Server interface detection method and system, server, equipment, medium and product

Abstract

The invention discloses a server interface detection method and system, a server, equipment, a medium and a product, and relates to the technical field of computers. Receiving a pressure parameter and an interface signal parameter at the server interface, and judging whether the pressure parameter and / or the interface signal parameter meet a preset fault judgment rule or not; if the pressure parameter and / or the interface signal parameter meet a preset fault judgment rule, acquiring a fault type of the server interface by retrieving a preset fault type database according to the pressure parameter and / or the interface signal parameter; rapid and accurate abnormity identification is realized; and sending the interface identifier of the server interface, the fault type and the target maintenance suggestion corresponding to the fault type to a human-computer interaction interface. And the operation and maintenance personnel can carry out maintenance quickly. The technical problem that the existing interface troubleshooting efficiency is low is solved, and the troubleshooting efficiency and the response speed are improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a server interface detection method, system, server, device, medium and product. Background Art

[0002] Servers are responsible for storing, computing, and transmitting large amounts of data at high speeds. The interconnection between servers and external components relies on a variety of interfaces, which not only transmit electrical signals but also handle protocol negotiation, error checking, and state management. As server integration and interface speeds continue to increase, server interface anomalies (such as minor loose contacts, signal integrity degradation, or protocol layer negotiation anomalies) can cause throughput degradation, link resets, and even service interruptions.

[0003] Related technologies typically rely on baseboard management controller logs, operating system kernel errors, or application alarms to locate interface faults. When an error occurs in the log, the log entry typically only provides an error code or a general exception identifier, making it difficult to distinguish between mechanical contact, signal integrity degradation, or protocol layer compatibility issues. Operations and maintenance personnel still need to use offline tools to troubleshoot each issue, resulting in inefficient troubleshooting. Therefore, a server interface detection method is urgently needed to address the current technical problem of low interface troubleshooting efficiency. Summary of the Invention

[0004] The present application provides a server interface detection method, system, server, device, medium and product to at least solve the problem of low efficiency in server interface fault troubleshooting in related technologies.

[0005] This application provides a server interface detection method, including:

[0006] Receiving pressure parameters and interface signal parameters at the server interface, and determining whether the pressure parameters and / or interface signal parameters meet preset fault judgment rules;

[0007] If the pressure parameters and / or interface signal parameters meet the preset fault judgment rules, the fault type of the server interface is obtained by searching the preset fault type database based on the pressure parameters and / or interface signal parameters;

[0008] The interface identifier of the server interface, the fault type and the target maintenance suggestion corresponding to the fault type are sent to the human-computer interaction interface.

[0009] The present application provides a server interface detection system, comprising: a parameter acquisition module and a processing module;

[0010] A parameter acquisition module is used to obtain pressure parameters and interface signal parameters at the server interface and send the pressure parameters and interface signal parameters to the processing module;

[0011] a processing module, configured to receive pressure parameters and interface signal parameters at a server interface, and determine whether the pressure parameters and / or interface signal parameters meet preset fault determination rules;

[0012] The processing module is also used to obtain the fault type of the server interface by searching the preset fault type database based on the pressure parameters and / or interface signal parameters if the pressure parameters and / or interface signal parameters meet the preset fault judgment rules, and send the interface identifier, fault type and target maintenance suggestion corresponding to the fault type of the server interface to the human-computer interaction interface.

[0013] The present application also provides a server, including the above-mentioned detection system.

[0014] The present application also provides a server interface detection device, comprising:

[0015] A data receiving unit, configured to receive pressure parameters and interface signal parameters at a server interface, and determine whether the pressure parameters and / or interface signal parameters meet preset fault determination rules;

[0016] a fault identification unit configured to obtain the fault type of the server interface by searching a preset fault type database based on the pressure parameters and / or interface signal parameters if the pressure parameters and / or interface signal parameters meet the preset fault judgment rules;

[0017] The fault alarm unit is used to send the interface identifier of the server interface, the fault type and the target maintenance suggestion corresponding to the fault type to the human-computer interaction interface.

[0018] The present application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any one of the above detection methods when executing the computer program.

[0019] The present application also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned detection methods are implemented.

[0020] The present application also provides a computer program product, including a computer program, which implements the steps of any of the above detection methods when executed by a processor.

