A data monitoring system and method based on a low-code platform

Abstract

This invention relates to the field of data monitoring technology, and in particular to a data monitoring system and method based on a low-code platform, comprising: a visual design module, a code editing module, an error reporting module, a data collection module, a sensitive data processing module, and an exception handling module. This invention determines the anomaly tendency category of the project under development based on code call characterization values, determines whether to correct the core thread count based on the anomaly tendency category, and identifies sensitive data in each piece of code; processes sensitive data based on sensitive data distribution characterization values, corrects function configuration parameters based on syntax error change values, and allocates computing resources according to the actual situation of the project, thereby improving the data management efficiency of the low-code platform.

Description

Technical Field

[0001] This invention relates to the field of data monitoring technology, and in particular to a data monitoring system and method based on a low-code platform. Background Technology

[0002] As enterprises deepen their digital transformation, the complexity of business processes and the diversity of data are increasing dramatically, making the need for rapid application development and deployment to cope with market changes increasingly urgent. Low-code development platforms have emerged to address this need. Through a visual graphical interface and pre-built components, they allow developers and ordinary business personnel to quickly build applications by dragging and dropping and configuring, significantly reducing the programming threshold and application development cycle.

[0003] In various applications of low-code platforms, data monitoring systems are a crucial component, used for real-time monitoring, analysis, and early warning of massive amounts of data generated by business systems and IoT devices. However, existing low-code platforms suffer from low efficiency in building such systems due to insufficient user expertise.

[0004] Chinese Patent Publication No. CN120067080A discloses a data acquisition method and system based on a low-code development platform. The method includes: automatically identifying and adapting to connection methods of various data sources using predefined connectors; automatically inferring relationships between different data sources; performing data mapping and transformation; automatically defining data acquisition logic using predefined rules, including filtering conditions and sorting rules; automatically executing data acquisition operations according to a plan; immediately triggering data acquisition operations when data sources change; handling encryption and access control for sensitive data; automatically testing acquired data to verify the normal operation of the data acquisition process; automatically recording monitoring data; and automatically handling and taking measures against anomalies during data acquisition. However, the above technical solution has the following problems: it does not consider allocating computing resources according to the actual situation of the project, affecting the data management efficiency of the low-code platform. Summary of the Invention

[0005] To address this issue, the present invention provides a data monitoring system and method based on a low-code platform, which overcomes the problem in the prior art that the allocation of computing resources according to the actual situation of the project is not considered, thus affecting the data management efficiency of the low-code platform.

[0006] On one hand, the present invention provides a data monitoring system based on a low-code platform, comprising:

[0007] The visual design module is used to configure the functionality of the project to be developed.

[0008] A code editing module, which is connected to the visual design module, is used to generate corresponding code blocks based on the functions configured in the visual design module;

[0009] An error reporting module, which is connected to the code editing module, is used to implement syntax analysis error reporting and syntax analysis error reporting;

[0010] The data collection module, which is connected to the visualization design module and the code editing module respectively, is used to determine the abnormal tendency category of the project to be developed based on the code call characterization value, and to determine whether to correct the core thread count and identify sensitive data in each code based on the abnormal tendency category.

[0011] A sensitive data processing module, which is connected to the code editing module and the data collection module respectively, is used to process sensitive data based on the sensitive data distribution characterization value, including adding a new function call block or determining whether to modify the function configuration parameters based on the data filtering rate.

[0012] An exception handling module is connected to the sensitive data processing module and the error reporting module, respectively, and is used to configure parameters based on the syntax error change value correction function. This includes correcting the static analysis cycle for syntax analysis error reporting based on the syntax error change value correction, or correcting the update frequency of the visualization design module based on the impact on the data filtering rate.

[0013] Furthermore, the data collection module is used to periodically determine the abnormal tendency category of the project under development based on the code call characterization value, including:

[0014] Used to determine the code call representation value based on the number of configuration bytes and the number of newly added bytes;

[0015] This is used to classify projects under development into a category with a strong tendency to cause anomalies when the code call characteristic value is greater than the preset code call characteristic value.

