Intelligent identification system for information technology consultation

Abstract

The invention discloses an intelligent identification system for information technology consultation, and relates to the technical field of interface calling performance management, and the system automatically collects and analyzes a data input and output process and interface calling information through a data collection module, a data analysis module, an interaction performance evaluation module and an intelligent optimization module. Key features are extracted through a machine learning algorithm, and performance indexes such as data acquisition frequency, signal jitter and error retry rate of an interface are calculated. Based on the data, the system can evaluate the interaction performance of the interface in real time, compare the interaction performance with a preset performance threshold value, and automatically identify interface groups with qualified performance and abnormal performance. The data acquisition frequency is adjusted in a targeted manner, the interface calling mechanism is optimized, the data flow path is adjusted, and the response speed and the resource utilization rate of the system are improved. In the peak period, the system can effectively control the interface calling accumulation rate and the edge data processing rate, and the interaction efficiency in an information technology consultation scene is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of interface call performance management, and in particular to an intelligent recognition system for information technology consultation. Background Art

[0002] In the field of modern information technology consulting, interfaces serve as a bridge for data exchange between system modules, carrying a large amount of tasks and data transmission. As business needs become increasingly complex, the performance of system interfaces has become a key factor affecting overall system efficiency and user experience. Traditional information technology consulting systems often use static or simple optimization methods to manage interface performance, but these methods often seem inadequate when faced with growing system loads and complex business scenarios.

[0003] In the context of information technology consulting, the efficiency of interface calls directly affects the system's response speed and resource utilization. When the interface call accumulation rate is high, the system will face problems such as increased latency and decreased processing capacity, and may even cause interface timeouts, data loss, or system crashes. These problems usually stem from two aspects: one is the complexity and volume of data flow, and the other is the frequency of interface calls and error retries.

[0004] With the diversification of user needs and the improvement of real-time requirements, traditional technologies usually rely on manual intervention or simple preset rules to deal with interface performance issues, such as setting the tolerance of the interface through fixed thresholds, or relying on manual adjustment of data collection frequency and error retry mechanisms. The limitation of this approach is that it cannot dynamically adjust parameters according to real-time system status and changes, and it does not have the ability to intelligently evaluate and optimize. Faced with complex system environments, static rules often lead to over-optimization or waste of resources, affecting the flexibility and reliability of the system. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an intelligent identification system for information technology consultation to solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent recognition system for information technology consultation, including a data acquisition module, a data analysis module, an interactive performance evaluation module and an intelligent optimization module; The data collection module is used to predefine the data flow scope in the information technology consulting scenario and collect relevant data input and output processes and interface call information to establish an interactive dependency data set; The data analysis module is used to extract features of the input and output processes, the performance characteristics of the i-th interface call and the data dependency based on the interactive dependency data set through a machine learning algorithm, establish a data flow model and train it to calculate and obtain: the data collection frequency dynamic adjustment coefficient dttz of the i-th interface i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i ; The interactive performance evaluation module is used to collect the number of hops of the data flow path transmitted by the pth path of the i-th interface and the delay of the pth path of the ith interface , and associate the data acquisition frequency dynamic adjustment coefficient dttz of the i-th interface i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i , to construct the comprehensive index of interactive performance Zh i ; At the same time, the performance threshold Cp is preset to generate the comprehensive index of interactive performance Zh i Comparison and grading results with the performance threshold Cp; The intelligent optimization module is used to dynamically adjust the data collection frequency, optimize the interface call mechanism or reallocate the data flow path according to the comparison and grading results, including adjusting the peak interface call accumulation rate and optimizing the edge data processing rate, so as to improve the interaction efficiency in the information technology consulting scenario.

[0007] Preferably, the data acquisition module includes an input data acquisition unit, an output data acquisition unit and a call relationship acquisition unit; The input data collection unit is used to collect user interaction behaviors, record operation paths and input data, and intercept and parse data packets returned by third-party services; The output data acquisition unit is used to collect the transmission content and data status of the external interface; The call relationship collection unit is used to collect relevant data input and output processes and interface call information, and the relevant data input and output processes include: data flow paths across modules and intermediate node information, loss rate during data flow, number of changes during data flow, and number of checks during data flow; The interface call information includes: the interface path record of module A calling module B, interface name, call sequence, dependency chain, call frequency, and call data flow; The data collected by the input data collection unit, the output data collection unit and the call relationship collection unit are summarized to establish an interactive dependency relationship data set.

