Dynamic metadata analysis system and method based on JSON
By dynamically monitoring changes in the byte length of JSON documents and calculating hierarchical differences and metadata drift coefficients, the problem of parsing failure in traditional JSON parsing methods when faced with dynamic changes in metadata is solved. This achieves efficient and accurate data parsing, reduces waste of computing resources, and improves system stability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-05
- Publication Date
- 2026-04-07
AI Technical Summary
Traditional JSON parsing methods cannot effectively identify newly added fields, changed nesting structures, or missing nodes when dealing with dynamically changing metadata, leading to parsing failures, information loss, and system instability, thus reducing the automation level of data processing.
By dynamically monitoring the byte length changes of nodes at each level of the JSON document, quantitative analysis is performed to determine whether the current moment is suitable for data parsing. This includes modules for information acquisition, hierarchical differences, and cross-level differences. Hierarchical difference metrics and metadata drift coefficients are calculated to determine the comprehensive dynamic metadata parsing impact value and thus decide whether to proceed with parsing.
It accurately and efficiently determines whether the current moment is suitable for data parsing, avoids invalid parsing operations, significantly reduces the waste of computing resources, and improves the overall parsing efficiency.
Smart Images
Figure CN121809448A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data parsing technology, and more specifically, to a dynamic metadata parsing system and method based on JSON. Background Technology
[0002] With the widespread application of big data and microservice architecture, JSON, as a lightweight data exchange format, has become one of the mainstream formats for network data transmission and storage due to its ease of human reading and writing, as well as its ease of machine parsing and generation.
[0003] Traditional JSON parsing methods typically rely on predefined, static data schemas. This static approach struggles when dealing with JSON documents whose metadata changes dynamically. Parsers may throw exceptions due to their inability to recognize newly added fields, altered nested structures, or missing nodes, leading to parsing failures, information loss, or interruptions in the data processing pipeline. This not only impacts system stability and reliability but also significantly reduces the automation level of data processing, making it impossible to effectively predict whether the current document's structure is in a parsable, stable state before parsing. Summary of the Invention
[0004] This invention provides a dynamic metadata parsing system and method based on JSON. By dynamically monitoring the byte length changes of nodes at each level of the JSON document and performing quantitative analysis, it accurately and efficiently determines whether the current moment is suitable for data parsing. This avoids invalid parsing operations when the metadata is unstable or the structure fluctuates greatly, significantly reducing the waste of computing resources and improving the overall parsing efficiency.
[0005] To achieve the above objectives, the present invention provides a JSON-based dynamic metadata parsing system, comprising: The information acquisition module is used to obtain the byte length of all collection moments in the JSON document within a preset time period before the current moment; The hierarchical difference module is used to divide the preset duration into multiple time periods, and determine the hierarchical difference metric of the JSON document at the current time based on the dispersion of the length of all bytes in the JSON document within each time period. The cross-layer difference module is used to determine the cross-layer difference degree of the JSON document in each time period based on the difference in the length of all bytes between the end time period and the remaining time period of the JSON document, and to determine the metadata drift coefficient of the JSON document at the current time based on the cross-layer difference degree. The data parsing module is used to perform a weighted summation of the hierarchical difference metric and the metadata drift coefficient to obtain a comprehensive dynamic metadata parsing impact value, and to determine whether the JSON document can be parsed based on the comprehensive dynamic metadata parsing impact value.
[0006] Furthermore, it also includes: The information processing module is used to process the length of all bytes in the JSON document, wherein the information processing includes deleting duplicate byte lengths, deleting erroneous byte lengths, and deleting invalid byte lengths.
[0007] Furthermore, the hierarchical difference module is used for: The hierarchical difference module is used to calculate the byte length variance of all bytes in the JSON document within each time period; The hierarchical difference module is used to perform linear fitting on all byte length variances to obtain a byte length fitting line; The hierarchical difference module is used to determine the average slope of all slopes on the byte length fitting line, which is used as the hierarchical difference metric of the JSON document at the current moment.