[0016] Furthermore, the data collection module is used to classify the project to be developed into a weak anomaly tendency category when the code call characterization value is less than or equal to a preset code call characterization value.

[0017] Furthermore, the data collection module is used to determine whether to identify sensitive data in each piece of code based on the anomaly tendency category, and to correct the core identification of the number of threads in each piece of code, including:

[0018] This is used to identify sensitive data in each piece of code and correct the core thread count when the project under development is classified as having a strong tendency to cause anomalies.

[0019] Furthermore, the sensitive data processing module is used to process sensitive data based on the sensitive data distribution representation value, including:

[0020] Used to identify consecutive sensitive data within a preset analysis period as a single sensitive block;

[0021] Used to determine the sensitive data distribution characterization value based on the average number of bytes in each sensitive block and the number of newly added bytes;

[0022] Used to determine whether to correct the function configuration parameters based on the data screening rate when the sensitive data distribution characterization value is less than or equal to the preset sensitive distribution value;

[0023] Used to determine the data filtering rate based on the total number of bytes of each string in the sensitive data and the total number of bytes of the sensitive data within the preset analysis period;

[0024] This is a configuration parameter for a syntax error change value correction function when the data filtering rate is less than or equal to the preset data filtering rate.

[0025] Furthermore, the sensitive data processing module is used to add a function call block when the sensitive data distribution characterization value is greater than the preset sensitive distribution value;

[0026] The sensitive data processing module is used to control the visualization design module to continue running using the current operating parameters when the data filtering rate is greater than the preset data filtering rate.

[0027] Furthermore, the exception handling module is used to configure parameters based on the syntax error change value correction function, including:

[0028] Used to plot the error count time-domain curve based on the number of errors at several time points within a preset analysis period;

[0029] Used to obtain each peak in the error count time-domain curve, and to determine the valid peaks by identifying peaks that are greater than the average number of errors;

[0030] Used to determine the number of valid peaks as the syntax error change value;

[0031] Used to store code within a preset analysis period to the change database when the syntax error change value is less than or equal to the preset syntax error change value, and to adjust the update frequency of the visualization design module based on the data filtering rate.

[0032] The increase in update frequency is negatively correlated with the impact on data screening rate.

[0033] Furthermore, the exception handling module is used to adjust the static analysis cycle for syntax analysis errors based on the syntax error change value when the syntax error change value is greater than the preset syntax error change value.

[0034] The reduction in static analysis cycle is positively correlated with the change in syntax error rate.

[0035] Furthermore, the data collection module is used to control the visualization design module to continue running using the current operating parameters when the project to be developed is in a weak anomaly tendency category.

[0036] On the other hand, the present invention also provides a data monitoring method using the above-mentioned low-code platform-based data monitoring system, comprising:

[0037] The code call representation value is determined based on the code of the project to be developed, which is obtained periodically.

[0038] Determine the anomaly tendency category of the project to be developed based on code call characterization values;

[0039] Based on the abnormal tendency category, determine whether to identify sensitive data in each piece of code and correct the core thread count;

[0040] After identifying sensitive data, the sensitive data is processed based on the distribution characteristics of the sensitive data, including adding new function call blocks or determining whether to modify function configuration parameters based on the data filtering rate.

[0041] Configuration parameters for the syntax error change value correction function include the static analysis cycle used for syntax analysis error correction based on syntax error change value correction, or the update frequency of the visual design module used to configure the function of the project to be developed based on the impact of data screening rate correction.

[0042] Compared with existing technologies, the beneficial effects of this invention are that the data collection module periodically determines the abnormal tendency category of the project to be developed based on the code call representation value. The code call representation value is determined by the number of configured bytes and the number of newly added bytes. The number of configured bytes represents the stable and reliable code generated by the visual design module; the number of newly added bytes represents the sum of user-defined code and code configured by the visual design module. The code call representation value quantifies the project's dependence on custom code. When the code call representation value is greater than the preset code call representation value, it indicates that the proportion of user-written code with higher uncertainty and complexity is larger. The more custom logic there is, the higher the potential error risk and performance uncertainty. Projects under this condition are classified as having a strong abnormal tendency category.

