Interface monitoring method and device, equipment, storage medium and program product

Abstract

The invention discloses an interface monitoring method and device, equipment, a storage medium and a program product, and relates to the technical field of interface processing, and the method comprises the steps: carrying out the annotation scanning of a full-amount interface of a transaction system, and obtaining interface information; according to the interface information, marking classification is carried out on the full-quantity interfaces, classification labels are obtained, and the classification labels comprise multiple types of interface indexes; according to the multiple types of interface indexes, comparing the interface information with a preset alarm threshold value to obtain a comparison result; and when the comparison result has the abnormal index, performing strategy matching on the abnormal index through a preset intelligent problem assistant model to obtain an interface optimization plan. According to the method and the device, the full-quantity interfaces of the transaction system are marked and classified, and the multiple types of interface indexes are set, so that when the interface information is monitored in real time, the multiple types of interface indexes can be compared with the preset alarm threshold value, interface abnormity monitoring and automatic optimization are realized, the situation that log screening consumes a large amount of time is avoided, and the efficiency is improved. Therefore, the stability and reliability of the transaction system are improved.

Description

Technical Field

[0001] The present application relates to the field of interface processing technology, and in particular to an interface monitoring method, apparatus, device, storage medium, and program product. Background Art

[0002] With the development of the internet, trading systems are becoming increasingly large, and the dependencies between them are becoming increasingly complex. A single system function may involve multiple services, necessitating real-time system monitoring to screen for issues. However, existing trading system monitoring focuses primarily on system-level errors, generating alerts only for abnormal services.

[0003] When new interfaces are added, interfaces change, or interfaces experience issues, existing monitoring solutions struggle to identify the faulty interface. This requires a step-by-step, time-consuming sifting through logs. Consequently, existing trading system monitoring focuses primarily on system-level error reporting, making it difficult to address diverse interface requirements at the interface level. Summary of the Invention

[0004] The main purpose of this application is to provide an interface monitoring method, device, equipment, storage medium and program product, aiming to solve the technical problem that the monitoring of existing transaction systems focuses more on error reporting in the system dimension and is difficult to cope with diverse interface requirements at the interface level.

[0005] To achieve the above objectives, the present application proposes an interface monitoring method, which includes:

[0006] Scan all interfaces of the trading system for annotations to obtain corresponding interface information;

[0007] Classify the full set of interfaces according to the interface information to obtain corresponding classification labels, where the classification labels include multiple types of interface indicators;

[0008] Comparing the interface information with preset alarm thresholds according to the multiple types of interface indicators to obtain a comparison result;

[0009] When there are abnormal indicators in the comparison results, a preset intelligent problem assistant model is used to perform strategy matching on the abnormal indicators to obtain an interface optimization plan.

[0010] In one embodiment, the step of performing annotation scanning on all interfaces of the trading system to obtain corresponding interface information includes:

[0011] Scan the full set of interfaces of the trading system through the message middleware to obtain the interface name and channel information corresponding to the full set of interfaces;

[0012] Construct a corresponding full interface list based on the interface name and the channel information;

[0013] Collecting interface log data from all interfaces at a preset time frequency;

[0014] The full interface list and the interface log data are used as interface information of the full interface.

[0015] In one embodiment, the step of labeling and classifying the full set of interfaces according to the interface information to obtain corresponding classification labels includes:

[0016] Determining the labeling dimensions of the full interface, the labeling dimensions including the channel dimension and the risk level dimension;

[0017] According to the full interface list, the full interface is classified and labeled according to the channel dimension to obtain an interface channel label, where the interface channel label is used to represent the importance of the channel called by the interface;

[0018] Classify and label all interfaces according to the risk level dimension to obtain interface risk labels, where the interface risk labels are used to characterize the degree of influence of the channel called by the interface;

[0019] The interface channel label and the interface risk label are used as classification labels for the full interface.

[0020] In one embodiment, the step of comparing the interface information with a preset alarm threshold according to the multiple types of interface indicators to obtain a comparison result includes:

[0021] Determine multiple types of interface indicators of the classification label, wherein the multiple types of interface indicators include a request volume indicator, an error volume indicator, and a time consumption indicator;

[0022] Summarize the interface log data according to the request volume index, the error volume index, and the time consumption index to obtain interface index data corresponding to the same type of tags in the same time period;

[0023] Determine whether the interface indicator data exceeds a preset alarm threshold;

[0024] When the interface indicator data exceeds the preset alarm threshold, the interface log data corresponding to the preset alarm threshold is marked as abnormal to obtain an abnormal indicator, and the abnormal indicator is used as a comparison result.

[0025] In one embodiment, when there are abnormal indicators in the comparison results, the step of performing strategy matching on the abnormal indicators through a preset intelligent problem assistant model to obtain an interface optimization plan includes:

[0026] Locating the full interface according to the abnormal indicator, and determining the abnormal interface and abnormal information of the abnormal interface;

[0027] Performing problem analysis on the abnormal information through a preset intelligent problem assistant model to obtain the abnormal factors of the abnormal interface;

[0028] According to the abnormal factors, matching is performed in a preset knowledge base to obtain an interface optimization plan, wherein the preset knowledge base includes a business knowledge base, a solution knowledge base, and a production event knowledge base;

[0029] The abnormal interface is adjusted according to the interface optimization plan.

