Data exchange method and system based on markup language

Abstract

The invention relates to the technical field of data exchange, in particular to a markup language-based data exchange method and system, and the method comprises the following steps: determining a processing priority, monitoring a load and adjusting a processing sequence, optimizing IO response time, recognizing a redundant field and evaluating a key risk, and executing key updating based on the number and complexity of data packet labels. And the tag analysis accuracy and efficiency are optimized. According to the invention, through accurate analysis of the markup language in the data packet, scheduling, transmission and analysis in the data exchange process can be optimized, and evaluation of the number of labels, the complexity of the nested structure and the field length can be realized, so that each data packet can dynamically allocate priority according to the characteristics of the data packet, thereby effectively improving the data transmission efficiency, reducing the delay and improving the data exchange efficiency. By monitoring the load and the transmission opportunity of the data packets in real time, the processing sequence of the data packets can be dynamically adjusted, and resource waste caused by overload of the data exchange system is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data exchange, and in particular, to a data exchange method and system based on a markup language. Background Art

[0002] Data exchange technology mainly involves data transmission and sharing between different systems, platforms or applications. Its core goal is to ensure that data can be exchanged between different computers or devices in a standardized and efficient manner, so that systems can understand and process this data mutually, and it is widely used in many fields such as information exchange, data synchronization, and remote communication. Data exchange technology usually includes standardization of data formats, design and implementation of transmission protocols, and compression and encryption of data, etc.

[0003] Among them, the data exchange method based on a markup language refers to a technology that uses a markup language (such as XML, JSON, etc.) as the data format to transmit data through a network or computer system. The core is to use a standardized markup language to represent the data structure, so that different systems can easily parse and process the data. Its main purpose is to simplify the data transmission process between different platforms, improve the interoperability and compatibility of data exchange, and is widely used in scenarios such as Web services, e-commerce, and information sharing in many information exchange and integration applications to ensure that data can be transmitted accurately and quickly.

[0004] In the prior art, during the data exchange process, relying on fixed rules and standards results in a lack of flexibility in the processing process. The scheduling of data packets and the allocation of transmission priorities cannot be dynamically adjusted according to the actual load or the complexity of data packets. Therefore, in a high-concurrency environment, the resource utilization rate cannot be maximized. The prior art cannot effectively distinguish the complexity of data packets, so it is impossible to optimize the processing order, resulting in the risk that some important data packets face transmission delays or losses. In addition, there are still defects in the processing of data redundancy and invalid metadata. Redundant information cannot be identified and cleaned in time, increasing the transmission burden during the data exchange process. The existing encryption key management method is static and cannot be dynamically updated based on redundant data or real-time risk assessment, thus easily leading to potential threats of key leakage or cracking. Summary of the Invention

[0005] The purpose of the present invention is to solve the disadvantages existing in the prior art, and to propose a data exchange method and system based on a markup language.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A data exchange method based on a markup language, including the following steps, S1: Based on the received markup language data packets, determine the number of tags in each packet. Through hierarchical analysis, evaluate the complexity of the nested structure in the markup language, measure the field length of each tag, match it with the classification criteria, determine the processing priority of the data packets, and obtain the structural priority score. S2: Based on the structural priority score, monitor the current load of the data exchange system, determine the transmission timing of each data packet, adjust the processing sequence of the data packets through queue management, refine the distribution order within the queue, and generate the asynchronous queuing delay difference. S3: Based on the asynchronous queuing delay difference, analyze the markup language content in the data packets, identify duplicate tag fields and invalid metadata, calculate the proportion of redundant tags or fields, conduct a risk assessment on the key sensitivity of the redundant content, and determine whether to execute the key update mechanism to obtain the redundant key update parameters. S4: Based on the redundant key update parameters, evaluate the security of the multi-segment encryption key currently in use. Through the key segment mapping table, determine the key interval that needs to be updated, overwrite the original encrypted old value with the new key hash value, and generate the key replacement update amount.

[0007] The improvements of the present invention are that the structural priority score includes the number of tags, the complexity of the nested level, and the field length; the asynchronous queuing delay difference includes the queue processing sequence, the transmission timing of the data packets, and the IO response delay; the redundant key update parameters include the proportion of redundant fields, the proportion of redundant tags, and the key sensitivity evaluation result; the key replacement update amount includes the key update interval, the key hash change information, and the encryption segment replacement result.

[0008] The improvements of the present invention are that the specific steps for obtaining the structural priority score are as follows: S111: Based on the received markup language data packets, detect the number of tags in each packet, identify the field length of the tags, and obtain the field length data. S112: Process the tags through hierarchical analysis, evaluate the complexity of the nested structure, and combine the field length data to use the formula: ; Calculate the nested structure complexity value , where represents the field length of the th tag, is the average value of the field lengths of all tags, is the nested depth of the th tag, is the total number of tags in the data packet; S113: Based on the nested structure complexity value, match it with the classification criteria according to the nested structure complexity, and conduct a priority evaluation on each data packet to obtain the structural priority score.