[0043] Furthermore, for projects with a strong tendency to cause anomalies, their code is more complex and requires more real-time monitoring and computing resources at runtime, including more frequent syntax checks and logical analysis. In this case, increasing the number of core threads provides computing power for highly complex projects, reducing monitoring latency and lag while further improving data management efficiency on low-code platforms.

[0044] Furthermore, the higher the code call representation value, the higher the project's anomaly risk, the more monitoring and computing resources are required, and the greater the increase in the number of core threads. By dynamically adjusting resource allocation to intelligently distinguish between simple and complex projects, the efficiency of data management on low-code platforms is further improved.

[0045] Furthermore, the sensitive data distribution characterization value measures the concentration of user-defined code input. When the sensitive data distribution characterization value is greater than the preset sensitive distribution value, users tend to add large, continuous, and fully functional custom code blocks. In this case, the platform lacks a general functional module for that user. To address this, duplicate code is clustered and integrated into new function call blocks to improve function configuration. When the sensitive data distribution characterization value is less than or equal to the preset sensitive distribution value, users mostly make scattered, small-scale modifications. In this case, users adjust interface text or fine-tune logic. The decision to correct function configuration parameters is based on the impact data filtering rate. By generating reusable function call blocks, the probability of developers repeatedly writing the same code is reduced. This allows the low-code platform to generate user-specific function call blocks, providing precise data support for platform function iteration and further improving the data management efficiency of the low-code platform.

[0046] Furthermore, the decision to modify function configuration parameters is based on the impact data filtering rate, which reflects the nature of user modifications. When the impact data filtering rate is greater than the preset impact data filtering rate, the modification is mainly a string, belonging to user customization, and does not reflect platform functional defects, so no function configuration modification is needed. When the impact data filtering rate is less than or equal to the preset impact data filtering rate, code logic modifications are involved. Users modify the code to compensate for the shortcomings of the platform's existing configuration functions. In this case, the function configuration parameters are modified based on the syntax error change value, which improves development efficiency and further enhances the data management efficiency of the low-code platform.

[0047] Furthermore, based on the configuration parameters of the syntax error change value correction function, the syntax error change value reflects the fluctuation of errors. When the syntax error change value is less than or equal to the preset syntax error change value, there are fewer error peaks. In this case, the user has functional defects in the project and completes the deployment of the project after supplementing some code. For this case, the code is stored and the update frequency of the visual design module is increased. When the syntax error change value is greater than the preset syntax error change value, it indicates that the user frequently makes changes to eliminate errors. For this case, the static analysis cycle is reduced to provide error results more quickly, helping users to iterate quickly and further improving the data management efficiency of the low-code platform. Attached Figure Description

[0048] Figure 1 This is a block diagram of a data monitoring system based on a low-code platform according to an embodiment of the present invention;

[0049] Figure 2 This is a flowchart illustrating the steps of a data monitoring method based on a low-code platform according to an embodiment of the present invention.

[0050] Figure 3 This is a logical decision diagram of the data collection module in this embodiment of the invention for determining the abnormal tendency category of the project to be developed based on the code call characterization value;

[0051] Figure 4 This is a logic decision diagram for the sensitive data processing module in an embodiment of the present invention, which processes sensitive data based on the sensitive data distribution characterization value. Detailed Implementation

[0052] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0053] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0054] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0055] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0056] Please see Figure 1 The diagram shown is a block diagram of a data monitoring system based on a low-code platform according to an embodiment of the present invention. The system of the present invention includes:

[0057] The visual design module is used to configure the functionality of the project to be developed.

[0058] A code editing module, which is connected to the visual design module, is used to generate corresponding code blocks based on the functions configured in the visual design module;

[0059] An error reporting module, which is connected to the code editing module, is used to implement syntax analysis error reporting and syntax analysis error reporting;

[0060] The data collection module, which is connected to the visualization design module and the code editing module respectively, is used to determine the abnormal tendency category of the project to be developed based on the code call characterization value, and to determine whether to correct the core thread count and identify sensitive data in each code based on the abnormal tendency category.