[0030] In one embodiment, before the step of performing annotation scanning on all interfaces of the trading system to obtain corresponding interface information, the step further includes:

[0031] Build a business knowledge base, a solution knowledge base, and a production event knowledge base. The business knowledge base includes product business knowledge corresponding to the transaction system, the solution knowledge base includes historical alarm issues, and the production event knowledge base includes solutions corresponding to production problem events.

[0032] Extracting content from the business knowledge base, the solution knowledge base, and the production event knowledge base in a sharded manner to obtain business knowledge, exception information, and solutions;

[0033] Dividing the business knowledge, the abnormal information, and the solution into a training data set and a test data set;

[0034] Training a preset AI model based on the training data set to obtain an initial assistant model;

[0035] Input the test data set into the initial assistant model and determine whether the accuracy of the output result reaches a preset threshold;

[0036] If the preset threshold is not reached, return to the step of training the preset AI model according to the training data set until the accuracy reaches the preset threshold, and use the initial assistant model corresponding to the preset threshold as the intelligent question assistant model.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes an interface monitoring device, which includes:

[0038] The annotation scanning module is used to perform annotation scanning on all interfaces of the trading system to obtain the corresponding interface information;

[0039] A labeling and classification module is used to label and classify the full set of interfaces according to the interface information to obtain corresponding classification labels, where the classification labels include multiple types of interface indicators;

[0040] A threshold alarm module, configured to compare the interface information with a preset alarm threshold according to the multiple types of interface indicators to obtain a comparison result;

[0041] The interface optimization module is used to, when there are abnormal indicators in the comparison results, perform strategy matching on the abnormal indicators through a preset intelligent problem assistant model to obtain an interface optimization plan.

[0042] In addition, to achieve the above-mentioned purpose, the present application also proposes an interface monitoring device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the interface monitoring method described above.

[0043] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the interface monitoring method described above are implemented.

[0044] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the interface monitoring method described above are implemented.

[0045] One or more technical solutions proposed in this application have at least the following technical effects: the interface monitoring method of this application includes: performing annotation scanning on all interfaces of the transaction system to obtain corresponding interface information; labeling and classifying all interfaces according to the interface information to obtain corresponding classification labels, and the classification labels include multiple types of interface indicators; based on the multiple types of interface indicators, comparing the interface information with preset alarm thresholds to obtain comparison results; when there are abnormal indicators in the comparison results, performing strategy matching on the abnormal indicators through a preset intelligent problem assistant model to obtain an interface optimization plan.

[0046] Since this application labels and classifies all interfaces of the trading system and sets multiple types of interface indicators, multiple types of interface indicators can be compared with preset alarm thresholds when monitoring interface information in real time to achieve interface anomaly monitoring and automatic optimization, avoiding the situation where log screening takes a lot of time, thereby improving the stability and reliability of the trading system. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 A flowchart of the first embodiment of the interface monitoring method of this application is provided;

[0050] Figure 2 A brief flowchart of monitoring and management of the transaction system interface provided in Example 1 of this application;

[0051] Figure 3 This is an example diagram of the channel dimension and risk level dimension provided in Example 1 of this application;

[0052] Figure 4 This is an example diagram of multiple types of interface indicators provided in Example 1 of this application;

[0053] Figure 5 A schematic diagram of the process of model anomaly analysis provided in Example 1 of the present application;

[0054] Figure 6 A flowchart of the second embodiment of the interface monitoring method of this application is provided;

[0055] Figure 7 This is a schematic diagram of the module structure of the interface monitoring device according to an embodiment of the present application;

[0056] Figure 8 This is a schematic diagram of the device structure of the hardware operating environment involved in the interface monitoring method in the embodiment of the present application.

[0057] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0058] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0059] In order to better understand the technical solution of this application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0060] It should be noted that the execution subject of this embodiment can be a computing service device with annotation scanning, label classification, and interface monitoring functions, such as a personal computer, server, etc., or an electronic device capable of performing the above functions, an interface monitoring device that executes the interface monitoring method of this application, etc., and this embodiment is not limited to this. The following uses the interface monitoring device as an example to illustrate this embodiment and the following embodiments.

[0061] Based on this, the first embodiment of the present application is proposed. The embodiment of the present application provides an interface monitoring method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the interface monitoring method of this application.

[0062] In this embodiment, the interface monitoring method includes steps S10 to S40:

[0063] Step S10: perform annotation scanning on all interfaces of the trading system to obtain corresponding interface information.

[0064] It should be noted that the trading system is a software system used to handle various transaction-related operations, covering multiple links in the transaction process, including order creation, order processing, payment processing, inventory management, etc.

[0065] It is understood that the full set of interfaces can be the collection of all interfaces provided by the trading system to the outside world. The full set of interfaces can include all external interaction points of the trading system that may be involved, such as the interface for querying product information, the interface for placing orders, and the interface for querying order status.

[0066] It should be understood that the interface information is various descriptive data about the interface in the transaction system, including, for example, the name of the interface, the input parameters of the interface, the channel of the interface, the functional description of the interface, etc., which is not limited in this embodiment.