[0009] The improvement of the present invention is that the step of obtaining the asynchronous queuing delay difference is specifically as follows: S211: Based on the structure priority score, monitor the current load of the data exchange system, and use the formula: ; Determine the transmission timing of the data packet , and adjust the processing sequence of the data packet through queue management, where represents the data packet priority score, represents the load score of the current system, represents the processing time of the data packet, represents the delay adjustment coefficient, represents the data packet queuing duration, represents the expected processing time, represents the system priority benchmark; S212: Based on the processing sequence of the data packet, refine the distribution order of the data packets in the queue, adjust the processing time and response order of the data packets, and reorder them to optimize the IO response time and generate the asynchronous queuing delay difference.

[0010] The improvement of the present invention is that the step of obtaining the redundant key update parameter is specifically as follows: S311: Based on the asynchronous queuing delay difference, perform structured processing on the markup language content in the data packet, split the tag fields by level, detect the corresponding relationship between the field naming, nested structure and data content at the level, identify the number of times the field name appears repeatedly and the consistency interval of the field value, and obtain the tag field repeatability; S312: According to the tag field repeatability, combined with the field naming frequency, nested structure depth and field value update interval, filter out invalid metadata, and extract the nested positions of the repeated tag fields, and use the formula: ; Obtain the redundant tag field density value , and obtain the tag redundancy interval distribution quantity, where represents the th weight parameter of the field, represents the number of repetitions of the th field, represents the number of valid values of the th field, represents the nested level of the th field, represents the th data update density of the field, represents the set of update densities of all fields, Represents the total number of fields; S313: Based on the distribution quantity of the label redundancy interval, determine the corresponding position of the field distribution density and the data packet key information, identify the field nested structure path containing the key pattern characters, mark the field group that matches the sensitive key pattern rule, compare the key positions for verification, and obtain the redundant key update parameter.

[0011] The improvement of the present invention is that the obtaining step of the key replacement update quantity is specifically as follows: S411: Based on the redundant key update parameter, perform a multi-segment security evaluation on the currently used encryption key, filter the key interval to be updated through the key segment mapping table, and obtain the preliminary key interval update data; S412: Based on the preliminary key interval update data, perform a key replacement process, replace the hash value of the original encryption key, and generate a new key hash value, using the formula: ; Obtain the key replacement update quantity , where and respectively represent the and the th hash values of the key intervals, and respectively represent the and the th encryption key values of the key intervals, represents the total number of key intervals.

[0012] The improvement of the present invention is that the step further includes: S5: According to the key replacement update quantity, evaluate the markup language parsing requirements in the data packet, analyze the nested path of each label, and combine the depth of the label path and the complexity of the marked content to dynamically adjust the parameters in the data packet parsing process, optimize the accuracy and efficiency of label parsing, and obtain the label parsing optimization result; The label parsing optimization result includes the label nesting depth, the label path structure, and the parsing accuracy.

[0013] The improvement of the present invention is that the obtaining step of the label parsing optimization result is specifically as follows: S511: According to the key replacement update quantity, identify the path and its depth of each label in the data packet, extract the structural information of the label content, and evaluate the nested level of the label path and the complexity of the marked content to obtain the label evaluation data; S512: Based on the label evaluation data, optimize the label path, and dynamically adjust the marked path by real-time monitoring the data packet parsing process to obtain the label path adjustment configuration; S513: Adjust the configuration based on the label path. According to the requirements of label parsing accuracy and efficiency, optimize the parsing path and adjust parameters to obtain the optimized result of label parsing.