[0008] Furthermore, the cross-layer difference module is used for: The cross-layer difference module is used to combine the lengths of all bytes between the last time period and the remaining time period of the JSON document based on the collection time sequence to obtain multiple byte length groups; The cross-layer difference module is used to determine the absolute value of the difference between the lengths of two bytes in each byte length group, as the byte length difference; The cross-layer difference module is used to take the average of all byte length differences as the cross-layer difference degree of the JSON document.
[0009] Furthermore, the cross-layer difference module is used for: The cross-layer difference module is used to extract the same cross-layer difference degree and determine the steady-state data drift coefficient of the JSON document; The cross-layer difference module is used to extract different cross-layer difference degrees and determine the wave state data drift coefficient of the JSON document; The cross-layer difference module is used to perform a weighted summation of the steady-state data drift coefficient and the wave-state data drift coefficient to obtain the metadata drift coefficient of the JSON document.
[0010] Furthermore, the cross-layer difference module is used for: The cross-layer difference module is used to extract the same cross-layer difference degree and determine the steady-state data drift coefficient of the JSON document; The cross-layer difference module is used to extract different cross-layer difference degrees and determine the wave state data drift coefficient of the JSON document; The cross-layer difference module is used to perform a weighted summation of the steady-state data drift coefficient and the wave-state data drift coefficient to obtain the metadata drift coefficient of the JSON document.
[0011] Furthermore, the cross-layer difference module is used for: The cross-layer difference module is used to calculate the wave state data drift coefficient of the JSON document according to the following formula: ; Where q is the wave-state data drift coefficient of the JSON document, n is the number of different cross-layer differences, and r i For the i-th distinct cross-layer difference, r i+1 Let be the degree of difference between the (i+1)th distinct layer.
[0012] Furthermore, the data parsing module is used for: The data parsing module is used to pre-set a preset comprehensive dynamic metadata parsing impact value, and determine whether the JSON document can be parsed based on the relationship between the comprehensive dynamic metadata parsing impact value and the preset comprehensive dynamic metadata parsing impact value; The data parsing module is used to determine that the JSON document can be parsed when the comprehensive dynamic metadata parsing impact value is less than the preset comprehensive dynamic metadata parsing impact value. The data parsing module is used to determine that the JSON document cannot be parsed when the comprehensive dynamic metadata parsing impact value is greater than or equal to the preset comprehensive dynamic metadata parsing impact value.
[0013] To achieve the above objectives, the present invention also provides a dynamic metadata parsing method based on JSON, comprising: Get the byte length of all collected moments in the JSON document within a preset time period before the current moment; The preset duration is divided into multiple time periods. Based on the dispersion of the length of all bytes in the JSON document within each time period, the hierarchical difference metric of the JSON document at the current moment is determined. Based on the difference in the length of all bytes between the end time period and the remaining time period of the JSON document, the cross-layer difference degree of the JSON document in each time period is determined, and the metadata drift coefficient of the JSON document at the current time is determined according to the cross-layer difference degree. The hierarchical difference metric and the metadata drift coefficient are weighted and summed to obtain a comprehensive dynamic metadata parsing impact value. Based on the comprehensive dynamic metadata parsing impact value, it is determined whether the JSON document can be parsed.
[0014] Furthermore, before determining the hierarchical difference metric of the JSON document at the current moment based on the dispersion of the length of all bytes in the JSON document within each time period, the following steps are also included: The length of all bytes in the JSON document is processed, including removing duplicate byte lengths, removing erroneous byte lengths, and removing invalid byte lengths.
[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention discloses a dynamic metadata parsing system and method based on JSON. The system includes: an information acquisition module that acquires the byte length of all collected moments of a JSON document within a preset time period before the current moment; a hierarchical difference module that divides the preset time period into multiple time periods and determines a hierarchical difference metric based on the dispersion of byte length; a cross-hierarchical difference module that determines a metadata drift coefficient based on the difference in byte length between the last time period and the remaining time periods; and a data parsing module that performs a weighted sum of the hierarchical difference metric and the metadata drift coefficient to obtain a comprehensive dynamic metadata parsing impact value, which determines whether the JSON document can be parsed. This system accurately and efficiently determines whether the current moment is suitable for data parsing, avoids invalid parsing operations when the metadata is unstable or has large structural fluctuations, significantly reduces the waste of computing resources, and improves the overall parsing efficiency. Attached Figure Description
[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A schematic diagram of the structure of a JSON-based dynamic metadata parsing system in an embodiment of the present invention is shown; Figure 2 The diagram illustrates a flowchart of a JSON-based dynamic metadata parsing method according to an embodiment of the present invention. Detailed Implementation
[0017] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.