[0061] A sensitive data processing module, which is connected to the code editing module and the data collection module respectively, is used to process sensitive data based on the sensitive data distribution characterization value, including adding a new function call block or determining whether to modify the function configuration parameters based on the data filtering rate.

[0062] An exception handling module is connected to the sensitive data processing module and the error reporting module, respectively, and is used to configure parameters based on the syntax error change value correction function. This includes correcting the static analysis cycle for syntax analysis error reporting based on the syntax error change value correction, or correcting the update frequency of the visualization design module based on the impact on the data filtering rate.

[0063] Please see Figure 2 The diagram shown is a flowchart illustrating the steps of a data monitoring method based on a low-code platform according to an embodiment of the present invention. The method of the present invention includes:

[0064] S1, determine the code call representation value based on the code of the project to be developed obtained periodically;

[0065] S2, determine the anomaly tendency category of the project to be developed based on code call representation values;

[0066] S3 determines whether to identify sensitive data in each piece of code and correct the core thread count based on the abnormal tendency category;

[0067] After identifying sensitive data, the sensitive data is processed based on the distribution characteristics of the sensitive data, including adding new function call blocks or determining whether to modify function configuration parameters based on the data filtering rate.

[0068] Configuration parameters for the syntax error change value correction function include the static analysis cycle used for syntax analysis error correction based on syntax error change value correction, or the update frequency of the visual design module used to configure the function of the project to be developed based on the impact of data screening rate correction.

[0069] Specifically, the code blocks generated by the code editing module can be added, deleted, and modified by the user;

[0070] Specifically, the error reporting module is used to implement syntax analysis error reporting and periodically run code style checks according to the static analysis cycle, including:

[0071] This is used to trigger syntax checking every time a key is pressed, and to trigger syntax analysis and report errors when the code structure does not conform to the language specification;

[0072] This is used to identify infinite loop structures during the execution of a single static analysis cycle in the error reporting module, and to trigger logical errors upon identification of an infinite loop structure in order to analyze the code logic.

[0073] Please see Figure 3 As shown, this is a logic diagram illustrating how the data collection module of this invention determines the abnormal tendency category of a project under development based on code call characterization values. The data collection module of this invention is used to periodically determine the abnormal tendency category of a project under development based on code call characterization values, including:

[0074] The number of bytes of the configuration code of the visualization design module within the preset analysis period is used to determine the number of configuration bytes;

[0075] This is used to determine the total number of bytes of newly added code in the code editing module within the preset analysis period as the number of newly added bytes;

[0076] This is used to calculate the ratio of the number of configured bytes to the number of newly added bytes, thus obtaining the configuration percentage.

[0077] This is used to calculate the difference between 1 and the configuration ratio to obtain the code call representation value;

[0078] If the code call representation value is less than or equal to the preset code call representation value, the project to be developed will be classified as a weak anomaly tendency category.

[0079] If the code call characteristic value is greater than the preset code call characteristic value, the project to be developed will be classified as a strong exception tendency category.

[0080] Specifically, the preset code call representation value is selected within the range [0.3, 0.4]. Those skilled in the art can select the preset code call representation value themselves, which can be determined by analyzing historical project data. The code call representation values ​​and anomaly occurrences of multiple projects are collected, and the critical value at which the anomaly occurrence increases significantly is determined as the code call representation value. In this embodiment, preferably, the preset code call representation value is 0.4.

[0081] Specifically, the data collection module is used to determine whether to identify sensitive data in each piece of code based on the anomaly tendency category, and to correct the core identification of the number of threads in each piece of code, including:

[0082] If the project to be developed is of the weak anomaly tendency category, then the visual design module will continue to run using the current running parameters;

[0083] If the project to be developed is of a strong tendency to cause anomalies, then identify the sensitive data in each piece of code and correct the core thread count.