[0014] A data exchange system based on a markup language, the system comprising: The priority evaluation module measures the number of labels in each received markup language data packet, evaluates the complexity of the nested structure in the markup language through hierarchical analysis, measures the field length of each label, matches with the classification standard, determines the processing priority of the data packet, and obtains the structural priority score; The scheduling optimization module monitors the current load of the data exchange system based on the structural priority score, determines the transmission timing of each data packet, adjusts the processing sequence of the data packets through queue management, refines the distribution order within the queue, and generates an asynchronous queuing delay difference; The redundancy and key update module analyzes the markup language content in the data packet based on the asynchronous queuing delay difference, identifies duplicate label fields and invalid metadata, calculates the proportion of redundant labels or fields, conducts a risk assessment on the key sensitivity of the redundant content, determines whether to execute the key update mechanism, and obtains the redundant key update parameter; The key replacement module evaluates the security of the multi-segmentation of the currently used encryption key according to the redundant key update parameter, determines the key interval to be updated through the key segment mapping table, executes the key replacement process, overwrites the original encrypted old value with the new key hash value, and generates the key replacement update amount; The label parsing optimization module evaluates the markup language parsing requirements in the data packet according to the key replacement update amount, analyzes the nested path of each label, combines the depth of the label path and the complexity of the markup content, dynamically adjusts the parameters in the data packet parsing process, and obtains the optimized result of label parsing.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by precisely analyzing the markup language in data packets, the scheduling, transmission, and parsing in the data exchange process can be optimized. The evaluation of the number of tags, the complexity of the nested structure, and the field length enables each data packet to be dynamically assigned a priority according to its characteristics, thereby effectively improving the data transmission efficiency and reducing latency. By real-time monitoring the load and the transmission timing of data packets, the processing order of data packets can be dynamically adjusted, reducing the waste of resources caused by the overload of the data exchange system. The detection of redundant data and the dynamic update mechanism of encryption keys effectively reduce the impact of redundant information on the transmission efficiency and enhance data security. By optimizing the tag parsing path and dynamically adjusting the parsing parameters, complex data structures can be processed more precisely, improving the accuracy and efficiency of data parsing. Especially in the face of heterogeneous systems and high-concurrency scenarios, the efficiency and security of data exchange can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flowchart of a data exchange method based on markup language proposed by the present invention; Figure 2 is a flowchart for obtaining the structural priority score in the present invention; Figure 3 is a flowchart for obtaining the asynchronous queuing delay difference in the present invention; Figure 4 is a flowchart for obtaining the redundant key update parameters in the present invention; Figure 5 is a flowchart for obtaining the key replacement update amount in the present invention; Figure 6 is a flowchart for obtaining the tag parsing optimization result in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specifically defined. Embodiment

[0019] Please refer toFigure 1 , the present invention provides a technical solution: a data exchange method based on a markup language, comprising the following steps: S1: Based on the received markup language data packet, determine the number of tags in each data packet. Through hierarchical analysis, evaluate the complexity of the nested structure in the markup language, measure the field length of each tag, match it with the classification standard, and determine the processing priority of the data packet according to the matching result to obtain the structural priority score; S2: Based on the structural priority score, monitor the current load of the data exchange system, determine the transmission timing of each data packet, adjust the processing sequence of the data packets through queue management, refine the distribution order within the queue, optimize the IO response time, and generate the asynchronous queuing delay difference; S3: Based on the asynchronous queuing delay difference, analyze the markup language content in the data packet, identify duplicate tag fields and invalid metadata, calculate the proportion of redundant tags or fields, conduct a risk assessment on the key sensitivity of the redundant content, and determine whether to execute the key update mechanism to obtain the redundant key update parameter; S4: Based on the redundant key update parameter, evaluate the security of the multi-segmentation of the currently used encryption key. Through the key segment mapping table, determine the key interval that needs to be updated, execute the key replacement process, overwrite the original encrypted old value with the new key hash value, and generate the key replacement update amount; S5: According to the key replacement update amount, evaluate the markup language parsing requirements in the data packet, analyze the nested path of each tag, and combine the depth of the tag path and the complexity of the markup content to dynamically adjust the parameters during the data packet parsing process, optimize the accuracy and efficiency of tag parsing, and obtain the tag parsing optimization result.

[0020] A tag refers to the basic element or structural unit in a markup language, usually used to identify and describe the attributes or structure of data content. For example, in markup languages such as XML, HTML, JSON, etc., tags are used to mark the start and end of data and classify or describe the data.

[0021] A tag path refers to the path used to locate a specific data element or tag, which describes the hierarchical relationship and nested order of a tag in the data structure. A tag path represents starting from the root element, passing through each level of child elements in turn, and finally locating to a specific data tag. Each level of tag in the path will be nested within the previous level of tag, forming a hierarchical structure.

[0022] The structural priority score includes the number of tags, the complexity of the nested hierarchy, and the field length. The asynchronous queuing delay difference includes the queue processing order, the data packet transmission timing, and the IO response delay. The redundant key update parameter includes the redundant field ratio, the redundant tag ratio, and the key sensitivity evaluation result. The key replacement update amount includes the key update interval, the key hash change information, and the encryption segment replacement result. The tag parsing optimization result includes the tag nesting depth, the tag path structure, and the parsing accuracy.

[0023] Please refer to Figure 2 , and the specific steps for obtaining the structural priority score are as follows: S111: Based on the received markup language data packets, detect the number of tags in each data packet, identify the field length of the tags, and obtain the field length data; Receive the markup language data packets and process each of them to identify the number of tags in the data packets and confirm the field length of each tag. In this way, the tag information of each data packet can be carefully extracted for subsequent analysis. In practical applications, assume that a markup language data packet is received, which contains 5 tags with field lengths of 50, 100, 150, 120, and 80 respectively. By processing each tag, count the number and obtain the field length information of each tag. Next, the data will be further used to analyze the structural complexity of the data packets. For example, assume there is a data packet with the following field length data: [50, 100, 150, 120, 80]. Based on the field data, the average length can be calculated, the longest and shortest tag lengths can be identified, and subsequent structural complexity evaluations can be performed. The field length data will be the key input in the subsequent steps to ensure accurate calculation and effective output results during analysis and evaluation. Finally, the number of tags (5 tags) and the field length data will be obtained.