[0067] In practice, when new or modified interfaces are added to a trading system, annotations are added to all interfaces before the application goes into production. Therefore, the interface monitoring device can traverse the entire trading system's interface code in real time. When it detects an interface with the target annotation, it extracts relevant information such as the interface name, parameters, and return value to obtain the interface information.

[0068] In a feasible implementation, step S10 of this embodiment may include the steps of: performing annotation scanning on the full interface of the transaction system through the message middleware to obtain the interface name and channel information corresponding to the full interface; constructing a corresponding full interface list based on the interface name and the channel information; collecting interface log data from the full interface at a preset time frequency; and using the full interface list and the interface log data as the interface information of the full interface.

[0069] It should be noted that message middleware can be a type of software that transmits messages between different software components or systems, providing an asynchronous communication mechanism so that interface monitoring equipment can perform annotation scanning on all interfaces of the trading system.

[0070] It's understood that an interface name can be a name that uniquely identifies an interface in a trading system. Channel information can be data related to the channel associated with the trading system interface, such as online webpages, mobile apps, and offline physical stores. This helps differentiate how the interface is used across different channels. Using interface names and channel information, a comprehensive interface list can be constructed, encompassing all interfaces in the trading system.

[0071] It should be understood that the preset time frequency can be a pre-set time interval in the interface monitoring device, which determines the period at which the interface monitoring device collects interface log data from all interfaces. For example, it can be set to collect once every hour, every day, or every week, which is not limited in this embodiment.

[0072] Interface log data can be data that records relevant information of the interface during operation, and may include the time when the interface is called, the identity information of the caller, input parameters, output results, abnormal data, etc.

[0073] In this embodiment, if Figure 2 As shown, Figure 2 A brief flow chart of the monitoring and management of the transaction system interface provided in Example 1 of the present application. At the beginning of interface monitoring, the interface monitoring device uses the message passing and coordination functions of the message middleware to scan and collect interface data of the transaction system, identify interfaces with specific annotations, parse out the interface name and channel information, and build a full interface list based on the acquired interface name and channel information. Afterwards, log data is collected (interface error, request log, etc.) for each interface in the full interface list according to a pre-set time frequency (such as every hour or every day). Finally, the constructed full interface list and the collected interface log data are integrated as the interface information of the full interface. By acquiring the constructed full interface list and collecting interface log data, the operating status of the interface can be monitored in real time, which facilitates the timely discovery and resolution of interface problems.

[0074] Step S20: labeling and classifying the entire interface according to the interface information to obtain corresponding classification labels, where the classification labels include multiple types of interface indicators.

[0075] It should be noted that the classification label is a mark used to identify the category to which the interface belongs during the labeling and classification process. For example, there may be performance labels (such as high response speed interface, low response speed interface), channel labels, risk level labels, etc., which are not limited in this embodiment.

[0076] It is understandable that the multiple types of interface indicators can be standards for measuring different aspects of interface characteristics, such as response time, throughput, request volume, error volume and time consumption, etc., and this embodiment does not limit this.

[0077] In practice, after obtaining the aforementioned interface information, the interface monitoring device can first analyze the interface name and channel information contained in the interface information. Based on pre-defined interface metric classification standards, such as response time, throughput, number of requests, number of errors, and time consumption, all interfaces are compared one by one. If a certain metric is met, the corresponding classification label is assigned, thus completing the labeling and classification.

[0078] In a feasible implementation, step S20 of this embodiment may include the steps of: determining the labeling dimensions of the full interface, the labeling dimensions including the channel dimension and the risk level dimension; classifying and labeling the full interface according to the channel dimension according to the full interface list to obtain an interface channel label, and the interface channel label is used to characterize the importance of the channel called by the interface; classifying and labeling the full interface according to the risk level dimension to obtain an interface risk label, and the interface risk label is used to characterize the degree of influence of the channel called by the interface; using the interface channel label and the interface risk label as classification labels for the full interface.

[0079] It should be noted that the interface channel label is an identifier obtained by classifying and labeling all interfaces according to the channel dimension. It reflects the importance of the channel called by the interface and is used to distinguish the priorities of interfaces related to different channels in system management to facilitate resource allocation.

[0080] It is understandable that the interface risk label is an identifier obtained by classifying and labeling all interfaces based on the risk level dimension. It reflects the degree of influence of the channel called by the interface and is used to help identify high-risk interfaces and take preventive measures in advance.

[0081] For example, to understand the relationship between interface channel labels and interface risk labels, refer to Figure 3 , Figure 3An example diagram of the channel dimension and risk level dimension provided for the first embodiment of the present application. Interface channels may include customer-facing channels, internal system channels, and channels called by other services of this system; for customer-facing channels, including corporate online banking, mobile banking, etc., these channels are usually directly facing customers. If an abnormality occurs in the interface, it will directly affect the customer experience, which has a greater impact. The default risk level is high; for internal system channels, including counter systems, management systems, etc., these channels are used for internal operations and management, and the risk level can be assessed as "medium"; for channels called by other services of this system, the risk level of such service calls can be assessed as "low". Make a comprehensive risk level judgment based on different channels.