[0008] Preferably, the data analysis module includes a preprocessing unit, a model training unit and a feature extraction unit; The preprocessing unit is used to remove irrelevant data, outliers, duplicate data and normalize the interactive dependency data set to adapt it to the needs of the machine learning model, and then use the model training unit to train the interactive dependency data set using a machine learning algorithm to establish a data flow model, and use the feature extraction unit to extract the input signal features, output data features, data flow features and call performance features of the i-th interface to analyze and calculate: The data acquisition frequency dynamic adjustment coefficient dttz of the i-th interface i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i ; The call performance characteristics include collection frequency, CPU usage, and number of errors.

[0009] Preferably, the data acquisition frequency dynamic adjustment coefficient dttz of the i-th interface i Calculated by the following formula:

[0010] in, Indicates the frequency change value of the i-th interface in the current time period minus the collection frequency in the previous time period. represents the average acquisition frequency of the ith interface, Indicates the CPU usage of the i-th interface. Indicates the average CPU usage of the i-th interface. Indicates the number of errors on the i-th interface. represents the maximum number of tolerable errors of the ith interface, obtained according to the fault tolerance standard predetermined by the service level agreement; , and Represents the weight value; The input signal jitter value ddz of the i-th interface i The specific way to obtain is: S11, collect the input signal time series data of the i-th interface, the expression is: ; n represents the total number of points of the input signal feature; S12. Calculate the standard deviation of the input signal characteristics of the i-th interface to obtain the signal change amplitude value :

[0011]

[0012] in, represents the input signal characteristics of the i-th interface at the j-th time, represents the mean value of the input signal characteristics of the i-th interface; S13, based on the mean value of the input signal characteristics of the i-th interface Sum signal change amplitude value , calculate the input signal jitter value ddz of the i-th interface using the following formula: i :

[0013] The i-th interface error retry rate cwl i Calculated by the following formula:

[0014] Where m represents the number of error categories, including network errors, interface timeouts, and server response failure categories; represents the weight of each error type, Indicates the number of retries of the i-th interface on the k-th error type. Indicates the total number of failures of the i-th interface for the k-th error type.

[0015] Preferably, the interaction performance evaluation module includes a path hop count calculation unit and a per-path delay calculation unit; The path hop calculation unit is used to collect the number of intermediate nodes in the path during the transmission of the pth path of the ith interface, and calculate the path hop number of the data flow transmitted by the pth path of the ith interface through the following formula: :

[0016] in, is the total number of intermediate nodes in the transmission process of the pth path, Indicates that node g is a transit node on path p, otherwise , if the pth path is a direct connection, that is, there is no intermediate node, then ; The delay calculation unit for each path is used to calculate the delay of the pth path of the i-th interface. :

[0017] in, is the queue delay of the g-th node on the path, is the transmission delay of the link between node g and node g+1.

[0018] Preferably, the interactive performance evaluation module further includes an association unit and an evaluation unit; The associated unit is used to calculate the path hop number of the data flow transmitted by the pth path of the i-th interface , the delay of the pth path of the ith interface , dynamic adjustment coefficient of data acquisition frequency of the ith interface dttz i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i After dimensionless processing, the comprehensive index of interaction performance Zh of the i-th interface is calculated by the following formula: i :

[0019] in, , , , and Respectively represent the number of hops of the data flow transmitted by the pth path of the i-th interface , the delay of the pth path of the ith interface , dynamic adjustment coefficient of data acquisition frequency of the ith interface dttz i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i The weight value of .

[0020] Preferably, the evaluation unit is used to preset a performance threshold Cp for generating a comprehensive index Zh of the interaction performance of the ith interface. i The comparison and grading results with the performance threshold Cp include: When the interaction performance comprehensive index Zh of the i-th interface i > performance threshold Cp, it indicates that the interaction performance of the i-th interface is qualified, and the first port qualified group is generated; When the performance threshold Cp×80%≤the comprehensive index of interaction performance Zh of the i-th interface i When ≤ the performance threshold Cp, it means that the interaction performance of the i-th interface is unqualified, and the second port abnormal group is generated; When the interaction performance comprehensive index Zh of the i-th interface i When the performance threshold Cp×80% is less than the performance threshold Cp, it indicates that the interaction performance of the i-th interface is unqualified, and a third port abnormal group is generated.