[0024] S112: Process the tags through hierarchical analysis, evaluate the complexity of the nested structure, and combine the field length data using the formula: ; Calculate the nested structure complexity value , where represents the field length of the th tag, that is, the length of the data field contained in each tag in the data packet, is the average value of the field lengths of all tags, that is, the sum of the field lengths of all tags divided by the number of tags, and is used as the benchmark for the field length, is the nested depth of the th tag, that is, the nested level of the tag in the markup language structure, is the total number of tags in the data packet; If there are 5 tags, with the corresponding field lengths being: [50, 100, 150, 120, and 80], and the nesting depths being: [2, 1, 3, 2, and 1], next, calculate the average length of all tag fields ( ) ; For the calculation of the nesting complexity of each tag, if the following nesting depth data has been obtained: [2, 1, 3, 2, and 1], according to the formula, it is necessary to process the difference between the field length of each tag and the average length, and then combine the nesting depth to calculate the complexity value. The field length of the first tag is 50, the average length is 100, and the nesting depth is 2. The complexity contributed by it is calculated as follows: Field length of Tag 1 , nesting depth ; ; Field length of Tag 2 , nesting depth ; ; Field length of Tag 3 , nesting depth ; ; Field length of Tag 4 , nesting depth ; ; Field length of Tag 5 , nesting depth ; ; Add up the complexity values of all tags: ; Obtain the nesting complexity value of 2.3834, which will reflect the nesting complexity of the data packet and provide a basis for subsequent priority evaluation.

[0025] S113: Based on the nesting structure complexity value, match according to the classification standard and the nesting structure complexity, and conduct priority evaluation on each data packet to obtain the structure priority score; Based on the aforementioned nested structure complexity values, the priority of each data packet is evaluated according to the classification criteria. By comparing the nested complexity values with the preset classification criteria, a structural priority score is obtained. For example, assume the set criteria are: if the nested complexity is greater than 1.5, the priority is high; if it is between 1 and 1.5, the priority is medium; if it is less than 1, the priority is low. According to the aforementioned nested complexity values, the priority score of each data packet can be obtained, and the data packets are sorted. The data packets with higher complexity are processed first. For example, if the nested complexity value of a certain data packet is 2.0, according to the classification criteria, its priority is high. Through this step, the structural priority score of each data packet will be obtained, and the data packets will be sorted according to the score to determine their processing order.

[0026] Please refer to Figure 3 , and the steps for obtaining the asynchronous queuing delay difference are specifically as follows: S211: Based on the structural priority score, monitor the current load of the data exchange system, and use the formula: ; Determine the transmission timing of the data packet , and adjust the processing sequence of the data packet through queue management, where represents the data packet priority score, represents the load score of the current system, represents the processing time of the data packet, which refers to the time interval from the start of processing to completion, represents the delay adjustment coefficient, which is used to adjust the change in processing time caused by network delay or system response, represents the data packet queuing duration, which refers to the time the data packet waits in the queue, represents the expected processing time, which refers to the time the system should process the data packet under ideal circumstances, represents the system priority benchmark; Obtain the current load score of the system ( ) and the priority score of the data packet ( ). For example, assume the priority score of data packet A ( ) is 80, and the current system load score ( ) obtained through monitoring is 60. Then, obtain the processing time of the data packet ( ), assume it is 10 milliseconds, which is the processing time of data packet A in the system. The network delay adjustment coefficient ( ) is set to 1.1 to reflect the impact of actual network delay. This coefficient is obtained based on the delay situation of the past few transmissions. The queuing duration of data packet A ( ) is 30 milliseconds, and the expected processing time ( is 50 milliseconds, which are derived from the performance goals set by the system. Finally, the system's priority benchmark ( is 20. Based on the above parameters, calculate the transmission timing of the data packet ( ): ; Therefore, the transmission timing of data packet A is 77.7 milliseconds, which reflects the timing of data packet A being processed in the queue. The calculated result can help the system reasonably arrange the priorities and transmission orders of each data packet, thereby optimizing the overall data transmission efficiency.

[0027] S212: Based on the processing sequence of the data packets, refine the distribution order of the data packets in the queue, adjust the processing time and response order of the data packets, and reorder them to optimize the IO response time and generate an asynchronous queuing delay difference; According to the calculated processing timing of the data packet ( ), combined with the queuing duration of the data packet ( ) and the expected processing time ( ), sort the data packets. Based on this, adjust the processing time and response order of the data packets. Assume that at a certain moment, the queuing duration of data packet A is 50 milliseconds and the expected processing time is 60 milliseconds, while the queuing duration of data packet B is 30 milliseconds and the expected processing time is 40 milliseconds. Then the system will reorder according to the parameters to ensure that those tasks with longer waiting times and expected processing times close to each other are processed first. After reordering, the system can effectively optimize the IO response time and avoid unnecessary delays during the transmission of the data packets in the queue, generating an asynchronous queuing delay difference.