[0082] In this implementation, the collected full list of interfaces can be categorized and filtered by interface name and channel. Labeling dimensions include channel and risk level. Channel identifies the upstream system that the interface calls, while risk level identifies the importance of the interface. By labeling interfaces based on channel and risk level, we can better understand the relationship between interfaces and channels and potential risks, facilitating the rational allocation of resources and improving system security.

[0083] Further, if Figure 2 As shown, during the interface management process, the above are the default labeling categories for interfaces. Customized labeling and classification can also be performed for interfaces. Based on the default labeling categories, labeling dimensions can be added. Interface labeling editing is provided, allowing for editing of interface labels individually or in batches, modifying the default labeling results, and adding personalized label settings. This can include functional scenarios, API market launches, and other such additions, which are not limited in this embodiment.

[0084] Furthermore, thresholds can be configured for the above-mentioned multiple interface indicators. Figure 4 , Figure 4 This is an example diagram of multiple types of interface indicators provided in Example 1 of this application. Considering the channel and importance of the interface, three types of interface indicators can be formulated: request volume, error volume, and time consumption. Each type of label (for example, label 1, label 2, label n) can be configured with different alarm threshold ranges (such as threshold 1, threshold 2, threshold n) for the three types of indicators. The thresholds can be included in the alarm judgment of the interface monitoring device to determine whether it is triggered.

[0085] Step S30: According to the multiple types of interface indicators, the interface information is compared with a preset alarm threshold to obtain a comparison result.

[0086] It should be noted that the preset alarm threshold can be a pre-set limit value used to determine whether the interface is in a normal state. For different interface indicators, corresponding alarm thresholds can be set. When a certain indicator value in the interface information exceeds or falls below this threshold, it may indicate that there is an abnormality in the interface. At this time, if Figure 2 As shown, the interface monitoring device can further monitor and warn of future situations:

[0087] (1) Alarm identification: The collected interface log data is compared with the preset alarm threshold configured above. When the threshold is exceeded, an abnormality mark is made and an alarm message is generated.

[0088] (2) Real-time push notifications. All alert information can be pushed to relevant developers in real time so that problems can be handled promptly. An indicator warning user subscription module can be provided. Each user can subscribe to the alert information of any type of indicator. Subscriptions can be made according to the dimension of indicator + tag, focusing on the anomalies that individuals need to pay attention to. When an abnormality exceeds the threshold in such an indicator, a real-time reminder will be issued. There are many modes for alert push, which can be connected through the communication module to trigger SMS sending, email sending, or office software message sending, etc. This embodiment does not limit this.

[0089] In the specific implementation, the interface monitoring device obtains the interface log data such as the interface error and request log of the trading system through real-time reception or periodic collection, and compares it with the preset alarm threshold according to the above-mentioned multiple types of interface indicators to obtain the comparison results.

[0090] In a feasible implementation, step S30 of this embodiment may include the steps of: determining multiple types of interface indicators of the classification label, the multiple types of interface indicators including request volume indicators, error volume indicators and time consumption indicators; summarizing the interface log data according to the request volume indicators, the error volume indicators and the time consumption indicators respectively, to obtain interface indicator data corresponding to the same type of labels in the same time period; judging whether the interface indicator data exceeds the preset alarm threshold; when the interface indicator data exceeds the preset alarm threshold, marking the interface log data corresponding to the preset alarm threshold as abnormal, obtaining abnormal indicators, and using the abnormal indicators as comparison results.

[0091] It's important to note that the request volume metric measures the number of times an interface is requested within a specific time period, reflecting the frequency of interface usage. The error volume metric is a statistical metric for the number of errors that occur within a specific time period, reflecting the stability and reliability of the interface. The time consumption metric measures the time it takes for the interface to process a single request, reflecting the processing efficiency of the interface.

[0092] When interface indicator data exceeds the preset alarm threshold, the corresponding interface log data can be specially marked to distinguish normal from abnormal data. At this time, the abnormal indicator can be obtained to clearly identify the abnormal aspects of the interface.

[0093] In this embodiment, after obtaining the above-mentioned interface log data, the log information within the time interval can be summarized at a certain time frequency, and the request volume, error volume, and processing time of each request of each interface can be summarized by interface. Then, according to the tag dimension, the interface indicator data corresponding to the same tag in the same time period can be summarized. When the interface indicator data of this type of tag exceeds the preset alarm threshold, it can be marked as abnormal to obtain an abnormal indicator. In this way, by preserving the interface for a certain period of time, real-time monitoring of the request volume, error volume, etc. can be achieved, allowing operation and maintenance personnel to quickly locate and resolve interface problems.

[0094] Step S40: When there are abnormal indicators in the comparison result, a preset intelligent problem assistant model is used to perform strategy matching on the abnormal indicators to obtain an interface optimization plan.

[0095] It's important to note that the Intelligent Problem Assistant model is a pre-built, intelligent analytical model designed to handle various interface-related issues. The Intelligent Problem Assistant model incorporates extensive knowledge and can analyze input anomaly indicators to identify the problem type and solution.

[0096] It is understood that an interface optimization plan is an improvement plan generated based on the results of policy matching to address interface anomalies. For example, it can improve interface performance, reduce error rates, and shorten service time, thereby restoring the interface to normal operation or enhancing its operational effectiveness.