[0021] Preferably, the intelligent optimization module includes a first optimization unit, a second optimization unit and a third optimization unit; The first optimization unit is used to trigger the first strategy after identifying the qualified group of the first port, including: for the i-th port, increasing the current data collection frequency by 5%-10%, improving the interface call accumulation rate control mechanism by 10%-15%; improving the current edge data processing rate by 5%-10%; maintaining the existing path without adjusting the number of data flow path hops.

[0022] Preferably, the second optimization unit is used to trigger the second strategy after identifying the second port abnormal group, including: for the i-th port, reducing the current data collection frequency by 5%-10%, reducing the interface call accumulation rate control mechanism by 10%-15%; increasing the edge data processing rate by 5%-10%; adjusting to reduce the number of data flow path hops by 10%-15%.

[0023] Preferably, the third optimization unit is used to trigger the third strategy after identifying the third port abnormal group, including: for the i-th port, reducing the current data collection frequency by 20%-30%, reducing the interface call accumulation rate control mechanism by 16%-30%; improving the edge data processing rate by 11%-20%; adjusting to reduce the number of data flow path hops by 16%-30%, and optimizing the error retry rate control mechanism by 20%-30%.

[0024] The present invention provides an intelligent recognition system for information technology consultation. It has the following beneficial effects: (1) This is an intelligent identification system for information technology consulting. By pre-defining the information technology consulting scenario, the data collection module can effectively identify and collect relevant input and output processes and interface call information, thereby providing a high-quality data source for subsequent data analysis. The establishment of an interactive dependency dataset provides key data support for subsequent system analysis, helps to reveal the data flow and dependency between interfaces, and provides a basis for the formulation of optimization strategies.

[0025] (2) This is an intelligent recognition system for information technology consulting. Through machine learning algorithms, the data analysis module can automatically extract key features from a large amount of data, helping to accurately establish a data flow model, thereby better capturing potential performance issues of the system. The calculated data collection frequency dynamic adjustment coefficient dttz of the i-th interface i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i It provides a quantitative basis for subsequent performance evaluation and optimization, ensuring that the optimization strategy can be adjusted according to the actual system status.

[0026] (3) An intelligent identification system for information technology consulting, by analyzing the number of hops of the data flow path transmitted by the pth path of the i-th interface ,Accurately calculating the number of path hops helps identify high-latency paths and provides direction for subsequent optimization. Delay calculation not only considers the queue delay of each node, but also the link transmission delay between nodes. Queue delay reflects the waiting time when data is processed at each node, while link delay is related to factors such as the bandwidth and transmission distance of the network connection. Both together determine the total delay of the path.

[0027] (4) This is an intelligent identification system for information technology consulting. By integrating multi-dimensional performance indicators, the comprehensive index of interactive performance can accurately reflect the actual performance of the interface and provide a reliable basis for subsequent optimization decisions. The function of the evaluation unit is to compare the comprehensive index of interactive performance with the performance threshold CP based on the preset performance threshold Cp, thereby generating different port groups. This process divides the interface into three different performance groups based on the comparison and grading results of the comprehensive performance index and the performance threshold CP, and generates corresponding strategies for dynamic optimization. Through the adjustment of different strategies, the system parameters can be finely adjusted under different performance conditions to avoid resource waste or performance degradation caused by excessive adjustment. Whether it is optimizing edge data processing, reducing the number of path hops, or adjusting the frequency of data collection, the optimization strategy aims to improve the stability, response speed and processing power of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The present invention is a flowchart of an intelligent identification system for information technology consultation. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Example 1 See also Figure 1 ,The present invention provides an intelligent recognition system for information technology consultation, including a data acquisition module, a data analysis module, an interactive performance evaluation module and an intelligent optimization module; The data collection module is used to predefine the data flow scope in the information technology consulting scenario and collect relevant data input and output processes and interface call information to establish an interactive dependency data set; The data analysis module is used to extract features of the input and output processes, the performance characteristics of the i-th interface call and the data dependency based on the interactive dependency data set through a machine learning algorithm, establish a data flow model and train it to calculate and obtain: the data collection frequency dynamic adjustment coefficient dttz of the i-th interface i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i ; The interactive performance evaluation module is used to collect the number of hops of the data flow path transmitted by the pth path of the i-th interface and the delay of the pth path of the ith interface , and associate the data acquisition frequency dynamic adjustment coefficient dttz of the i-th interface i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i , to construct the comprehensive index of interactive performance Zh i ; At the same time, the performance threshold Cp is preset to generate the comprehensive index of interactive performance Zh i Comparison and grading results with the performance threshold Cp; The intelligent optimization module is used to dynamically adjust the data collection frequency, optimize the interface call mechanism or reallocate the data flow path according to the comparison and grading results, including adjusting the peak interface call accumulation rate and optimizing the edge data processing rate, so as to improve the interaction efficiency in the information technology consulting scenario.