[0028] Please refer to Figure 4 . The specific steps for obtaining the redundant key update parameters are as follows: S311: Based on the asynchronous queuing delay difference, structurally process the markup language content in the data packet, split the tag fields by hierarchy, detect the corresponding relationships between the field names, nested structures, and data contents at each hierarchy, identify the number of times the field names appear repeatedly and the consistency intervals of the field values, and obtain the tag field repetition degree; By parsing the packet using a markup language, the tag fields within it are split out. Tag fields are usually contained within multiple hierarchical structures of the packet. For example, in network transmission, each packet contains multiple tags such as "IP address", "data type", etc. Moreover, tags have a multi-level nested structure, where each tag contains different sub-tags or nested items. The core of the task is to identify the tag fields and their nested relationships. To this end, by detecting the naming rules of each tag field, it is verified whether they conform to the expected standards (such as whether they are pre-defined tag names). In terms of the data content, the specific values of each tag field are further analyzed to determine whether they are consistent at different transmission times, whether they conform to the defined rules, and to ensure the stability and consistency of the field values. For example, the value of the "IP address" tag field should vary within a certain range, while the value of the "data type" tag should be one of the pre-defined types. Through the rules, the number of times the field name appears repeatedly can be identified, the consistency interval of the field values can be checked, and finally, the repetition degree of the tag field can be obtained based on the analysis results. For example, assume that in a certain packet, the field "IP address" appears three times, its value remains consistent, and the field name repeats in multiple levels. At this time, it can be determined that the repetition degree of this tag field is high. This operation can further help identify potential redundant fields in the packet, thus contributing to subsequent tag redundancy identification and risk assessment.

[0029] S312: According to the repetition degree of the tag fields, combined with the naming frequency of the fields, the depth of the nested structure, and the update interval of the field values, filter out invalid metadata, and extract the nested positions of the repeated tag fields. Use the formula: ; Obtain the density value of the redundant tag fields , and get the distribution quantity of the tag redundancy interval. Among them, represents the weight parameter of the th field, represents the number of repetitions of the th field, represents the number of valid values of the th field, represents the nested level of the th field, represents the data update density of the th field, represents the set of update densities of all fields, represents the total number of fields; Based on the repetition degree of tag fields, conduct an in-depth analysis of the tag fields, and screen out invalid metadata by combining the field naming frequency, the depth of nested structure, and the field value update interval. First, evaluate the field naming frequency and check whether each field is repeated multiple times, especially those tag fields that are repeated many times. For example, if the "user information" tag appears repeatedly in multiple locations, it can be considered that the repetition degree of this field is relatively high. Then, analyze the depth of the nested structure. For each tag field, check whether it is nested in multiple hierarchical structures. For example, the "address" tag contains sub-tags such as "city", "province", and "postal code". The deeper the nested level of the field, the more complex its structure usually is, and there is more redundant information. Finally, consider the field value update interval and check the change frequency of the field value. If the values of some fields do not change for a long time, it indicates that they are invalid metadata. For example, assume that the "IP address" field and the "device ID" field in a data packet appear frequently during transmission and their values are updated relatively frequently, while the value of the "user name" field remains unchanged for a long time. Then, it can be considered that the validity of the "user name" field is relatively low, while the validity of the "IP address" and "device ID" fields is relatively high. Based on this, invalid metadata can be further screened out, and the nested positions of the repeated tag fields can be extracted. In this process, calculate the density value of the redundant tag fields through a formula, and there are the following data: For field 1 ("IP address"), the weight is 2, the number of repetitions is 5, the number of valid values is 5, the nested level is 1, and the update density is 4; For field 2 ("device ID"), the weight is 1.5, the number of repetitions is 4, the number of valid values is 4, the nested level is 2, and the update density is 3; For field 3 ("user name"), the weight is 1, the number of repetitions is 1, the number of valid values is 0, the nested level is 1, and the update density is 1.

[0030] According to the data, calculate the density value of the redundant tag fields: ; For field 1: ; For field 2: ; For field 3: ; Calculate the density value of the redundant tag fields: ; Therefore, the density value of the redundant tag fields is approximately 0.118. This result indicates that in the case of fields, the density of the redundant tag fields is relatively low, indicating that there are fewer redundant tag fields in the data packet.