[0097] In the specific implementation, after the user receives an abnormal alarm of the subscription indicator, he can jump to the interface monitoring device through the PC or mobile terminal to view the abnormality in detail. For each abnormal interface, the intelligent analysis assistant can be used to handle it, and automatically search for historical disposal solutions to provide matching interface optimization plans.

[0098] In another feasible implementation, step S40 of this embodiment may include the steps of: locating the full interface according to the abnormal indicator, determining the abnormal interface and the abnormal information of the abnormal interface; performing problem analysis on the abnormal information through a preset intelligent problem assistant model to obtain the abnormal factors of the abnormal interface; matching in a preset knowledge base according to the abnormal factors to obtain an interface optimization plan, the preset knowledge base including a business knowledge base, a solution knowledge base and a production event knowledge base; adjusting the abnormal interface according to the interface optimization plan.

[0099] It should be noted that the abnormal interface is the interface with the problem identified through the above location process. The abnormal information is information related to the abnormal interface that can describe the problem characteristics, such as the error content of the interface during a specific time period, the numerical range of the abnormal request volume, and the specific duration of excessive request processing time.

[0100] It is understandable that abnormal factors are the root cause of problems with abnormal interfaces, such as code logic errors, database connection problems, network transmission failures and other possible reasons.

[0101] It should be understood that the business knowledge base contains business-related knowledge, such as business processes, business rules, and business data structures. The solution knowledge base stores various historical alarm issues. The production event knowledge base records past events that occurred in the production environment and their handling. These knowledge bases together constitute the preset knowledge base, providing a rich knowledge base for finding interface optimization plans.

[0102] In this embodiment, since each type of abnormal indicator can be located at a specific abnormal interface, the intelligent problem analysis assistant can be used to analyze the abnormal information and obtain the abnormal factors. Figure 5 , Figure 5 The flowchart of the model anomaly analysis provided in the first embodiment of the present application is as follows: the interface monitoring device refines and summarizes the abnormal information based on the abnormal factors of the abnormal information through the intelligent problem assistant model, extracts business knowledge, and searches the business knowledge base to obtain the business knowledge background; at the same time, the abnormal information is refined and summarized through the intelligent problem assistant model, and the abnormal handling knowledge base (i.e., the production event knowledge base) is searched to obtain the handling plan. By analyzing the abnormal information through the above-mentioned model, a matching solution result can be output. Therefore, the abnormal interface can be adjusted according to the optimization plan, which can quickly solve the abnormal problems of the interface and improve the stability of the interface.

[0103] Further, if Figure 2 As shown, after completing the above intelligent analysis, you can also proceed to problem resolution. The intelligent assistant's analysis results can be used to determine the cause of the problem and, with the help of the provided solution, implement appropriate problem resolution, thus achieving a closed-loop problem resolution. If no matching results are found, you can enter the cause and solution in the problem resolution interface to supplement and continuously optimize the model. If the analyzed alarm is reasonable, you can adjust the threshold with a single click, continuously optimizing the preset alarm threshold to improve alarm accuracy.

[0104] In the technical solution provided by this embodiment, when new or modified interfaces are added to the trading system, all interfaces are annotated before the application goes into production. Therefore, the interface monitoring device can traverse the entire trading system's interface code in real time. When an interface with the target annotation is detected, it extracts relevant information such as the interface name, parameters, and return value to obtain the interface information. The interface information can then be analyzed for the interface name and channel information. Based on pre-set interface metric classification standards, such as response time, throughput, number of requests, number of errors, and time consumption, all interfaces are compared one by one. Interfaces that meet certain metrics are assigned corresponding classification labels, thus completing the labeling and classification. Interface log data, such as interface error and request logs, is then acquired through the aforementioned real-time reception or scheduled collection. Based on these multiple interface metrics, these data are compared against preset alarm thresholds to obtain comparison results. After a user receives an abnormality alert for a subscribed metric, they can jump to the interface monitoring device via a PC or mobile device to view the abnormality in detail. For each abnormal interface, an intelligent analysis assistant can be used to process it, automatically searching historical resolution plans and providing matching interface optimization plans. Since this embodiment labels and classifies all interfaces of the trading system and sets multiple types of interface indicators, multiple types of interface indicators can be compared with preset alarm thresholds when monitoring interface information in real time to achieve interface abnormality monitoring and automatic optimization, avoiding the situation where log screening takes a lot of time, thereby improving the stability and reliability of the trading system.

[0105] Based on the above embodiment 1 of this application, the second embodiment of this application is proposed. In the second embodiment of this application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be repeated hereafter. Figure 6 , Figure 6 This is a flow chart of the second embodiment of the interface monitoring method of this application.

[0106] Before step S10, this example also includes steps S01 to S06:

[0107] Step S01: Construct a business knowledge base, a solution knowledge base and a production event knowledge base. The business knowledge base includes product business knowledge corresponding to the transaction system, the solution knowledge base includes historical alarm problems, and the production event knowledge base includes solutions corresponding to production problem events.

[0108] Step S02: extracting the contents of the business knowledge base, the solution knowledge base and the production event knowledge base in a sharding manner to obtain business knowledge, abnormal information and solutions.