[0018] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.
[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.
[0020] In the description of this application, it should be noted that, unless otherwise expressly 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 between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.
[0021] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.
[0022] like Figure 1 As shown, an embodiment of the present invention discloses a dynamic metadata parsing system based on JSON, including: an information acquisition module, a hierarchical difference module, a cross-layer difference module, and a data parsing module.
[0023] In some embodiments of this application, the information acquisition module is used to acquire the byte length of all collection times of the JSON document within a preset time period before the current time; In this embodiment, the preset duration is preferably the past 2 minutes.
[0024] In this embodiment, the data collection time is the past 1st second, the past 2nd second, the past 3rd second, ..., the past 118th second, the past 119th second, and the past 120th second.
[0025] In some embodiments of this application, the hierarchical difference module is used to divide a preset duration into multiple time periods, and determine the hierarchical difference metric value of the JSON document at the current moment based on the dispersion of the length of all bytes of the JSON document in each time period; In this embodiment, every 10 seconds is considered as a time period, such as the 1st second, the 2nd past second, the 3rd past second, ..., the 8th past second, the 9th past second, and the 10th past second as a time period.
[0026] In some embodiments of this application, it also includes: The information processing module is used to process the length of all bytes in the JSON document, wherein the information processing includes deleting duplicate byte lengths, deleting erroneous byte lengths, and deleting invalid byte lengths.
[0027] The beneficial effects of the above technical solution are: by deleting the length of duplicate bytes, the length of erroneous bytes, and the length of invalid bytes, the present invention can guarantee the basic accuracy of the data.
[0028] In some embodiments of this application, the hierarchical difference module is used for: The hierarchical difference module is used to calculate the byte length variance of all bytes in the JSON document within each time period; The hierarchical difference module is used to perform linear fitting on all byte length variances to obtain a byte length fitting line; The hierarchical difference module is used to determine the average slope of all slopes on the byte length fitting line, which is used as the hierarchical difference metric of the JSON document at the current moment.
[0029] In this embodiment, each time period includes a length of 10 bytes, and the corresponding variance can be calculated as the byte length variance.
[0030] In this embodiment, the fitting process of the byte length fitting line will not be described in detail here.
[0031] The beneficial effects of the above technical solution are: the present invention determines the mean slope of all slopes on the byte length fitting line as the level difference measure of the JSON document at the current moment, realizes the dispersion analysis of all byte lengths, and lays the foundation for data parsing and judgment.
[0032] In some embodiments of this application, the cross-layer difference module is used to determine the cross-layer difference degree of the JSON document in each time period based on the difference in the length of all bytes between the end time period and the remaining time period of the JSON document, and to determine the metadata drift coefficient of the JSON document at the current time based on the cross-layer difference degree. In some embodiments of this application, the cross-layer difference module is used for: The cross-layer difference module is used to combine the lengths of all bytes between the last time period and the remaining time period of the JSON document based on the collection time sequence to obtain multiple byte length groups; The cross-layer difference module is used to determine the absolute value of the difference between the lengths of two bytes in each byte length group, as the byte length difference; The cross-layer difference module is used to take the average of all byte length differences as the cross-layer difference degree of the JSON document.
[0033] In this embodiment, based on the order of collection time, all byte lengths between the last time period and the remaining time periods are combined in a one-to-one correspondence. That is, the last time period can be bound to the remaining 9 time periods. The last time period and each time period can obtain 10 byte length groups. The byte length difference corresponding to each byte length group is calculated, and finally the average value is taken.
[0034] The beneficial effects of the above technical solution are: the present invention uses the average of all byte length differences as the cross-layer difference degree of the JSON document, realizes the analysis of byte length differences and fluctuation analysis, and thus ensures the accuracy of data analysis and judgment.