[0031] S313: Based on the distribution quantity of label redundancy intervals, determine the corresponding positions of the field distribution density and the data packet key information, identify the nested structure path of the fields containing key pattern characters, mark the field groups that match the sensitive key pattern rules, compare the key positions for verification, and obtain the redundant key update parameters; By obtaining the distribution quantity of the redundant label redundancy intervals, determine the corresponding positions of the field distribution density and the data packet key information. Specifically, first, it is necessary to detect the field distribution density, that is, which label fields appear frequently in the entire data packet, especially the parts of the fields containing sensitive information. Further, by identifying the nested structure path of the fields containing key pattern characters, the fields can be marked to ensure that the fields do not involve redundant sensitive information. Then, by comparing the key positions of the fields for verification, it is determined whether the field positions conform to the set key pattern rules. If some fields appear frequently and contain key patterns, further key update mechanisms need to be executed to avoid redundant or invalid key fields. According to this process, the redundant key update parameters are finally obtained. The role of the redundant key update parameters is to optimize the key fields in the data packet to ensure that the sensitive information in the data packet is properly protected.

[0032] Please refer to Figure 5 , and the specific steps for obtaining the key replacement update amount are as follows: S411: Based on the redundant key update parameters, conduct a multi-segment security assessment of the currently used encryption key. Through the key segment mapping table, screen the key intervals that need to be updated to obtain the preliminary key interval update data; By analyzing the security of the current encryption key, check whether it meets the requirements of multi-segment encryption. At this time, it is necessary to rely on the key segment mapping table to help identify the intervals corresponding to each encryption key. Taking practical applications as an example, if it is assumed that in a certain encryption system, the key interval is divided into three segments, each segment involves different encryption strengths and lengths. Then, for a segment, the key segment mapping table can be used to determine which intervals of the key need to be updated. For example, if it is assumed that the encryption strength of key interval 1 is too low and needs to be updated, and the other intervals remain unchanged. By screening out the key intervals that need to be updated, the preliminary key interval update data can be obtained, preparing for the subsequent key replacement.

[0033] S412: Based on the preliminary key interval update data, perform the key replacement process, replace the hash value of the original encryption key, and generate a new key hash value, using the formula: ; Obtain the key replacement update amount , where and respectively represent the and the hash values of the and respectively represent the and the encrypted key values of the represents the total number of key intervals; According to the preliminary key interval update data, a key replacement process is carried out. The key lies in replacing the hash value in the original key interval and generating a new hash value. Specifically, for each key interval, by analyzing the changes in its hash value and encrypted key value, the key replacement update amount is calculated. During the key replacement process, the update amount of each key interval is calculated by using a formula, the original hash value in the key interval is replaced, and a new key hash value is generated. There are two key intervals, and the parameters are as follows: - The hash value of the first key interval , - The hash value of the second key interval , - The encrypted key value of the first key interval , - The encrypted key value of the second key interval , substitute into the formula to calculate the key replacement update amount : ; Therefore, the key replacement update amount is approximately 90.35. This result shows that by replacing the hash values and encrypted key values of the first and second key intervals, the obtained key replacement update amount is 90.35, further reflecting the intensity and necessity of key replacement.

[0034] Please refer to Figure 6 , and the specific steps for obtaining the optimized result of label parsing are as follows: S511: According to the key replacement update amount, identify the path and depth of each label in the data packet, extract the structural information of the label content, and evaluate the nesting level of the label path and the complexity of the marked content to obtain label evaluation data; For each tag of the data packet, first identify the tag path through key replacement update amount, where the depth of the path is the number of nested levels of the tag in the tree structure. The deeper the tag, the more complex its structure usually is. Then, extract the structural information of the tag content, such as the type, length of the tag content, and whether it contains sub-tags, etc. The data helps determine the complexity of the tag. Then, calculate the depth value according to the nested level of the tag path. The greater the depth, the more complex the nested level. Its calculation formula is the weighted sum of the nested depth of the tag path. Suppose there is a tag with a depth of 3 and a path complexity index of 2. Finally, obtain the tag complexity evaluation data through weighted calculation. In this process, the nested level and complexity are used to evaluate the computing resources required for tag parsing and help identify which tags cause bottlenecks in the parsing process.

[0035] S512: Based on the tag evaluation data, optimize the tag path. By real-time monitoring the data packet parsing process, dynamically adjust the marked path to obtain the tag path adjustment configuration. First, analyze the efficiency of the current path based on the tag evaluation data, and calculate the number of jumps of each tag path. The number of jumps refers to the number of times the tag path frequently jumps during the parsing process. Too many jumps will cause parsing delays, so dynamic adjustment is required. For each path, real-time monitor the node depth of the tag path. If the path depth is large or the node complexity is high, more processing time needs to be added to optimize the parsing accuracy of the tag. Suppose a certain path has 5 jumps, a node depth of 4, and a complexity coefficient of 1.2. Dynamically adjust the parsing strategy according to the parameters to reduce unnecessary jumps, thereby optimizing the parsing speed. On this basis, generate an adjustment plan to ensure the effectiveness of the tag path.