[0109] It should be noted that content extraction in a fragmented manner means dividing the knowledge base content into different parts according to predetermined division rules (for example, according to business type, problem type, time range, etc.) for the business knowledge base, solution knowledge base and production event knowledge base, and then extracting valuable information (such as business knowledge, exception information and solutions) from each part, so that key information can be obtained more efficiently from a large amount of knowledge base content.

[0110] Business knowledge is core business-related information extracted from the business knowledge base, including knowledge about business processes, business logic, and business functions. Exception information is a description of possible exceptions extracted from the solution knowledge base and production event knowledge base, including the type and manifestation of the exception. Solutions are the proposed solutions to these exceptions, such as adjusting parameters, fixing code vulnerabilities, and replacing hardware.

[0111] Step S03: Divide the business knowledge, the abnormal information and the solution into a training data set and a test data set.

[0112] It's understandable that the training dataset is a subset of data selected from the business knowledge, anomaly information, and solutions data sources, used to train the pre-set AI model. The test dataset is also separated from the business knowledge, anomaly information, and solutions data sources, but is independent of the training dataset. Its purpose is to evaluate the model's performance after training it with the training dataset.

[0113] Step S04: Train the preset AI model according to the training data set to obtain an initial assistant model.

[0114] It is understandable that after using the sharding method to refine and summarize business knowledge, abnormal information and solutions, the training data set can be input into the AI ​​model for training according to the three parts of business background, problem cause and solution.

[0115] Step S05: Input the test data set into the initial assistant model, and determine whether the accuracy of the output result reaches a preset threshold.

[0116] Step S06: If the preset threshold is not reached, return to the step of training the preset AI model according to the training data set until the accuracy reaches the preset threshold, and use the initial assistant model corresponding to the preset threshold as the intelligent question assistant model.

[0117] It should be noted that an AI model is an artificial intelligence model with a specific structure (such as a neural network structure) and initial parameter settings. By inputting a training data set into the preset AI model, the model will adjust its parameters based on the characteristics and patterns in the data to obtain the initial assistant model.

[0118] It should be understood that the accuracy of the output results refers to the proportion of the results output by the initial assistant model when processing the test dataset that match the actual correct results in the test dataset. For example, if the test dataset has 100 data points and the model gives correct results for 80 of them, the accuracy of the output results is 80%. This metric intuitively reflects the performance level of the model.

[0119] It's understood that the preset threshold is a standard value set before model training and evaluation. For example, in some application scenarios, a model accuracy rate of 90% may be required. If the accuracy of the initial assistant model's output results is lower than this preset threshold, it means that the model's performance does not meet the requirements and further training is required. If it reaches or exceeds the preset threshold, the model meets the requirements and can be used in subsequent applications.

[0120] In the technical solution provided in this embodiment, a business knowledge base is first constructed, encompassing business knowledge related to trading system products; a solution knowledge base containing historical alarm issues; and a production event knowledge base containing solutions to production problem events. These three knowledge bases are then subjected to content extraction in a segmented manner: business knowledge is extracted from the business knowledge base, exception information is extracted from the solution knowledge base, and solutions are derived from the production event knowledge base. The business knowledge, exception information, and solutions are then divided into training and test data sets. A pre-set AI model is trained using the training data set to generate an initial assistant model. The test data set is then input into the initial assistant model, and the accuracy of the output is calculated and compared with a preset threshold. If the accuracy does not reach the preset threshold, the pre-set AI model is trained again using the training data set, with adjustments to model parameters and other operations. This cycle repeats until the accuracy reaches the preset threshold, at which point the corresponding initial assistant model becomes the intelligent problem assistant model. This enables the model to more effectively handle various interface issues in practical applications, thereby improving the efficiency and accuracy of trading system interface processing.

[0121] It should be noted that the above examples are only used to understand this application and do not constitute a limitation on the interface monitoring method of this application. More simple transformations based on this technical concept are all within the scope of protection of this application.

[0122] This application also provides an interface monitoring device, please refer to Figure 7 , Figure 7This is a schematic diagram of the module structure of the interface monitoring device according to an embodiment of the present application; the interface monitoring device includes:

[0123] Annotation scanning module 701, used to perform annotation scanning on all interfaces of the trading system to obtain corresponding interface information;

[0124] A labeling and classification module 702 is configured to label and classify all interfaces according to the interface information to obtain corresponding classification labels, wherein the classification labels include multiple types of interface indicators;

[0125] The threshold alarm module 703 is used to compare the interface information with the preset alarm threshold according to the multiple types of interface indicators to obtain a comparison result;

[0126] The interface optimization module 704 is used to, when there are abnormal indicators in the comparison results, perform strategy matching on the abnormal indicators through a preset intelligent problem assistant model to obtain an interface optimization plan.

[0127] As an embodiment, the annotation scanning module 701 is also used to perform annotation scanning on the full interface of the transaction system through the message middleware to obtain the interface name and channel information corresponding to the full interface; construct a corresponding full interface list based on the interface name and the channel information; collect interface log data from the full interface at a preset time frequency; and use the full interface list and the interface log data as the interface information of the full interface.