[0021] This application achieves synchronous perception of the mechanical contact state and signal transmission state of the interface by simultaneously acquiring the pressure parameters and interface signal parameters at the server interface; then, the pressure parameters and / or interface signal parameters are judged according to the preset fault judgment rules, thereby achieving rapid and certain abnormality discovery. Compared with the passive recording method relying on logs, it shortens the time from the occurrence of the abnormality to the abnormality identification, and reduces the dependence on manual experience and offline tools. By retrieving and outputting the fault type of the server interface, the accuracy and repeatability of the fault type acquisition are improved, and the interface identifier, fault type and target maintenance suggestion corresponding to the fault type of the server interface are sent to the human-computer interaction interface, so that the operation and maintenance personnel can quickly perform maintenance and reduce the manual processing time required for interface fault troubleshooting. In summary, through synchronous collection, rule judgment, type retrieval and suggestion issuance, the rapid identification, accurate classification and maintenance guidance of interface abnormalities are achieved, which can solve the technical problem of low efficiency of existing interface fault troubleshooting, improve troubleshooting efficiency and response speed, and reduce server downtime and labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 A schematic diagram of the structure of the server interface detection system provided in an embodiment of the present application;

[0024] Figure 2 A flow chart of a server interface detection method provided in an embodiment of the present application;

[0025] Figure 3 A flow chart of a method for obtaining pressure characteristics provided in an embodiment of the present application;

[0026] Figure 4 A flowchart of a method for marking abnormal data provided in an embodiment of the present application;

[0027] Figure 5 A flow chart of the method for interface health analysis provided in an embodiment of the present application;

[0028] Figure 6 A schematic diagram of the structure of the detection device provided in an embodiment of the present application;

[0029] Figure 7 This is a schematic diagram of the structure of the electronic device provided in this application.

[0030] Reference numerals:

[0031] 1-Detection system; 11-Parameter acquisition module; 12-Processing module; 111-Interface pressure detection unit; 112-Interface information detection unit. DETAILED DESCRIPTION

[0032] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0033] It should be noted that, in the description of this application, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. The terms "first," "second," etc., in this application are used to distinguish similar objects, and are not used to describe a particular order or sequence.

[0034] Related technologies primarily rely on baseboard management controller logs, operating system kernel errors, or application layer alarms to locate interface anomalies, using offline diagnostic tools to perform cyclic self-tests or loopback tests on the interfaces. These methods can only record information such as error codes, link resets, and online and offline times. When determining unstable physical contact or decreased signal integrity, external measurement equipment and manual experience are required. This results in delayed detection, fragmented evidence, and difficulty integrating it, which not only affects judgment accuracy, but also prolongs the locating and handling cycles, resulting in inefficient interface troubleshooting.

[0035] Based on the above technical problems and needs, the inventive concept of this application aims to integrate the mechanical contact state and signal transmission state of the server interface into the fault detection link. Specifically, the pressure parameters and interface signal parameters are obtained for the same interface entity, and the pressure parameters and / or interface signal parameters are judged using preset fault judgment rules. After determining that there is an abnormality, the fault type of the server interface is obtained through a preset fault type database based on the pressure parameters and / or interface signal parameters as retrieval conditions, thereby achieving rapid identification and accurate classification of interface abnormalities. At the same time, the server interface identifier, fault type and target maintenance suggestion corresponding to the fault type are sent to the human-computer interaction interface together to achieve fault identification and timely feedback after classification. Through the methods of data collection, fault judgment, type retrieval and issuance of maintenance suggestions, the link from the occurrence of the abnormality to positioning and disposal is compressed, thereby improving the accuracy and timeliness of interface abnormality detection.

[0036] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0037] Figure 1 A schematic diagram of the server interface detection system provided in the embodiment of the present application is shown in FIG. Figure 1 As shown, the server interface detection system 1 includes a parameter acquisition module 11 and a processing module 12. The parameter acquisition module 11 is used to acquire the pressure parameters and interface signal parameters at the server interface, and send the pressure parameters and interface signal parameters to the processing module 12. By acquiring the pressure parameters related to mechanical contact and the interface signal parameters related to signal transmission, the synchronous perception of the physical contact state and the signal transmission state of the server interface is achieved, ensuring the comprehensiveness of the data used for subsequent judgments. Among them, the pressure parameters are data that can quantify the interface contact load and micro-vibration conditions; the interface signal parameters are data that can quantify the interface signal transmission quality, communication logic behavior and connection status. It should be noted here that the parameter acquisition module 11 can be implemented in various forms such as sensor acquisition circuits or firmware reading channels. The specific form is not limited here, as long as comparable pressure parameters and interface signal parameters can be formed.