[0036] S513: Based on the tag path adjustment configuration, according to the requirements of tag parsing accuracy and efficiency, optimize the parsing path and adjust the parameters to obtain the tag parsing optimization result. Combined with the tag path adjustment plan, analyze the relationship between the parsing time and complexity of the tag path. First, by comparing the parsing times of each path, select the path with the shortest parsing time and the lowest complexity for further optimization. The parsing time refers to the total time required from the start to the completion of the data packet parsing. To optimize the path, appropriately adjust the parameter settings during the parsing process, such as cache size, jump frequency, and resource allocation, etc. Suppose a certain path has a parsing time of 50 milliseconds and a complexity coefficient of 2.0. After adjustment, the time is reduced to 40 milliseconds, and the complexity drops to 1.5. Finally, obtain the optimized tag path parsing result. This result represents that through a series of optimization strategies, the path parsing efficiency is improved, and the parsing process is more efficient and accurate.

[0037] A data exchange system based on a markup language, the system includes: The priority evaluation module measures the number of tags in each received markup language data packet, evaluates the complexity of the nested structure in the markup language through hierarchical analysis, measures the field length of each tag, matches it with the classification standard, determines the processing priority of the data packet, and obtains the structure priority score; The scheduling optimization module monitors the current load of the data exchange system based on the structure priority score, determines the transmission timing of each data packet, adjusts the processing sequence of the data packets through queue management, refines the distribution order within the queue, and generates an asynchronous queuing delay difference; The redundancy and key update module analyzes the markup language content in the data packet based on the asynchronous queuing delay difference, identifies duplicate tag fields and invalid metadata, calculates the proportion of redundant tags or fields, conducts a risk assessment on the key sensitivity of the redundant content, determines whether to execute the key update mechanism, and obtains the redundancy key update parameter; The key replacement module evaluates the security of the multi-segment encryption key currently in use according to the redundancy key update parameter, determines the key interval to be updated through the key segment mapping table, executes the key replacement process, overwrites the original encrypted old value with the new key hash value, and generates the key replacement update amount; The tag parsing optimization module evaluates the markup language parsing requirements in the data packet according to the key replacement update amount, analyzes the nested path of each tag, combines the depth of the tag path and the complexity of the tag content, and dynamically adjusts the parameters in the data packet parsing process to obtain the tag parsing optimization result.

[0038] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A data exchange method based on markup language, characterized in that: The following steps are involved: S1: Based on the received markup language data packets, the number of tags in each data packet is measured, the complexity of the nested structure in the markup language is evaluated through hierarchical analysis, the field length of each tag is measured, and the processing priority of the data packet is determined by matching it with the classification criteria to obtain the structural priority score; S2: Based on the structural priority score, monitor the current data exchange system load, determine the transmission timing of each data packet, adjust the processing sequence of the data packets through queue management, refine the distribution order within the queue, and generate asynchronous queuing delay difference; S3: Based on the asynchronous queuing delay difference, analyze the markup language content in the data packet, identify repeated label fields and invalid metadata, calculate the proportion of redundant labels or fields, perform risk assessment on the key sensitivity of redundant content, determine whether to execute the key update mechanism, and obtain redundant key update parameters; S4: Based on the redundant key update parameters, evaluate the security of the currently used encryption key multi-segment, determine the key interval that needs to be updated through the key segment mapping table, overwrite the original encryption old value with the new key hash value, and generate the key replacement update amount.

2. The data exchange method based on markup language according to claim 1, characterized in that: The structural priority score includes the number of tags, nested level complexity, and field length; the asynchronous queuing delay difference includes queue processing order, data packet transmission timing, and IO response delay; the redundant key update parameters include redundant field ratio, redundant tag ratio, and key sensitivity assessment results; the key replacement update amount includes key update interval, key hash change information, and encryption segment replacement results.

3. The data exchange method based on markup language according to claim 1, characterized in that: The steps for obtaining the structural priority score are specifically as follows: S111: Based on the received markup language data packet, detect the number of tags in each data packet, identify the field length of the tag, and obtain field length data; S112: Process the tags through hierarchical analysis to evaluate the complexity of the nested structure, and use the formula in combination with the field length data: ； Calculate the nested structure complexity value ,in, Representative The field length of the tag, is the average length of all tag fields, For the The nesting depth of tags, is the total number of labels in the data packet; S113: Based on the nested structure complexity value, the classification standard is matched with the nested structure complexity, and a priority evaluation is performed on each data packet to obtain a structure priority score.