[0084] Specifically, the data collection module periodically determines the anomaly tendency category of the project under development based on code call representation values. The code call representation value is determined by the number of configured bytes and the number of newly added bytes. The configured bytes represent the stable and reliable code generated by the visual design module; the number of newly added bytes represents the sum of user-defined code and code configured by the visual design module. The code call representation value quantifies the project's dependence on custom code. When the code call representation value is greater than the preset value, it indicates a larger proportion of user-written code with higher uncertainty and complexity. The more custom logic there is, the higher the potential error risk and performance uncertainty. Projects under development in this case are classified as having a strong anomaly tendency category.

[0085] Specifically, for projects with a strong tendency to fail, their code is more complex and requires more real-time monitoring and computing resources at runtime, including more frequent syntax checks and logical analysis. In this case, increasing the number of core threads provides computing power for highly complex projects, reducing monitoring latency and lag while further improving data management efficiency on low-code platforms.

[0086] Specifically, the code in the newly added code, excluding configuration code, will be identified as sensitive data.

[0087] Specifically, the data collection module is used to correct the core thread count based on the code call representation value, wherein,

[0088] The increase in the number of core threads is positively correlated with the code call representation value.

[0089] In this embodiment, optionally,

[0090] Compare the code call representation value with the preset thread correction comparison value;

[0091] If the code call representation value is less than or equal to the preset thread correction comparison value, the core thread count will be corrected to the initial core thread count plus one.

[0092] If the code call representation value is greater than the preset thread correction comparison value, the core thread count will be corrected to the initial core thread count plus two.

[0093] The default thread correction comparison value is 0.45.

[0094] Specifically, the higher the code call representation value, the higher the project's anomaly risk, the more monitoring and computing resources are required, and the greater the increase in the number of core threads. By dynamically adjusting resource allocation to intelligently distinguish between simple and complex projects, the efficiency of data management on low-code platforms is further improved.

[0095] Please see Figure 4 The diagram shown illustrates the logic decision of the sensitive data processing module in this invention, which processes sensitive data based on the distribution representation value of sensitive data. The sensitive data processing module of this invention is used to process sensitive data based on the distribution representation value of sensitive data, and includes:

[0096] Used to identify consecutive sensitive data within a preset analysis period as a single sensitive block;

[0097] The ratio of the average number of bytes in each sensitive block to the number of newly added bytes is used to determine the sensitive data distribution characteristic value;

[0098] If the sensitive data distribution characterization value is less than or equal to the preset sensitive distribution value, then the function configuration parameters are adjusted based on the impact on the data screening rate.

[0099] If the sensitive data distribution representation value is greater than the preset sensitive distribution value, a new function call block will be added.

[0100] Specifically, the preset sensitivity distribution value is selected within the range [0.11, 0.13]. Those skilled in the art can select the preset sensitivity distribution value themselves. It can be determined based on historical code analysis. Statistical analysis can be abstracted into the number of bytes of the sensitive blocks of reusable modules. It can be understood that it can be divided according to whether the amount of data of the code contained in each sensitive block can be integrated into a general function. In this embodiment, preferably, the preset sensitivity distribution value is 0.11.

[0101] Specifically, the sensitive data distribution characteristic value measures the concentration of user-defined code input. When the sensitive data distribution characteristic value is greater than the preset sensitive distribution value, users tend to add large, continuous, and fully functional custom code blocks. In this case, the platform lacks a general functional module for that user. To address this, duplicate code is clustered and integrated into new function call blocks to improve function configuration. When the sensitive data distribution characteristic value is less than or equal to the preset sensitive distribution value, users mostly make scattered, small-scale modifications. In this case, users adjust interface text or fine-tune logic. The decision to modify function configuration parameters is based on the impact data filtering rate. By generating reusable function call blocks, the probability of developers repeatedly writing the same code is reduced. This allows the low-code platform to generate user-specific function call blocks, providing precise data support for platform function iteration and further improving the data management efficiency of the low-code platform.

[0102] Specifically, the sensitive data processing module is used to add function call blocks, including:

[0103] Retrieve sensitive blocks from historical data;

[0104] Each sensitive block is divided into several sensitive categories;

[0105] The sensitive class with the largest number of sensitive blocks is identified as the new sensitive class, and the number of sensitive blocks is the total number of sensitive blocks within a single sensitive class.