[0038] Processing module 12 is used to receive pressure parameters and interface signal parameters at the server interface and determine whether the pressure parameters and / or interface signal parameters meet preset fault diagnosis rules. Pressure parameters reflect the interface's stress, looseness, and vibration, while interface signal parameters reflect the link's transmission quality and communication behavior. By receiving these two types of data, omissions and deviations caused by comparison and manual information splicing are reduced, facilitating the complete acquisition and utilization of abnormal clues. Processing module 12 determines the pressure parameters and / or interface signal parameters based on preset fault diagnosis rules. The preset fault diagnosis rules are parameterized sets that determine whether the parameters are within the normal operating range. This enables immediate identification of abnormalities and reduces the waiting time associated with relying on log playback and manual comparison. It should be further clarified that the fault diagnosis rules can be a combination of a static threshold range and drift tolerance, or a statistical baseline and deviation metric that is updated over time, as long as they provide a clear indication of whether the pressure parameters and / or interface signal parameters meet the fault trigger conditions.

[0039] The processing module 12 is also used to obtain the fault type of the server interface by searching the preset fault type database based on the pressure parameters and / or interface signal parameters if the pressure parameters and / or interface signal parameters meet the preset fault judgment rules, and send the interface identifier, fault type and target maintenance suggestion corresponding to the fault type of the server interface to the human-computer interaction interface.

[0040] In this embodiment, when the fault judgment rules are met, the processing module 12 uses the current pressure parameters and / or interface signal parameters as search conditions to query the preset fault type database to obtain the fault type of the server interface. The database maps common parameter patterns to fault types, including mechanical contact problems, signal transmission degradation, protocol compatibility anomalies, and related hardware failures. By searching from parameters to types, the type of fault can be quickly determined, reducing repeated attempts and multiple rounds of troubleshooting, and improving the accuracy and timeliness of interface fault location. After obtaining the fault type, an alarm message containing the server interface identifier, the fault type, and the target maintenance recommendation corresponding to the fault type is generated and sent to the human-computer interaction interface, facilitating operation and maintenance personnel to perform further verification and maintenance according to the target maintenance recommendation, thereby improving the efficiency of server interface fault troubleshooting. Among them, the server interface identifier is identification information that can uniquely indicate the interface entity being tested. For example, it includes elements such as the device sequence, motherboard slot number, and port index to ensure the accuracy and traceability of positioning. The target maintenance suggestions are disposal items that are associated one-to-one or many-to-one with the fault types, and are derived from a suggestion table associated with a preset fault type database or other accessible data storage. The human-computer interaction interface is a display and interaction terminal for operation and maintenance personnel, including but not limited to a server management interface, a remote management console, or an integrated alarm system.

[0041] In summary, this detection system 1 realizes parallel monitoring of mechanical contact and signal transmission of the server interface by simultaneously covering pressure parameters and interface signal parameters; triggers retrieval by adopting preset fault judgment rules, and confirms the fault type of the current server interface according to the fault type database, thereby improving the accuracy of fault type acquisition; then, the server interface identification, fault type and target maintenance suggestions corresponding to the fault type are sent to the human-computer interaction interface, so that operation and maintenance personnel can perform maintenance quickly, improve troubleshooting efficiency and response speed, and reduce server downtime and labor costs.

[0042] In one embodiment, the parameter acquisition module 11 includes an interface pressure detection unit 111 and an interface information detection unit 112; wherein, the pressure detection end of the interface pressure detection unit 111 (such as a pressure sensor) is set at the server interface, and the parameter acquisition module 11 is specifically used to collect the pressure parameters at the server interface through the interface pressure detection unit 111, and send the pressure parameters to the processing module 12. This structural arrangement enables the interface pressure detection unit 111 to perceive the tiny load changes and local vibration responses during the connection process in real time at the actual contact point of the interface. Since the server interface may cause contact pressure fluctuations or micro-vibrations due to loosening, oxidation or structural fatigue during long-term operation, the interface pressure detection unit 111 can effectively obtain pressure parameters that reflect the reliability of mechanical contact, such as contact pressure value, pressure fluctuation frequency, etc., thereby providing real-time physical basis for the stability of the interface.

[0043] The interface information detection unit 112 is connected to the signal transmission line of the server interface. Through the interface information detection unit 112, at least one of the physical layer parameters, protocol layer parameters and interface status parameters of the server interface is collected, and at least one of the physical layer parameters, protocol layer parameters and interface status parameters is sent to the processing module 12. Specifically, the physical layer parameters usually involve voltage and current amplitude, eye diagram opening, jitter range, signal integrity, etc., which are used to reflect the transmission quality of the signal on the physical channel; the protocol layer parameters may include data packet format, frame check code, number of error retransmissions, handshake response delay, etc., which are used to reflect the processing status of the communication protocol stack; the interface status parameters may involve link connection status, online and offline event counts, link reset frequency, current port working mode, etc. The collection of these signal side parameters can realize dynamic monitoring of the operating status of the server interface during the information interaction process. In summary, through the interface pressure detection unit 111 and the interface information detection unit 112, the parameter acquisition module 11 can comprehensively monitor the server interface from both physical and signal levels. This multi-source data fusion perception method not only enhances the accuracy of fault identification, but also improves the response speed of interface anomaly detection, providing basic data support for fault judgment and maintenance suggestion generation.