[0035] In some embodiments of this application, the cross-layer difference module is used for: The cross-layer difference module is used to extract the same cross-layer difference degree and determine the steady-state data drift coefficient of the JSON document; The cross-layer difference module is used to extract different cross-layer difference degrees and determine the wave state data drift coefficient of the JSON document; The cross-layer difference module is used to perform a weighted summation of the steady-state data drift coefficient and the wave-state data drift coefficient to obtain the metadata drift coefficient of the JSON document.
[0036] In this embodiment, the steady-state data drift coefficient and the wave-state data drift coefficient can be weighted based on subjective weighting or objective weighting. Here, the weight of the steady-state data drift coefficient is preferably 0.7, and the weight of the wave-state data drift coefficient is preferably 0.3. The weights can be adjusted adaptively according to actual needs.
[0037] The beneficial effects of the above technical solution are: the present invention performs a weighted summation of the steady-state data drift coefficient and the wave-state data drift coefficient to obtain the metadata drift coefficient of the JSON document, thereby realizing the quantitative analysis of byte length and providing feedback on the fluctuation of byte length over time.
[0038] In some embodiments of this application, the cross-layer difference module is used for: The cross-layer difference module is used to extract the same cross-layer difference degree from all cross-layer difference degrees and obtain multiple cross-layer difference degree sets; The cross-layer difference module is used to count the number of the first cross-layer difference set in the cross-layer difference set; The cross-layer difference module is used to extract a cross-layer difference degree from each of the sets of cross-layer difference degrees, and to calculate the first cross-layer difference degree and value. The cross-layer difference module is used to calculate the average cross-layer difference of all cross-layer difference degrees, remove all cross-layer difference degree sets that are less than the average cross-layer difference degree, and count the number of second cross-layer difference degree sets of the remaining cross-layer difference degree sets. The cross-layer difference module is used to extract one cross-layer difference degree from the remaining cross-layer difference degree set, and calculate the second cross-layer difference degree and value; The cross-layer difference module is used to calculate the steady-state data drift coefficient of the JSON document based on the number of the first cross-layer difference set, the number of the second cross-layer difference set, the sum of the first cross-layer difference value, and the sum of the second cross-layer difference value.
[0039] In this embodiment, the cross-layer difference degree is the same in each cross-layer difference degree set, but the cross-layer difference degree is different between the cross-layer difference degree sets.
[0040] In this embodiment, the steady-state data drift coefficient of the JSON document is calculated according to the following formula: ; Where g is the steady-state data drift coefficient of the JSON document, s1 is the number of the first cross-layer difference set, s2 is the number of the second cross-layer difference set, c1 is the sum of the first cross-layer difference and c2 is the sum of the second cross-layer difference.
[0041] The beneficial effects of the above technical solution are: the present invention calculates the steady-state data drift coefficient of the JSON document based on the number of the first cross-layer difference set, the number of the second cross-layer difference set, the sum of the first cross-layer difference and the sum of the second cross-layer difference, and characterizes the stable change of byte length through the steady-state data drift coefficient.
[0042] In some embodiments of this application, the cross-layer difference module is used for: The cross-layer difference module is used to calculate the wave state data drift coefficient of the JSON document according to the following formula: ; Where q is the wave-state data drift coefficient of the JSON document, n is the number of different cross-layer differences, and r i For the i-th distinct cross-layer difference, r i+1 Let be the degree of difference between the (i+1)th distinct layer.
[0043] The beneficial effects of the above technical solution are: extracting different cross-layer differences, determining the wave-like data drift coefficient of the JSON document, and characterizing the fluctuation of byte length through the wave-like data drift coefficient.
[0044] In some embodiments of this application, the data parsing module is used to perform a weighted summation of the hierarchical difference metric and the metadata drift coefficient to obtain a comprehensive dynamic metadata parsing impact value, and to determine whether the JSON document can be parsed based on the comprehensive dynamic metadata parsing impact value.
[0045] In this embodiment, the hierarchical difference metric and the metadata drift coefficient are weighted based on subjective or objective weighting methods. The preferred weight for the hierarchical difference metric is 0.35, and the preferred weight for the metadata drift coefficient is 0.65. These can be adjusted adaptively according to actual needs.