[0106] Obtain the identical code from each sensitive block within the newly added sensitive class, and integrate the identical code into a new function call block.

[0107] Specifically, newly added call blocks can be named according to preset rules or can be renamed by the user.

[0108] Specifically, the sensitive data processing module is used to divide each sensitive block into several sensitive classes, including:

[0109] For each individual sensitive block, the code of that sensitive block is compared one by one with the code of the other sensitive blocks.

[0110] The ratio of the number of bytes of statements identical to those in a single sensitive block to the total number of bytes in the single sensitive block is determined as the code similarity between the two sensitive blocks;

[0111] The remaining sensitive blocks with a code similarity greater than the preset code similarity are grouped together with the single sensitive block into a single sensitive class;

[0112] If two sensitive classes contain the same sensitive blocks, then delete either of the sensitive classes.

[0113] Specifically, the preset code similarity is selected within the range of [0.89, 0.92]. Those skilled in the art can select and determine the preset code similarity themselves. The larger the preset code similarity, the more accurate the identification of the newly added function call block. In this embodiment, the preset code similarity is preferably 0.92.

[0114] Specifically, the sensitive data processing module is used to determine whether to correct the function configuration parameters based on the data filtering rate, including:

[0115] Obtain the ratio of the total number of bytes of each string in the sensitive data to the total number of bytes of the sensitive data within a preset analysis period to obtain the data removal rate.

[0116] If the impact data filtering rate is greater than the preset impact data filtering rate, the visualization design module will continue to run using the current operating parameters.

[0117] If the affected data filtering rate is less than or equal to the preset affected data filtering rate, the configuration parameters of the function will be corrected based on the syntax error change value.

[0118] Specifically, the decision to modify function configuration parameters is based on the impact data filtering rate, which reflects the nature of user modifications. When the impact data filtering rate is greater than the preset impact data filtering rate, the modification is mainly a string, which is a user customization and does not reflect platform functional defects, so no function configuration modification is needed. When the impact data filtering rate is less than or equal to the preset impact data filtering rate, code logic modifications are involved. Users modify the code to compensate for the shortcomings of the platform's existing configuration functions. In this case, function configuration parameters are modified based on syntax error changes, which improves development efficiency and further enhances data management efficiency for low-code platforms.

[0119] Specifically, the preset impact data screening rate is selected within the range of [0.78, 0.85]. Those skilled in the art can select the preset impact data screening rate themselves, which can be determined by analyzing the code change type: it can be understood that the classification of whether the modification involves structural changes can be realized. In this embodiment, preferably, the preset impact data screening rate is 0.8.

[0120] Specifically, the exception handling module is used to configure parameters based on the syntax error change value correction function, including:

[0121] Based on the number of errors at several time points within the preset analysis period, a time-domain curve of the number of errors is plotted.

[0122] Obtain the peaks in the time-domain curve of the number of errors, and determine the valid peaks as those peaks that are greater than the average number of errors.

[0123] The number of valid peaks is determined as the syntax error change value;

[0124] If the change value of syntax error is less than or equal to the preset change value of syntax error, the code within the preset analysis period will be stored in the change database, and the update frequency of the visualization design module will be adjusted based on the data filtering rate.

[0125] If the syntax error change value is greater than the preset syntax error change value, the static analysis cycle used for syntax analysis error reporting will be adjusted based on the syntax error change value.

[0126] Specifically, the preset syntax error change value is selected within the range [3, 4]. Those skilled in the art can select it themselves. It can be determined based on the development cycle simulation analysis. The average number of effective peaks in each preset analysis cycle during normal development is determined as the preset syntax error change value. In this embodiment, preferably, the preset syntax error change value is 3.