[0044] In a specific embodiment, the interface pressure detection unit 111 may adopt a structure based on a piezoelectric sensor or a strain gauge sensor. Specifically, a micro piezoelectric sensor assembly is provided in the connection area of ​​the server interface. The assembly is directly attached to the metal terminal or support portion of the interface slot, and can sense the contact load changes and micro-vibration amplitude generated by the interface during plugging or operation. The sensor signal is connected to the data processing component through an analog acquisition circuit to realize real-time acquisition and digital conversion of the pressure value. At the same time, the high sensitivity of the piezoelectric material ensures an effective response to tiny mechanical disturbances, thereby being able to monitor interface mechanical anomalies caused by loose contact, metal fatigue or uneven stress, and then generate pressure parameters.

[0045] The interface information detection unit 112 obtains the interface's physical layer signals, electrical parameters, and data transmission status through the signal transmission channel connected to the server interface. For example, in a Peripheral Component Interconnect Express (PCIe) or Serial Advanced Technology Attachment (SATA) interface, a high-speed differential signal sampling component can be configured to extract physical layer information such as signal eye diagrams, clock offsets, and level amplitudes. A protocol parsing module can also be configured to parse protocol layer data such as link negotiation, handshake processes, error codes, and retransmission information. Status monitoring records real-time status parameters such as interface online and offline times, abnormal interruptions, and link stability, forming interface operation information. It should be noted that this embodiment is merely illustrative; both the interface pressure detection unit 111 and the interface information detection unit 112 can utilize related technologies capable of acquiring pressure and signal information, and this is not specifically limited here.

[0046] Figure 2 Schematic diagram of the process of server interface detection method provided in the embodiment of the present application. Figure 2 As shown, including:

[0047] S21, receiving pressure parameters and interface signal parameters at the server interface, and determining whether the pressure parameters and / or interface signal parameters meet preset fault determination rules.

[0048] In this embodiment, the pressure parameters and interface signal parameters at the server interface sent by the parameter acquisition module are received. The pressure parameters are used to reflect the physical contact state of the interface during operation, such as plug-in contact load, micro-vibration trend, and contact stability fluctuations that may be caused by looseness or structural stress changes; the interface signal parameters are used to reflect the transmission state of the interface signal, including but not limited to the physical layer communication quality in the interface link, protocol layer error information and handshake behavior, and whether the interface is currently in a working state such as connection, reconnection or disconnection. After receiving these parameters, the pressure parameters and interface signal parameters are analyzed and compared with the preset fault judgment rules. The fault judgment rules include multiple judgment conditions for identifying the occurrence of abnormal conditions, such as: whether there are signs of persistent or sudden contact instability, whether there is an abnormal signal level, a decrease in bit rate, too many handshake failures, an excessive reconnection frequency, etc. When any one or a class of parameters meets these preset judgment conditions, it is considered that the server interface has an abnormality. By receiving interface-related parameters and setting multi-dimensional judgment logic, early identification of abnormal server interface status is achieved. Without relying on link communication failure or manual re-inspection, the location of the problem interface can be quickly determined, improving the automation and real-time performance of interface abnormality diagnosis.

[0049] S22: If the pressure parameter and / or the interface signal parameter meets the preset fault judgment rule, the fault type of the server interface is obtained by searching a preset fault type database according to the pressure parameter and / or the interface signal parameter.

[0050] In this embodiment, when the pressure parameters and / or interface signal parameters of the server interface are determined to meet preset fault diagnosis rules, the server interface is considered to be in an abnormal state. To further identify the specific fault type, the server interface's current pressure parameters and / or interface signal parameters are used as a search basis to access a local or remote fault type database. This database pre-records information about different types of interface faults and their corresponding characteristic parameters, enabling rapid comparison and fault type determination. Specifically, the received parameter information is matched with the fault data stored in the fault type database to determine which known fault type the current interface abnormal state best matches. For example, if the pressure parameters exhibit unstable contact pressure and the interface signal parameters exhibit frequent physical layer fluctuations, this may indicate a poor contact fault. If the protocol layer parameters exhibit high-frequency error responses while the physical layer parameters are normal, this may indicate a protocol negotiation failure fault. This embodiment enables automated classification and identification of server interface anomalies without relying on manual analysis or external measurement tools, thereby improving the accuracy and efficiency of fault determination. It can output clear fault type information in a structured manner, provide a basis for subsequent maintenance decisions, and further reduce the probability of human interference and misjudgment in interface fault troubleshooting.