[0031]

[0032] In this embodiment, through the data collection module and the data analysis module, the system can automatically collect and analyze various performance data of the interface call, such as data collection frequency, input signal jitter, error retry rate, etc. The machine learning algorithm is used to extract features from these data, and the data flow model is established in combination with the interactive dependency relationship, and the comprehensive interactive performance index Zh is calculated in real time. i, making performance evaluation more accurate and intelligent. Traditional methods usually rely on manual intervention or static rules to adjust interface performance, which is not only prone to over-optimization or resource waste, but also difficult to cope with complex real-time changes. Using the intelligent optimization module, based on the comparison of the Zhi value of real-time evaluation with the performance threshold Cp, the system can automatically identify the performance bottleneck of the interface, and dynamically adjust the data collection frequency, optimize the interface call mechanism or reallocate the data flow path according to the actual situation. This dynamic adjustment method can effectively improve the response speed of the system, reduce delays, and avoid timeouts and system crashes. The system can monitor in real time and adjust the interface call accumulation rate and edge data processing rate according to the performance changes of the interface to adapt to changes in business needs and system load. Through intelligent optimization, the system can not only improve the current interaction efficiency, but also better adapt to the diversity and variability of business scenarios, ensuring the flexibility and reliability of the system. In traditional methods, interface performance optimization requires a lot of manual adjustment and monitoring, while the intelligent optimization system can greatly reduce the need for manual intervention. The system can automatically make adjustments based on real-time data, helping the technical team to better focus on higher-level strategic decisions and business innovation, thereby improving overall work efficiency. The system is not only able to optimize in the initial stage, but also has the ability to continuously learn and adapt. As business needs change and technology advances, the system can continuously adjust its optimization strategy to maintain its optimal performance in a dynamic environment. This adaptability ensures the stability and efficiency of the system in long-term operation.

[0033] Example 2 This embodiment is explained in Example 1. For details, please refer to Figure 1 , the data acquisition module includes an input data acquisition unit, an output data acquisition unit and a call relationship acquisition unit; The input data collection unit is used to collect user interaction behaviors, record operation paths and input data, and intercept and parse data packets returned by third-party services; The output data acquisition unit is used to collect the transmission content and data status of the external interface; The call relationship collection unit is used to collect relevant data input and output processes and interface call information, and the relevant data input and output processes include: data flow paths across modules and intermediate node information, loss rate during data flow, number of changes during data flow, and number of checks during data flow; The interface call information includes: the interface path record of module A calling module B, interface name, call sequence, dependency chain, call frequency, and call data flow; The data collected by the input data collection unit, the output data collection unit and the call relationship collection unit are summarized to establish an interactive dependency relationship data set.

[0034] In this embodiment, the input data collection unit can not only record the user's interactive behavior, but also intercept and parse the data packets returned by the third-party service, so as to obtain more comprehensive system data and provide rich background information for subsequent analysis.

[0035] The output data acquisition unit is used to collect the transmission content and data status of the external interface, ensuring that the interaction between the system and the external interface is fully captured, providing a deep understanding of the communication process between the system and the external world.

[0036] The collection of interface call information can record in detail the dependencies between modules, call paths, interface names, call sequences, call frequencies, and call data traffic. This information is crucial for subsequent interface performance evaluation, helping the system to fully understand the interaction between modules and the usage of interfaces, thereby identifying interfaces with high loads, frequent calls, or excessive resource usage. Based on the interactive dependency dataset, the system can accurately evaluate the performance of interfaces and data flow paths, identify problems such as high latency and high packet loss during data transmission, and provide specific directions for subsequent optimization. For example, some data flow paths may be transmitted between multiple modules and involve high packet loss rates or data changes. The system can reduce the occurrence of problems by adjusting the path and optimizing the transmission mechanism.