[0127] Specifically, based on the configuration parameters of the syntax error change value correction function, the syntax error change value reflects the fluctuation of errors. When the syntax error change value is less than or equal to the preset syntax error change value, there are fewer error peaks. In this case, the user has functional defects in the project and completes the deployment after adding some code. For this case, the code is stored and the update frequency of the visual design module is increased. When the syntax error change value is greater than the preset syntax error change value, it indicates that the user frequently makes changes to eliminate errors. For this case, the static analysis cycle is reduced to provide error results more quickly, helping users to iterate quickly and further improving the data management efficiency of the low-code platform.

[0128] Specifically, those skilled in the art can develop updated versions of the visual design module based on the code data in the modified database. This is existing technology and will not be elaborated further.

[0129] Specifically, the anomaly handling module is used to adjust the update frequency of the visualization design module based on the impact on the data filtering rate, wherein,

[0130] The increase in update frequency is negatively correlated with the impact on data screening rate.

[0131] In this embodiment, optionally,

[0132] Compare the data removal rate with the first and second removal rates;

[0133] If the data removal rate is less than or equal to the first removal rate, the update frequency of the visualization design module will be adjusted to 1.28 times the initial update frequency.

[0134] If the data removal rate is less than or equal to the second removal rate but greater than the first removal rate, the update frequency of the visualization design module will be adjusted to 1.16 times the initial update frequency.

[0135] If the impact on the data removal rate is greater than the second removal rate, then the update frequency of the visualization design module will be adjusted to 1.06 times the initial update frequency.

[0136] The first screening rate is 0.4%, and the second screening rate is 0.6%.

[0137] Specifically, the exception handling module is used to correct the static analysis cycle of syntax analysis errors based on the syntax error change value, wherein,

[0138] The reduction in static analysis cycle is positively correlated with the change in syntax error rate.

[0139] In this embodiment, optionally,

[0140] In this embodiment, optionally,

[0141] Compare the syntax error change value with the first preset syntax error change value and the second preset syntax error change value;

[0142] If the change value of the syntax error is less than or equal to the first preset change value of the syntax error, the static analysis cycle used for syntax analysis error reporting will be adjusted to 0.93 times the initial static analysis cycle.

[0143] If the change value of the syntax error is less than or equal to the second preset change value of the syntax error and greater than the first preset change value of the syntax error, the static analysis cycle used for syntax analysis error reporting will be adjusted to 0.88 times the initial static analysis cycle.

[0144] If the change value of the syntax error is greater than the second preset change value of the syntax error, the static analysis cycle used for syntax analysis error reporting will be adjusted to 0.83 times the initial static analysis cycle.

[0145] The first preset syntax error change value is 5, and the second preset syntax error change value is 7.

[0146] Specifically, the larger the change value of the syntax error, the more frequently users make changes, and the faster the feedback loop they need. Shortening the static analysis cycle to provide error results more quickly helps users iterate rapidly.

[0147] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0148] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data monitoring system based on a low-code platform, characterized in that, include: The visual design module is used to configure the functionality of the project to be developed. A code editing module, which is connected to the visual design module, is used to generate corresponding code blocks based on the functions configured in the visual design module; An error reporting module, which is connected to the code editing module, is used to implement syntax analysis error reporting and syntax analysis error reporting; The data collection module, which is connected to the visualization design module and the code editing module respectively, is used to determine the abnormal tendency category of the project to be developed based on the code call characterization value, and to determine whether to correct the core thread count and identify sensitive data in each code based on the abnormal tendency category. The data collection module is used to determine the number of bytes of the configuration code of the visualization design module within the preset analysis period as the configuration byte count; Used to determine the total number of new bytes of code added in the code editing module within the preset analysis period as the number of new bytes; used to calculate the ratio of the number of configured bytes to the number of new bytes to obtain the configuration ratio; used to calculate the difference between 1 and the configuration ratio to obtain the code call representation value; A sensitive data processing module, which is connected to the code editing module and the data collection module respectively, is used to process sensitive data based on the sensitive data distribution characterization value, including adding a new function call block or determining whether to modify the function configuration parameters based on the data filtering rate. The sensitive data processing module is used to identify continuous sensitive data within a preset analysis period as a single sensitive block; and to determine the ratio of the average number of bytes in each sensitive block to the number of newly added bytes as the sensitive data distribution characterization value. An exception handling module is connected to the sensitive data processing module and the error reporting module, respectively, and is used to configure parameters based on the syntax error change value correction function. This includes correcting the static analysis cycle for syntax analysis error reporting based on the syntax error change value correction, or correcting the update frequency of the visualization design module based on the impact on the data filtering rate.

