: The form structure defines the properties of each field in the form. The definition of each field includes the following elements: Field type: specifies the data type of the field. The field type determines the type of data that users can enter and how it is presented on the interface; Field label: used to display the name or description of the field in the user interface; Default value: the initial value of the field, displayed when the user does not enter any value; Required: Specifies whether the field is required. If it is set to required, users must fill in the field before submitting the form. Data initialization refers to setting initial values ​​for form fields. The data initialization process for basic and complex types is as follows: Basic types include strings, numbers, and Boolean values. For these types, you can directly assign values ​​to the defaultValue attribute; Complex types include arrays and objects. For array types, you can use an array containing initial values ​​to initialize them. Validation rules are used to ensure that the data entered by the user complies with the predetermined format and requirements. The validation rules are as follows: Required field: specifies that a field must be filled in and cannot be left blank; String length limit: limit the minimum and maximum length of the string; Numerical range limit: limit the minimum and maximum values ​​of the value; Regular expression matching: Use regular expressions to verify the format of a string; Custom validation function: allows developers to write custom validation functions to meet specific validation requirements; Data formatting and parsing are used to convert data between the user interface and the data storage. Data formatting is to convert data into a user-friendly format for display, and data parsing is to convert the string input by the user into a data type that the program can process.

3. According to the complex form self-defined real-time verification system of the data framework technology of claim 2, it is characterized in that: The specific process of the verification rule configuration module configuring the verification logic of the form item is as follows: Verification logic configuration: associate verification rules with form items. One form item can correspond to multiple verification rules. When the user submits the form, the system will execute these rules in sequence and decide whether to allow the form submission based on the verification results. Basic rule verification: Basic rules are predefined validation rules. Basic rules include: checking whether the field value is empty, checking whether the length of the string is within the specified range, checking whether the value is within the specified range, and using regular expressions to check whether the string conforms to a specific format; Complex function definition: For some complex validation logic, custom functions can be used to implement them. These functions accept the value of the form item as input and return a Boolean value to indicate whether the validation is passed; Asynchronous backend verification: Asynchronous backend means that the front end communicates with the backend server through asynchronous requests. Some verification logic requires access to backend data to complete.

4. According to the complex form self-defined real-time verification system of the data framework technology of claim 1, it is characterized in that: The specific process of the real-time verification engine module verifying user input is as follows: Get the user's form input data, classify the form input data according to the configured verification logic, and divide it into basic rules, complex functions, and asynchronous backends for verification. First, use the basic rules for preliminary verification, generate a verification cycle, and divide the verification cycle JZ into multiple verification time periods {jz1, jz2, ..., jzn}, that is, JZ={jz1, jz2, ..., jzn}; Obtain the field value deviation value of the form input data in multiple verification periods, the field deviation value represents the absolute value of the difference between the actual input value and the expected value, and calculate the arithmetic mean of the multiple field value deviation values ​​obtained, and record the arithmetic mean of the multiple field value deviation values ​​as the average field value deviation value PZD; The string length deviation value of the form input data in multiple verification periods is obtained. The string length deviation value represents the absolute value of the difference between the actual input string length value and the preset length value. The arithmetic mean of the multiple string length deviation values ​​obtained is calculated, and the arithmetic mean of the multiple string length deviation values ​​is recorded as the average string length deviation value PZF.

5. According to the complex form self-defined real-time verification system of the data framework technology of claim 4, it is characterized in that: Obtain the numerical deviation rate of the form input data in multiple test periods. The numerical deviation rate represents the ratio of the absolute value of the difference between the actual input value and the preset value to the preset value. This is used to construct a set A of numerical deviation rates, and the mean of the difference between the largest subset and the smallest subset in set A is recorded as the numerical deviation rate difference SPC; The string format deviation rate of the form input data in multiple verification periods is obtained. The string format deviation rate indicates the degree of match between the input string and the preset format. A set B of string format deviation rates is constructed based on this, and the mean of the difference between the largest subset and the smallest subset in set B is recorded as the string format deviation rate difference ZGC.

6. According to the complex form self-defined real-time verification system of the data framework technology of claim 5, it is characterized in that: The average field value deviation value PZD, the average string length deviation value PZF, the numerical deviation rate difference value SPC, and the string format deviation rate difference value ZGC are obtained, and the average field value deviation value PZD, the average string length deviation value PZF, the numerical deviation rate difference value SPC, and the string format deviation rate difference value ZGC are combined to construct a basic rule verification feature matrix JT; The constructed feature matrix JT is used as the input of the machine learning model, and the label vector u is used as the output of the machine learning model. The label vector u represents the verification status of the form input data for basic rule verification. The output of the label vector u is 0 or 1. 0 represents that the form input data has not passed the preliminary verification of the basic rules, and 1 represents that the form input data has passed the preliminary verification of the basic rules. The label vector u is used as the prediction target, and the sum of the prediction errors of all training data is minimized as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the training is stopped to obtain a machine learning model that predicts the label vector u. Collect users' real-time form input data and build the basic rule verification feature matrix JT 实时 , the verification status of the form input data for basic rule verification is obtained through the trained machine learning model; If the label vector u output by the machine learning model is 0, it means that the form input data has not passed the preliminary verification of the basic rules; If the label vector u output by the machine learning model is 1, it means that the form input data has passed the preliminary verification of the basic rules.