[0046] In some embodiments of this application, the data parsing module is used for: The data parsing module is used to pre-set a preset comprehensive dynamic metadata parsing impact value, and determine whether the JSON document can be parsed based on the relationship between the comprehensive dynamic metadata parsing impact value and the preset comprehensive dynamic metadata parsing impact value; The data parsing module is used to determine that the JSON document can be parsed when the comprehensive dynamic metadata parsing impact value is less than the preset comprehensive dynamic metadata parsing impact value. The data parsing module is used to determine that the JSON document cannot be parsed when the comprehensive dynamic metadata parsing impact value is greater than or equal to the preset comprehensive dynamic metadata parsing impact value.
[0047] In this embodiment, the preset comprehensive dynamic metadata parsing impact value is preferably 6, but it can be adjusted adaptively according to actual needs.
[0048] The beneficial effects of the above technical solution are: the present invention determines whether data parsing of JSON documents can be performed based on the relationship between the comprehensive dynamic metadata parsing impact value and the preset comprehensive dynamic metadata parsing impact value. It accurately and efficiently determines whether data parsing is suitable at the current moment, avoids invalid parsing operations when metadata is unstable or the structure fluctuates greatly, significantly reduces the waste of computing resources, and improves the overall parsing efficiency.
[0049] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.
[0050] Correspondingly, such as Figure 2 As shown, this application also provides a JSON-based dynamic metadata parsing method, including: S110: Get the byte length of all collected moments in the JSON document within the preset time period before the current moment; S120: Divide the preset duration into multiple time periods, and determine the hierarchical difference metric of the JSON document at the current moment based on the dispersion of the length of all bytes in the JSON document within each time period; S130: Based on the difference in the length of all bytes of the JSON document between the two time periods, determine the cross-layer difference degree of the JSON document in each time period, and determine the metadata drift coefficient of the JSON document at the current time according to the cross-layer difference degree; S140: The hierarchical difference metric and the metadata drift coefficient are weighted and summed to obtain the comprehensive dynamic metadata parsing impact value, and it is determined whether the JSON document can be parsed based on the comprehensive dynamic metadata parsing impact value.
[0051] In some embodiments of this application, before determining the hierarchical difference metric of the JSON document at the current time based on the dispersion of the length of all bytes in the JSON document within each time period, the method further includes: The length of all bytes in the JSON document is processed, including removing duplicate byte lengths, removing erroneous byte lengths, and removing invalid byte lengths.
[0052] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.
[0053] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.
[0054] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A dynamic metadata parsing system based on JSON, characterized in that, include: The information acquisition module is used to obtain the byte length of all collection moments in the JSON document within a preset time period before the current moment; The hierarchical difference module is used to divide the preset duration into multiple time periods, and determine the hierarchical difference metric of the JSON document at the current time based on the dispersion of the length of all bytes in the JSON document within each time period. The cross-layer difference module is used to determine the cross-layer difference degree of the JSON document in each time period based on the difference in the length of all bytes between the end time period and the remaining time period of the JSON document, and to determine the metadata drift coefficient of the JSON document at the current time based on the cross-layer difference degree. The data parsing module is used to perform a weighted summation of the hierarchical difference metric and the metadata drift coefficient to obtain a comprehensive dynamic metadata parsing impact value, and to determine whether the JSON document can be parsed based on the comprehensive dynamic metadata parsing impact value.
2. The JSON-based dynamic metadata parsing system according to claim 1, characterized in that, Also includes: The information processing module is used to process the length of all bytes in the JSON document, wherein the information processing includes deleting duplicate byte lengths, deleting erroneous byte lengths, and deleting invalid byte lengths.
3. The JSON-based dynamic metadata parsing system according to claim 1, characterized in that, The hierarchical difference module is used for: The hierarchical difference module is used to calculate the byte length variance of all bytes in the JSON document within each time period; The hierarchical difference module is used to perform linear fitting on all byte length variances to obtain a byte length fitting line; The hierarchical difference module is used to determine the average slope of all slopes on the byte length fitting line, which is used as the hierarchical difference metric of the JSON document at the current moment.
4. The JSON-based dynamic metadata parsing system according to claim 1, characterized in that, The cross-layer difference module is used for: The cross-layer difference module is used to combine the lengths of all bytes between the last time period and the remaining time period of the JSON document based on the collection time sequence to obtain multiple byte length groups; The cross-layer difference module is used to determine the absolute value of the difference between the lengths of two bytes in each byte length group, as the byte length difference; The cross-layer difference module is used to take the average of all byte length differences as the cross-layer difference degree of the JSON document.