2. The data monitoring system based on a low-code platform according to claim 1, characterized in that, The data collection module is used to periodically determine the abnormal tendency category of the project to be developed based on the code call characterization value, including: This is used to classify projects under development into a category with a strong tendency to cause anomalies when the code call characteristic value is greater than the preset code call characteristic value.

3. The data monitoring system based on a low-code platform according to claim 2, characterized in that, The data collection module is used to classify the project to be developed into a weak anomaly tendency category when the code call characterization value is less than or equal to a preset code call characterization value.

4. The data monitoring system based on a low-code platform according to claim 3, characterized in that, The data collection module is used to determine whether to identify sensitive data in each piece of code based on the anomaly tendency category, and to correct the core identification of the number of threads in each piece of code, including: This is used to identify sensitive data in each piece of code and correct the core thread count when the project under development is classified as having a strong tendency to cause anomalies.

5. The data monitoring system based on a low-code platform according to claim 4, characterized in that, The sensitive data processing module is used to process sensitive data based on the sensitive data distribution representation value, including: Used to determine whether to correct the function configuration parameters based on the data screening rate when the sensitive data distribution characterization value is less than or equal to the preset sensitive distribution value; Used to determine the data filtering rate based on the total number of bytes of each string in the sensitive data and the total number of bytes of the sensitive data within the preset analysis period; This is a configuration parameter for a syntax error change value correction function when the data filtering rate is less than or equal to the preset data filtering rate.

6. The data monitoring system based on a low-code platform according to claim 5, characterized in that, The sensitive data processing module is used to add a function call block when the sensitive data distribution characterization value is greater than the preset sensitive distribution value. The sensitive data processing module is used to control the visualization design module to continue running using the current operating parameters when the data filtering rate is greater than the preset data filtering rate.

7. The data monitoring system based on a low-code platform according to claim 6, characterized in that, The exception handling module is used to configure parameters for the syntax error change value correction function, including: Used to plot the error count time-domain curve based on the number of errors at several time points within a preset analysis period; Used to obtain each peak in the error count time-domain curve, and to determine the valid peaks by identifying peaks that are greater than the average number of errors; Used to determine the number of valid peaks as the syntax error change value; Used to store code within a preset analysis period to the change database when the syntax error change value is less than or equal to the preset syntax error change value, and to adjust the update frequency of the visualization design module based on the data filtering rate. The increase in update frequency is negatively correlated with the impact on data screening rate.

8. The data monitoring system based on a low-code platform according to claim 7, characterized in that, The exception handling module is used to adjust the static analysis cycle of syntax analysis errors based on the syntax error change value when the syntax error change value is greater than the preset syntax error change value. The reduction in static analysis cycle is positively correlated with the change in syntax error rate.

9. The data monitoring system based on a low-code platform according to claim 4, characterized in that, The data collection module is used to control the visualization design module to continue running using the current operating parameters when the project to be developed is in a weak anomaly tendency category.

10. A data monitoring method using the data monitoring system based on a low-code platform as described in any one of claims 1-9, characterized in that, The code call representation value is determined based on the code of the project to be developed, which is obtained periodically. Determine the anomaly tendency category of the project to be developed based on code call characterization values; Based on the abnormal tendency category, determine whether to identify sensitive data in each piece of code and correct the core thread count; After identifying sensitive data, the sensitive data is processed based on the distribution characteristics of the sensitive data, including adding new function call blocks or determining whether to modify function configuration parameters based on the data filtering rate. Configuration parameters for the syntax error change value correction function include the static analysis cycle used for syntax analysis error correction based on syntax error change value correction, or the update frequency of the visual design module used to configure the function of the project to be developed based on the impact of data screening rate correction.

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