[0128] As an implementation method, the labeling classification module 702 is also used to determine the labeling dimensions of the full interface, and the labeling dimensions include channel dimensions and risk level dimensions; according to the full interface list, the full interface is classified and labeled according to the channel dimension to obtain an interface channel label, and the interface channel label is used to characterize the importance of the channel called by the interface; the full interface is classified and labeled according to the risk level dimension to obtain an interface risk label, and the interface risk label is used to characterize the degree of influence of the channel called by the interface; the interface channel label and the interface risk label are used as classification labels for the full interface.

[0129] As an embodiment, the threshold alarm module 703 is also used to determine multiple types of interface indicators of the classification label, and the multiple types of interface indicators include request volume indicators, error volume indicators and time consumption indicators; the interface log data are summarized according to the request volume indicators, the error volume indicators and the time consumption indicators respectively to obtain interface indicator data corresponding to the same type of labels in the same time period; it is judged whether the interface indicator data exceeds the preset alarm threshold; when the interface indicator data exceeds the preset alarm threshold, the interface log data corresponding to the preset alarm threshold is marked as abnormal to obtain an abnormal indicator, and the abnormal indicator is used as a comparison result.

[0130] As an implementation method, the interface optimization module 704 is also used to locate the full interface according to the abnormal indicators, determine the abnormal interface and the abnormal information of the abnormal interface; perform problem analysis on the abnormal information through a preset intelligent problem assistant model to obtain the abnormal factors of the abnormal interface; match the abnormal factors in the preset knowledge base to obtain an interface optimization plan, and the preset knowledge base includes a business knowledge base, a solution knowledge base and a production event knowledge base; adjust the abnormal interface according to the interface optimization plan.

[0131] As an embodiment, the interface monitoring device is also used to construct a business knowledge base, a solution knowledge base and a production event knowledge base, wherein the business knowledge base includes product business knowledge corresponding to the transaction system, the solution knowledge base includes historical alarm problems, and the production event knowledge base includes solutions corresponding to production problem events; the business knowledge base, the solution knowledge base and the production event knowledge base are extracted in a sharding manner to obtain business knowledge, abnormal information and solutions; the business knowledge, the abnormal information and the solutions are divided into a training data set and a test data set; the preset AI model is trained according to the training data set to obtain an initial assistant model; the test data set is input into the initial assistant model, and it is determined whether the accuracy of the output result reaches a preset threshold; if the preset threshold is not reached, the step of training the preset AI model according to the training data set is returned to execute until the accuracy reaches the preset threshold, and the initial assistant model corresponding to the preset threshold is used as the intelligent problem assistant model.

[0132] Other embodiments or specific implementations of the interface monitoring device of the present application can refer to the above-mentioned method embodiments and will not be repeated here.

[0133] The interface monitoring device provided in this application, which utilizes the interface monitoring method of the aforementioned embodiment, can address the technical problem that existing transaction system monitoring focuses primarily on error reporting in the system dimension, making it difficult to address diverse interface requirements at the interface level. Compared to the prior art, the beneficial effects of the interface monitoring device provided in this application are the same as those of the interface monitoring method provided in the aforementioned embodiment, and the other technical features of the interface monitoring device are the same as those disclosed in the aforementioned embodiment method, and are not further described here.

[0134] The present application provides an interface monitoring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the interface monitoring method in the above-mentioned embodiment one.

[0135] Reference below Figure 8 , Figure 8 The following is a schematic diagram of the device structure of the hardware operating environment involved in the interface monitoring method in the embodiment of the present application, which shows a schematic diagram of the structure of the interface monitoring device suitable for implementing the embodiment of the present application. The interface monitoring device in the embodiment of the present application can include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The interface monitoring device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0136] like Figure 8As shown, the interface monitoring device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of the interface monitoring device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and a communication device 1009. Communication device 1009 can allow the interface monitoring device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows an interface monitoring device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0137] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0138] The interface monitoring device provided in this application, which utilizes the interface monitoring method of the aforementioned embodiment, can address the technical issue that existing transaction system monitoring focuses primarily on system-level error reporting, making it difficult to address diverse interface requirements at the interface level. Compared to the prior art, the beneficial effects of the interface monitoring device provided in this application are the same as those of the interface monitoring method provided in the aforementioned embodiment, and the other technical features of the interface monitoring device are the same as those disclosed in the aforementioned embodiment, and are not further elaborated here.

[0139] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0140] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0141] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, and the computer-readable program instructions are used to execute the interface monitoring method in the above embodiment.

[0142] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0143] The computer-readable storage medium may be included in the interface monitoring device, or may exist independently without being assembled into the interface monitoring device.

[0144] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the interface monitoring device, the interface monitoring device: performs annotation scanning on the full interface of the transaction system to obtain corresponding interface information; labels and classifies the full interface according to the interface information to obtain corresponding classification labels, and the classification labels include multiple types of interface indicators; compares the interface information with preset alarm thresholds based on the multiple types of interface indicators to obtain comparison results; when there are abnormal indicators in the comparison results, the abnormal indicators are strategy matched through a preset intelligent problem assistant model to obtain an interface optimization plan.

[0145] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0147] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0148] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., a computer program) for executing the above-mentioned interface monitoring method. This computer-readable storage medium can solve the technical problem that the monitoring of existing trading systems focuses on error reporting in the system dimension and is difficult to cope with diverse interface requirements at the interface level. Compared with the existing technology, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the interface monitoring method provided in the above-mentioned embodiment, and will not be repeated here.