5. The JSON-based dynamic metadata parsing system according to claim 1, characterized in that, The cross-layer difference module is used for: The cross-layer difference module is used to extract the same cross-layer difference degree and determine the steady-state data drift coefficient of the JSON document; The cross-layer difference module is used to extract different cross-layer difference degrees and determine the wave state data drift coefficient of the JSON document; The cross-layer difference module is used to perform a weighted summation of the steady-state data drift coefficient and the wave-state data drift coefficient to obtain the metadata drift coefficient of the JSON document.
6. The JSON-based dynamic metadata parsing system according to claim 5, characterized in that, The cross-layer difference module is used for: The cross-layer difference module is used to extract the same cross-layer difference degree from all cross-layer difference degrees and obtain multiple cross-layer difference degree sets; The cross-layer difference module is used to count the number of the first cross-layer difference set in the cross-layer difference set; The cross-layer difference module is used to extract a cross-layer difference degree from each of the sets of cross-layer difference degrees, and to calculate the first cross-layer difference degree and value. The cross-layer difference module is used to calculate the average cross-layer difference of all cross-layer difference degrees, remove all cross-layer difference degree sets that are less than the average cross-layer difference degree, and count the number of second cross-layer difference degree sets of the remaining cross-layer difference degree sets. The cross-layer difference module is used to extract one cross-layer difference degree from the remaining cross-layer difference degree set, and calculate the second cross-layer difference degree and value; The cross-layer difference module is used to calculate the steady-state data drift coefficient of the JSON document based on the number of the first cross-layer difference set, the number of the second cross-layer difference set, the sum of the first cross-layer difference and the sum of the second cross-layer difference.
7. The JSON-based dynamic metadata parsing system according to claim 5, characterized in that, The cross-layer difference module is used for: The cross-layer difference module is used to calculate the wave state data drift coefficient of the JSON document according to the following formula: ； Where q is the wave-state data drift coefficient of the JSON document, n is the number of different cross-layer differences, and r i For the i-th distinct cross-layer difference, r i+1 Let be the degree of difference between the (i+1)th distinct layer.
8. The JSON-based dynamic metadata parsing system according to claim 1, characterized in that, The data parsing module is used for: The data parsing module is used to pre-set a preset comprehensive dynamic metadata parsing impact value, and determine whether the JSON document can be parsed based on the relationship between the comprehensive dynamic metadata parsing impact value and the preset comprehensive dynamic metadata parsing impact value; The data parsing module is used to determine that the JSON document can be parsed when the comprehensive dynamic metadata parsing impact value is less than the preset comprehensive dynamic metadata parsing impact value. The data parsing module is used to determine that the JSON document cannot be parsed when the comprehensive dynamic metadata parsing impact value is greater than or equal to the preset comprehensive dynamic metadata parsing impact value.
9. A JSON-based dynamic metadata parsing method, applied to the JSON-based dynamic metadata parsing system as described in any one of claims 1-8, characterized in that, include: Get the byte length of all collected moments in the JSON document within a preset time period before the current moment; The preset duration is divided into multiple time periods. Based on the dispersion of the length of all bytes in the JSON document within each time period, the hierarchical difference metric of the JSON document at the current moment is determined. Based on the difference in the length of all bytes between the end time period and the remaining time period of the JSON document, the cross-layer difference degree of the JSON document in each time period is determined, and the metadata drift coefficient of the JSON document at the current time is determined according to the cross-layer difference degree. The hierarchical difference metric and the metadata drift coefficient are weighted and summed to obtain a comprehensive dynamic metadata parsing impact value. Based on the comprehensive dynamic metadata parsing impact value, it is determined whether the JSON document can be parsed.
10. The JSON-based dynamic metadata parsing method according to claim 9, characterized in that, Before determining the hierarchical difference metric of the JSON document at the current time based on the dispersion of all byte lengths of the JSON document within each time period, the following steps are also included: The length of all bytes in the JSON document is processed, including removing duplicate byte lengths, removing erroneous byte lengths, and removing invalid byte lengths.