[0037] Example 3 This embodiment is explained in Example 1. For details, please refer to Figure 1 , the data analysis module includes a preprocessing unit, a model training unit and a feature extraction unit; The preprocessing unit is used to remove irrelevant data, abnormal values, duplicate data and normalize the interactive dependency data set to adapt it to the needs of the machine learning model, and then use the model training unit to train the interactive dependency data set using a machine learning algorithm to establish a data flow model, and use the feature extraction unit to extract the input signal features, output data features, data flow features and call performance features of the i-th interface to analyze and calculate: the data acquisition frequency dynamic adjustment coefficient dttzi of the i-th interface, the input signal jitter value ddzi of the i-th interface and the error retry rate cwli of the i-th interface; The call performance characteristics include collection frequency, CPU usage, and number of errors.

[0038] The data acquisition frequency dynamic adjustment coefficient dttz of the i-th interface i Calculated by the following formula:

[0039] in, Indicates the frequency change value of the i-th interface in the current time period minus the collection frequency in the previous time period. represents the average acquisition frequency of the ith interface, Indicates the CPU usage of the i-th interface. Indicates the average CPU usage of the i-th interface. Indicates the number of errors on the i-th interface. represents the maximum number of tolerable errors of the ith interface, obtained according to the fault tolerance standard predetermined by the service level agreement; , and Indicates the weight value; based on the changes in the interface's collection frequency, CPU usage, number of errors, and other factors, calculates the dynamic adjustment coefficient of the data collection frequency of the i-th interface dttz i This coefficient can reflect the load of the interface and adjust the data collection frequency according to actual needs to avoid resource waste caused by excessive collection or response lag caused by too low collection frequency. It can flexibly adjust the resource allocation and performance of the interface without adding too much additional overhead.

[0040] The input signal jitter value ddz of the i-th interface i The specific way to obtain is: S11, collect the input signal time series data of the i-th interface, the expression is: ; n represents the total number of points of the input signal feature; S12. Calculate the standard deviation of the input signal characteristics of the i-th interface to obtain the signal change amplitude value :

[0041]

[0042] in, represents the input signal characteristics of the i-th interface at the j-th time, represents the mean value of the input signal characteristics of the i-th interface; S13, based on the mean value of the input signal characteristics of the i-th interface Sum signal change amplitude value , calculate the input signal jitter value ddz of the i-th interface using the following formula: i :

[0043] This calculation helps identify input signal instabilities and can provide detailed performance analysis for subsequent interface optimization, especially when signal fluctuations affect interface performance.

[0044] The i-th interface error retry rate cwl i Calculated by the following formula:

[0045] Where m represents the number of error categories, including network errors, interface timeouts, and server response failure categories; represents the weight of each error type, Indicates the number of retries of the i-th interface on the k-th error type. Indicates the total number of failures of the i-th interface on the k-th error type. Considering that different types of errors may affect interface performance, the system calculates the retry rate of each type of error by setting different weights to ensure that the impact of different error types on the overall system performance is fully evaluated. Through this calculation method, the system can identify interfaces with high retry rates and focus on optimizing these interfaces to improve the stability and response speed of the overall system.

[0046] In this embodiment, by real-time calculation and dynamic adjustment of the data acquisition frequency, signal jitter value and error retry rate of the interface, the system can adjust its performance parameters according to the real-time interface load and status. This adaptive capability enables the system to cope with changing business needs and dynamic system loads. Based on the dynamically adjusted coefficients and parameters, the system can automatically optimize the interface call mechanism, data acquisition frequency and error retry strategy, thereby improving the overall efficiency of the system, reducing latency, reducing resource waste, and enhancing user experience.

[0047] Example 4 This embodiment is explained in Example 1. For details, please refer to Figure 1 , the interactive performance evaluation module includes a path hop count calculation unit and a per-path delay calculation unit; The path hop calculation unit is used to collect the number of intermediate nodes in the path during the transmission of the pth path of the ith interface, and calculate the path hop number of the data flow transmitted by the pth path of the ith interface through the following formula: :

[0048] in, is the total number of intermediate nodes in the transmission process of the pth path, Indicates that node g is a transit node on path p, otherwise , if the pth path is a direct connection, that is, there is no intermediate node, then ; The delay calculation unit for each path is used to calculate the delay of the pth path of the i-th interface. :

[0049] in, is the queue delay of the g-th node on the path, is the transmission delay of the link between node g and node g+1.