7. The complex form self-defined real-time verification system based on data framework technology according to claim 6 is characterized in that: After the form input data passes the preliminary verification of the basic rules, a complex function is used to perform a secondary verification, obtain the Boolean value within the verification period, and compare the obtained Boolean value with the preset Boolean value: If the obtained Boolean value is consistent with the preset Boolean value, it means that the form input data has passed the secondary verification of the complex function; If the obtained Boolean value is inconsistent with the preset Boolean value, it means that the form input data has not passed the secondary verification of the complex function; After the form input data passes the front-end verification of basic rules and complex functions, the form input data is submitted to the back-end for asynchronous back-end verification, the Boolean value within the verification cycle is obtained, and the obtained Boolean value is compared with the preset Boolean value: If the obtained Boolean value is consistent with the preset Boolean value, it means that the form input data has passed the asynchronous backend verification; If the obtained Boolean value is inconsistent with the preset Boolean value, it means that the form input data has not passed the asynchronous backend verification.

8. According to the complex form self-defined real-time verification system of the data framework technology of claim 1, it is characterized in that: The specific process of the form data analysis module analyzing the form data and optimizing the form is as follows: Obtain the submitted form data, generate a collection cycle, divide the collection cycle into m consecutive sub-cycles, and mark the midpoint time of each sub-cycle to obtain m midpoint times; Taking m midpoint moments as the base point, expand forward and backward by equal time lengths, mark s-1 expansion moments, and then summarize the midpoint moments and s-1 expansion moments to obtain s detection moments; Obtain the form completion rate at s detection moments, obtain the completion rate of s detection forms, and accumulate and average the completion rates of the s detection forms to obtain the completion rate of m sub-forms; The expression of sub-expression completion rate is: ; In the formula, BD zm is the subform completion rate of the mth subcycle, BD jcmn is the completion rate of the nth test form in the mth sub-cycle; Remove the maximum and minimum values ​​of the subform completion rate, and average the remaining m-2 subform completion rates to obtain the mean form completion rate. The expression for the mean form completion rate is: ; In the formula, BD jz is the mean form completion rate, BD zp is the subform completion rate of the pth sub-period; Using the method of calculating the mean form completion rate, we can also get the mean field completion rate ZD jz、 Fill in the mean time SJ jz， The form optimization evaluation factor BYP is calculated using the following formula: ； Among them, e1, e2 and e3 are all preset proportional factor coefficients, e3>e2>e1>0, and the form optimization evaluation coefficient BYP is compared with the preset form optimization evaluation coefficient threshold: If the form optimization evaluation coefficient BYP is greater than or equal to the preset form optimization evaluation coefficient threshold, no signal is generated; If the form optimization evaluation coefficient BYP is less than the preset form optimization evaluation coefficient threshold, a form optimization signal is generated and sent to the form management platform; After the form management platform receives the signal that the form needs to be optimized, it immediately takes measures to optimize the form.

9. The complex form self-defined real-time verification system based on data framework technology according to claim 1 is characterized in that: The specific process of the performance monitoring and optimization module monitoring system performance indicators and automatically taking measures is as follows: Get the real-time performance indicator data of the system during operation. The real-time performance indicator data includes the real-time verification response time SJX, the real-time CPU usage SCS and the real-time memory usage SNS. The real-time verification response time indicates the time required from the user entering data in the form to the system completing the verification and returning the result. The real-time CPU usage indicates the percentage of CPU resources occupied in the system at any given moment. The real-time memory usage indicates the proportion of the system's used memory to the total memory at any given moment. The system performance evaluation coefficient XXP is obtained by the following formula: ； Where f1, f2 and f3 are all preset proportional factor coefficients, f3>f2>f1>0, and the system performance evaluation coefficient XXP is compared with the preset system performance evaluation coefficient threshold: If the system performance evaluation coefficient XXP is less than the preset system performance evaluation coefficient threshold, it indicates that the system is operating well and no signal is generated; If the system performance evaluation coefficient XXP is greater than or equal to the preset system performance evaluation coefficient threshold, it indicates that the system operation performance is poor, and a system operation adjustment signal is generated and sent to the form management platform; After receiving the system operation adjustment signal, the form management platform immediately takes measures to adjust the system operation.

10. A complex form custom real-time verification method based on data framework technology, characterized in that: The following steps are involved: Step 1: Build a form by setting field types, labels, default values, and required attributes, support data initialization and validation rules, and perform data formatting and parsing; Step 2: Configure the validation logic of form items by associating basic rules and custom functions, and support asynchronous backend validation; Step 3: Obtain user input, classify it according to the verification logic, and perform basic rule verification, complex function verification, and asynchronous backend verification on the user input; Step 4: Generate an optimization coefficient by calculating the completion rate, filling rate and time average, and generate an optimization signal when the coefficient is lower than the preset threshold, prompting the form management platform to optimize; Step 5: Calculate the performance evaluation coefficient by real-time monitoring and verification of response time, CPU usage, and memory usage. When the coefficient exceeds the threshold, an adjustment signal is generated to optimize system operation.

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