[0149] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned interface monitoring method when executed by a processor.

[0150] The computer program product provided in this application can address the technical problem that existing trading system monitoring focuses primarily on system-level error reporting, making it difficult to address diverse interface requirements at the interface level. Compared to existing technologies, the beneficial effects of the computer program product provided in this application are the same as those of the interface monitoring method provided in the aforementioned embodiments, and are not further elaborated here.

[0151] The above description is only part of the embodiments of the present application and does not limit the scope of protection of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the scope of protection of the present application.

Claims

1. An interface monitoring method, characterized in that: The method comprises: Scan all interfaces of the trading system for annotations to obtain corresponding interface information; Classify the full set of interfaces according to the interface information to obtain corresponding classification labels, where the classification labels include multiple types of interface indicators; Comparing the interface information with preset alarm thresholds according to the multiple types of interface indicators to obtain a comparison result; When there are abnormal indicators in the comparison results, a preset intelligent problem assistant model is used to perform strategy matching on the abnormal indicators to obtain an interface optimization plan.

2. The method according to claim 1, wherein The step of performing annotation scanning on all interfaces of the trading system to obtain corresponding interface information includes: Scan the full set of interfaces of the trading system through the message middleware to obtain the interface name and channel information corresponding to the full set of interfaces; Construct a corresponding full interface list based on the interface name and the channel information; Collecting interface log data from all interfaces at a preset time frequency; The full interface list and the interface log data are used as interface information of the full interface.

3. The method according to claim 2, wherein The step of labeling and classifying the full set of interfaces according to the interface information to obtain corresponding classification labels includes: Determining the labeling dimensions of the full interface, the labeling dimensions including the channel dimension and the risk level dimension; According to the full interface list, the full interface is classified and labeled according to the channel dimension to obtain an interface channel label, where the interface channel label is used to represent the importance of the channel called by the interface; Classify and label all interfaces according to the risk level dimension to obtain interface risk labels, where the interface risk labels are used to characterize the degree of influence of the channel called by the interface; The interface channel label and the interface risk label are used as classification labels for the full interface.

4. The method according to claim 3, wherein The step of comparing the interface information with a preset alarm threshold according to the multiple types of interface indicators to obtain a comparison result includes: Determine multiple types of interface indicators of the classification label, wherein the multiple types of interface indicators include a request volume indicator, an error volume indicator, and a time consumption indicator; Summarize the interface log data according to the request volume index, the error volume index, and the time consumption index to obtain interface index data corresponding to the same type of tags in the same time period; Determine whether the interface indicator data exceeds a preset alarm threshold; When the interface indicator data exceeds the preset alarm threshold, the interface log data corresponding to the preset alarm threshold is marked as abnormal to obtain an abnormal indicator, and the abnormal indicator is used as a comparison result.

5. The method according to claim 4, wherein When there are abnormal indicators in the comparison results, the step of performing strategy matching on the abnormal indicators through a preset intelligent problem assistant model to obtain an interface optimization plan includes: Locating the full interface according to the abnormal indicator, and determining the abnormal interface and abnormal information of the abnormal interface; Performing problem analysis on the abnormal information through a preset intelligent problem assistant model to obtain the abnormal factors of the abnormal interface; According to the abnormal factors, matching is performed in a preset knowledge base to obtain an interface optimization plan, wherein the preset knowledge base includes a business knowledge base, a solution knowledge base, and a production event knowledge base; The abnormal interface is adjusted according to the interface optimization plan.

6. The method according to any one of claims 1 to 5, characterized in that Before the step of performing annotation scanning on all interfaces of the trading system to obtain corresponding interface information, the method further includes: Build a business knowledge base, a solution knowledge base, and a production event knowledge base. The business knowledge base includes product business knowledge corresponding to the transaction system, the solution knowledge base includes historical alarm issues, and the production event knowledge base includes solutions corresponding to production problem events. Extracting content from the business knowledge base, the solution knowledge base, and the production event knowledge base in a sharded manner to obtain business knowledge, exception information, and solutions; Dividing the business knowledge, the abnormal information, and the solution into a training data set and a test data set; Training a preset AI model based on the training data set to obtain an initial assistant model; Input the test data set into the initial assistant model and determine whether the accuracy of the output result reaches a preset threshold; If the preset threshold is not reached, return to the step of training the preset AI model according to the training data set until the accuracy reaches the preset threshold, and use the initial assistant model corresponding to the preset threshold as the intelligent question assistant model.

7. An interface monitoring device, characterized in that: The device comprises: The annotation scanning module is used to perform annotation scanning on all interfaces of the trading system to obtain the corresponding interface information; A labeling and classification module is used to label and classify the full set of interfaces according to the interface information to obtain corresponding classification labels, where the classification labels include multiple types of interface indicators; A threshold alarm module, configured to compare the interface information with a preset alarm threshold according to the multiple types of interface indicators to obtain a comparison result; The interface optimization module is used to, when there are abnormal indicators in the comparison results, perform strategy matching on the abnormal indicators through a preset intelligent problem assistant model to obtain an interface optimization plan.

8. An interface monitoring device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the interface monitoring method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the interface monitoring method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the interface monitoring method according to any one of claims 1 to 6 are implemented.

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