[0050] In this embodiment, by calculating the number of hops of the path, the complexity of the path and the potential delay hazards in the data transmission process can be reflected. A path with a higher number of hops may mean more transit nodes, which may increase the transmission time and reduce the response efficiency. Therefore, accurately calculating the number of path hops helps to identify high-latency paths and provide direction for subsequent optimization. The delay calculation not only considers the queue delay of each node, but also the link transmission delay between nodes. The queue delay reflects the waiting time when the data is processed at each node, while the link delay is related to factors such as the bandwidth and transmission distance of the network connection. The two together determine the total delay of the path.

[0051] Example 5 This embodiment is explained in Example 4. For details, please refer to Figure 1 , the interactive performance evaluation module also includes an association unit and an evaluation unit; The associated unit is used to calculate the path hop number of the data flow transmitted by the pth path of the i-th interface , the delay of the pth path of the ith interface , dynamic adjustment coefficient of data acquisition frequency of the ith interface dttz i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i After dimensionless processing, the comprehensive index of interaction performance Zh of the i-th interface is calculated by the following formula: i :

[0052] in, , , , and Respectively represent the number of hops of the data flow transmitted by the pth path of the i-th interface , the delay of the pth path of the ith interface , dynamic adjustment coefficient of data acquisition frequency of the ith interface dttz i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i The weight value of .

[0053] The evaluation unit is used to preset a performance threshold Cp and to generate a comprehensive index Zh of the interaction performance of the i-th interface. i The comparison and grading results with the performance threshold Cp include: When the interaction performance comprehensive index Zh of the i-th interface i > performance threshold Cp, it indicates that the interaction performance of the i-th interface is qualified, and the first port qualified group is generated; When the performance threshold Cp×80%≤the comprehensive index of interaction performance Zh of the i-th interface i When ≤ the performance threshold Cp, it means that the interaction performance of the i-th interface is unqualified, and the second port abnormal group is generated; When the interaction performance comprehensive index Zh of the i-th interface i When the performance threshold Cp×80% is less than the performance threshold Cp, it indicates that the interaction performance of the i-th interface is unqualified, and a third port abnormal group is generated.

[0054] In this embodiment, by integrating multi-dimensional performance indicators, the interaction performance comprehensive index can accurately reflect the actual performance of the interface and provide a reliable basis for subsequent optimization decisions. The function of the evaluation unit is to compare the interaction performance comprehensive index with the performance threshold CP based on the preset performance threshold Cp, thereby generating different port groups. This process divides the interface into three different performance groups based on the comparison results of the performance comprehensive index and the threshold.

[0055] Example 6 This embodiment is explained in Example 5. For details, please refer to Figure 1 , the intelligent optimization module includes a first optimization unit, a second optimization unit and a third optimization unit; The first optimization unit is used to trigger the first strategy after identifying the qualified group of the first port, including: for the i-th port, increasing the current data collection frequency by 5%-10%, improving the interface call accumulation rate control mechanism by 10%-15%; improving the current edge data processing rate by 5%-10%; maintaining the existing path without adjusting the number of data flow path hops.

[0056] The second optimization unit is used to trigger the second strategy after identifying the second port abnormal group, including: for the i-th port, reducing the current data collection frequency by 5%-10%, reducing the interface call accumulation rate control mechanism by 10%-15%; increasing the edge data processing rate by 5%-10%; adjusting to reduce the number of data flow path hops by 10%-15%.

[0057] The third optimization unit is used to trigger the third strategy after identifying the third port abnormal group, including: for the i-th port, reducing the current data collection frequency by 20%-30%, reducing the interface call accumulation rate control mechanism by 16%-30%; improving the edge data processing rate by 11%-20%; adjusting and reducing the number of data flow path hops by 16%-30%, and optimizing the error retry rate control mechanism by 20%-30%.