4. The data exchange method based on markup language according to claim 1, characterized in that: The steps for obtaining the asynchronous queuing delay difference are specifically as follows: S211: Based on the structural priority score, monitor the current data exchange system load using the formula: ； Determine when to transmit a packet , and adjust the processing sequence of data packets through queue management, where represents the packet priority score, Represents the current system load score, Represents the processing time of the data packet, represents the delay adjustment factor, Represents the queueing time of the data packet, represents the expected processing time, Represents the system priority benchmark; S212: Based on the processing sequence of the data packets, refine the distribution order of the data packets in the queue, adjust the processing time and response order of the data packets, reorder them, optimize the IO response time, and generate an asynchronous queuing delay difference.

5. The data exchange method based on markup language according to claim 1, characterized in that: The steps for obtaining the redundant key update parameters are specifically as follows: S311: Based on the asynchronous queuing delay difference, the markup language content in the data packet is structured, the label field is split by level, the corresponding relationship between the field naming, nested structure and data content under the level is detected, the number of repeated occurrences of the field name and the consistency interval of the field value are identified, and the label field repetition degree is obtained; S312: According to the label field duplication, combined with the field naming frequency, the nested structure depth and the field value update interval, invalid metadata is screened, and the nested position of the repeated label field is extracted, using the formula: ； Get the redundant tag field density value , we get the label redundancy interval distribution, where Representative The weight parameter of each field, Representative The number of times a field is repeated, Representative The number of valid values ​​for a field, Representative The nesting level of fields, Indicates The data update intensity of each field, Represents the update intensity set of all fields, Represents the total number of fields; S313: According to the distribution of the label redundant interval, determine the field distribution density and the corresponding position of the data packet key information, identify the field nested structure path containing the key pattern characters, mark the field group that matches the sensitive key pattern rule, compare the key position for verification, and obtain the redundant key update parameter.

6. The data exchange method based on markup language according to claim 1, characterized in that: The steps for obtaining the key replacement update amount are specifically as follows: S411: Perform a multi-segment security assessment on the currently used encryption key according to the redundant key update parameter, select the key interval that needs to be updated through the key segment mapping table, and obtain preliminary key interval update data; S412: Based on the preliminary key interval update data, a key replacement process is performed to replace the hash value of the original encryption key and generate a new key hash value using the formula: ； Get key replacement update amount ,in, and Respectively represent and The hash value of the key interval, and Respectively represent and The encryption key value of the key interval, Indicates the total number of key intervals.

7. The data exchange method based on markup language according to claim 1, characterized in that: The steps also include: S5: According to the key replacement update amount, the markup language parsing requirements in the data packet are evaluated, the nested path of each label is analyzed, and the parameters in the data packet parsing process are dynamically adjusted in combination with the depth of the label path and the complexity of the label content, so as to optimize the accuracy and efficiency of the label parsing and obtain the label parsing optimization result; The tag parsing optimization results include tag nesting depth, tag path structure, and parsing accuracy.

8. The data exchange method based on markup language according to claim 7, characterized in that: The steps for obtaining the tag parsing optimization result are specifically as follows: S511: According to the key replacement update amount, identify the path and depth of each label in the data packet, extract the structural information of the label content, and evaluate the nesting level of the label path and the complexity of the label content to obtain label evaluation data; S512: Optimizing the label path based on the label evaluation data, dynamically adjusting the label path by real-time monitoring of the data packet parsing process, and obtaining a label path adjustment configuration; S513: Based on the label path adjustment configuration, according to the label resolution accuracy and efficiency requirements, the resolution path is optimized and parameters are adjusted to obtain a label resolution optimization result.

9. A data exchange system based on markup language, characterized in that: According to any one of claims 1 to 8, the data exchange method based on markup language is performed, and the system comprises: The priority assessment module determines the number of tags in each data packet based on the received markup language data packets, evaluates the complexity of the nested structure in the markup language through hierarchical analysis, measures the field length of each tag, matches it with the classification criteria, determines the processing priority of the data packet, and obtains the structural priority score; The scheduling optimization module monitors the current data exchange system load based on the structural priority score, determines the transmission timing of each data packet, adjusts the processing sequence of the data packets through queue management, refines the distribution order within the queue, and generates asynchronous queuing delay difference; The redundancy and key update module analyzes the markup language content in the data packet based on the asynchronous queuing delay difference, identifies repeated label fields and invalid metadata, calculates the proportion of redundant labels or fields, performs risk assessment on the key sensitivity of redundant content, determines whether to execute the key update mechanism, and obtains redundant key update parameters; The key replacement module evaluates the security of the currently used encryption key multi-segment according to the redundant key update parameters, determines the key interval that needs to be updated through the key segment mapping table, executes the key replacement process, overwrites the original encryption old value with the new key hash value, and generates the key replacement update amount; The label parsing optimization module evaluates the markup language parsing requirements in the data packet according to the key replacement update amount, analyzes the nested path of each label, and dynamically adjusts the parameters in the data packet parsing process in combination with the depth of the label path and the complexity of the label content to obtain the label parsing optimization result.

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