[0058] In this embodiment, by reducing the data acquisition frequency and the interface call accumulation rate control mechanism, the system load can be effectively reduced, the delay can be reduced, and the stability of the system can be improved. By enhancing the edge computing capability, the third optimization unit ensures that the system can cope with more data requests and improve the overall response capability. Reducing the number of path hops and optimizing the error retry mechanism can effectively reduce the negative impact of delays and error retries during data transmission and ensure smooth data transmission. The intelligent optimization module automatically identifies different performance groups based on the evaluation results and triggers corresponding optimization strategies based on the different characteristics of the groups. This automated process greatly improves the operating efficiency of the system. Through the adjustment of different strategies, the system parameters can be finely adjusted under different performance conditions to avoid resource waste or performance degradation caused by excessive adjustment. Whether it is optimizing edge data processing, reducing the number of path hops, or adjusting the data acquisition frequency, the optimization strategy aims to improve the stability, response speed and processing power of the system.

[0059] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0060] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. An intelligent identification system for information technology consultation, characterized in that: Including data acquisition module, data analysis module, interactive performance evaluation module and intelligent optimization module; The data collection module is used to predefine the data flow scope in the information technology consulting scenario and collect relevant data input and output processes and interface call information to establish an interactive dependency data set; The data analysis module is used to extract features of the input and output processes, the performance characteristics of the i-th interface call and the data dependency based on the interactive dependency data set through a machine learning algorithm, establish a data flow model and train it to calculate and obtain: the data collection frequency dynamic adjustment coefficient dttz of the i-th interface i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i ; The interactive performance evaluation module is used to collect the number of hops of the data flow path transmitted by the pth path of the i-th interface and the delay of the pth path of the ith interface , and associate the data acquisition frequency dynamic adjustment coefficient dttz of the i-th interface i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i , to construct the comprehensive index of interactive performance Zh i ; At the same time, the performance threshold Cp is preset to generate the comprehensive index of interactive performance Zh i Comparison and grading results with the performance threshold Cp; The intelligent optimization module is used to dynamically adjust the data collection frequency, optimize the interface call mechanism or reallocate the data flow path according to the comparison and grading results, including adjusting the peak interface call accumulation rate and optimizing the edge data processing rate, so as to improve the interaction efficiency in the information technology consulting scenario.

2. The intelligent identification system for information technology consultation according to claim 1, characterized in that: The data acquisition module includes an input data acquisition unit, an output data acquisition unit and a call relationship acquisition unit; The input data collection unit is used to collect user interaction behaviors, record operation paths and input data, and intercept and parse data packets returned by third-party services; The output data acquisition unit is used to collect the transmission content and data status of the external interface; The call relationship collection unit is used to collect relevant data input and output processes and interface call information, and the relevant data input and output processes include: data flow paths across modules and intermediate node information, loss rate during data flow, number of changes during data flow, and number of checks during data flow; The interface call information includes: the interface path record of module A calling module B, interface name, call sequence, dependency chain, call frequency, and call data flow; The data collected by the input data collection unit, the output data collection unit and the call relationship collection unit are summarized to establish an interactive dependency relationship data set.

3. The intelligent identification system for information technology consultation according to claim 1, characterized in that: The data analysis module includes a preprocessing unit, a model training unit and a feature extraction unit; The preprocessing unit is used to remove irrelevant data, outliers, duplicate data and normalize the interactive dependency data set to adapt it to the needs of the machine learning model, and then use the model training unit to train the interactive dependency data set using a machine learning algorithm to establish a data flow model, and use the feature extraction unit to extract the input signal features, output data features, data flow features and call performance features of the i-th interface to analyze and calculate: The data acquisition frequency dynamic adjustment coefficient dttz of the i-th interface i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i ; The call performance characteristics include collection frequency, CPU usage, and number of errors.

4. The intelligent identification system for information technology consultation according to claim 3 is characterized in that: The data acquisition frequency dynamic adjustment coefficient dttz of the i-th interface i Calculated by the following formula: ； in, Indicates the frequency change value of the i-th interface in the current time period minus the collection frequency in the previous time period. represents the average acquisition frequency of the ith interface, Indicates the CPU usage of the i-th interface. Indicates the average CPU usage of the i-th interface. Indicates the number of errors on the i-th interface. represents the maximum number of tolerable errors of the ith interface, obtained according to the fault tolerance standard predetermined by the service level agreement; , and Represents the weight value; The input signal jitter value ddz of the i-th interface i The specific way to obtain is: S11, collect the time series data of the input signal of the ith interface, the expression is: ; n represents the total number of points of the input signal feature; S12. Calculate the standard deviation of the input signal characteristics of the i-th interface to obtain the signal change amplitude value : ； ； in, represents the input signal characteristics of the i-th interface at the j-th time, represents the mean value of the input signal characteristics of the i-th interface; S13, based on the mean value of the input signal characteristics of the i-th interface Sum signal change amplitude value , calculate the input signal jitter value ddz of the i-th interface using the following formula: i : ； The i-th interface error retry rate cwl i Calculated by the following formula: ； Where m represents the number of error categories, including network errors, interface timeouts, and server response failure categories; represents the weight of each error type, Indicates the number of retries of the i-th interface on the k-th error type. Indicates the total number of failures of the i-th interface for the k-th error type.

5. The intelligent identification system for information technology consultation according to claim 1, characterized in that: The interactive performance evaluation module includes a path hop count calculation unit and a per-path delay calculation unit; The path hop calculation unit is used to collect the number of intermediate nodes in the path during the transmission of the pth path of the ith interface, and calculate the path hop number of the data flow transmitted by the pth path of the ith interface through the following formula: : ； in, is the total number of intermediate nodes in the transmission process of the pth path, Indicates that node g is a transit node on path p, otherwise , if the pth path is a direct connection, that is, there is no intermediate node, then ; The delay calculation unit for each path is used to calculate the delay of the pth path of the i-th interface. : ； in, is the queue delay of the g-th node on the path, is the transmission delay of the link between node g and node g+1.

6. The intelligent identification system for information technology consultation according to claim 1, characterized in that: The interactive performance evaluation module also includes an association unit and an evaluation unit; The associated unit is used to calculate the path hop number of the data flow transmitted by the pth path of the i-th interface , the delay of the pth path of the ith interface , dynamic adjustment coefficient of data acquisition frequency of the ith interface dttz i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i After dimensionless processing, the comprehensive index of interaction performance Zh of the i-th interface is calculated by the following formula: i : ； in, , , , and Respectively represent the number of hops of the data flow transmitted by the pth path of the i-th interface , the delay of the pth path of the ith interface , dynamic adjustment coefficient of data acquisition frequency of the i-th interface dttz i , the input signal jitter value ddz of the i-th interface i and the i-th interface error retry rate cwl i The weight value of .

7. The intelligent identification system for information technology consultation according to claim 6, characterized in that: The evaluation unit is used to preset a performance threshold Cp and to generate a comprehensive index Zh of the interaction performance of the i-th interface. i The comparison and grading results with the performance threshold Cp include: When the interaction performance comprehensive index Zh of the i-th interface i > performance threshold Cp, it indicates that the interaction performance of the i-th interface is qualified, and the first port qualified group is generated; When the performance threshold Cp×80%≤the comprehensive index of interaction performance Zh of the i-th interface i When ≤ the performance threshold Cp, it means that the interaction performance of the i-th interface is unqualified, and the second port abnormal group is generated; When the interaction performance comprehensive index Zh of the i-th interface i When the performance threshold Cp×80% is less than the performance threshold Cp, it indicates that the interaction performance of the i-th interface is unqualified, and a third port abnormal group is generated.

8. The intelligent identification system for information technology consultation according to claim 7, characterized in that: The intelligent optimization module includes a first optimization unit, a second optimization unit and a third optimization unit; The first optimization unit is used to trigger the first strategy after identifying the first port qualified group, including: for the i-th port, increasing the current data collection frequency by 5%-10% and improving the interface call accumulation rate control mechanism by 10%-15%; Improve the current edge data processing rate by 5%-10%; maintain the existing path without adjusting the number of data flow path hops.

9. The intelligent identification system for information technology consultation according to claim 8, characterized in that: The second optimization unit is used to trigger the second strategy after identifying the second port abnormal group, including: for the i-th port, reducing the current data collection frequency by 5%-10%, reducing the interface call accumulation rate control mechanism by 10%-15%; increasing the edge data processing rate by 5%-10%; adjusting to reduce the number of data flow path hops by 10%-15%.

10. The intelligent identification system for information technology consultation according to claim 8, characterized in that: The third optimization unit is used to trigger the third strategy after identifying the third port abnormal group, including: for the i-th port, reducing the current data collection frequency by 20%-30%, reducing the interface call accumulation rate control mechanism by 16%-30%; improving the edge data processing rate by 11%-20%; adjusting and reducing the number of data flow path hops by 16%-30%, and optimizing the error retry rate control mechanism by 20%-30%.

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