[0051] S23, sending the interface identifier of the server interface, the fault type, and the target maintenance suggestion corresponding to the fault type to the human-computer interaction interface.

[0052] In this embodiment, after the fault type of the server interface is identified, in order to facilitate the operation and maintenance personnel to grasp the interface status in a timely manner and take effective disposal measures, the information related to the interface abnormality is sent to the human-computer interaction interface. Specifically, it includes: first, the server interface identifier, which is used to clarify the physical location and logical number of the faulty interface in the whole machine or the whole cabinet to ensure that the troubleshooting process is targeted; second, the fault type, which is used to intuitively show the user the type of abnormality occurring in the current interface, so as to facilitate the analysis of the scope and severity of the fault impact; third, the target maintenance suggestion corresponding to the fault type, for example, the operation suggestion retrieved from the preset maintenance suggestion database according to the fault type, usually including inspection steps, replacement suggestions or structural adjustment plans. In summary, through this embodiment, real-time visual display of abnormal information can be achieved. On the one hand, the information interaction method reduces the dependence on manual experience and improves the standardization and executability of interface operation and maintenance; on the other hand, the early screening and push of target maintenance suggestions helps to shorten the interface fault processing cycle and further improve the overall operational stability and response efficiency.

[0053] In one embodiment, the determination of whether the pressure parameter satisfies a preset fault judgment rule in step S21 is further described herein, including:

[0054] S2111, obtaining at least one pressure feature corresponding to the pressure parameter. If any pressure feature is not within its corresponding preset pressure threshold range, the pressure parameter meets the preset fault judgment rule.

[0055] In this embodiment, after receiving pressure parameters from the parameter acquisition module, at least one pressure feature that reflects the interface contact state is extracted from the pressure parameters. A pressure feature is a statistical or dynamic indicator that quantifies the mechanical contact quality and stability of the interface. It typically includes the mean pressure value, fluctuation amplitude, instantaneous gradient, micro-vibration frequency component, and loading / unloading trends. The mean pressure value reflects whether the interface contact load is within a reasonable range, the fluctuation amplitude indicates the stability of the interface contact, and high-frequency micro-vibrations may indicate problems such as plugging instability or resonance interference. After extracting these pressure features, they are compared with corresponding preset pressure threshold ranges for judgment. Each feature has independent upper and lower thresholds, which are determined based on a large amount of measured data and can effectively distinguish between normal and abnormal contact states. If any pressure feature exceeds its corresponding threshold range, the current pressure parameter is considered abnormal, and the pressure parameter is determined to meet the fault judgment rule, thereby triggering the subsequent fault identification and alarm process. In summary, this embodiment not only enhances the sensitivity of fault detection to minor contact anomalies, but also reduces the problem of false positives or false negatives caused by a single indicator in actual operation, thereby improving the robustness and accuracy of the judgment results. At the same time, the above method can detect possible mechanical contact degradation trends earlier, which helps to intervene and maintain in advance, thereby improving the reliability of interface operation and the overall stability of the server.

[0056] Figure 3 A flow chart of a method for obtaining pressure characteristics provided in an embodiment of the present application. After receiving the pressure parameters and interface signal parameters at the server interface, obtaining the pressure characteristics is further described here. Based on the above embodiment, Figure 3 Shown, including:

[0057] S31, according to a preset sampling period, time-sorting the pressure parameters corresponding to the server interface to obtain a pressure sampling data set;

[0058] S32: According to a preset sliding time window and a preset pressure feature extraction rule, feature extraction is performed on the pressure sampling data set to obtain at least one pressure feature.

[0059] In this embodiment, in order to more comprehensively and dynamically reflect the mechanical contact status of the server interface, a time-series sampling and sliding window analysis mechanism is used for the collected pressure parameters. In actual operation, the pressure status at the server interface is continuously collected and a pressure data sequence arranged in chronological order is formed according to a preset sampling period. The sampling period can be flexibly adjusted according to the server operating status and the frequency of interface use. Common settings include collecting data once per second or more frequently to ensure the continuity and timeliness of the data. The raw data obtained by sampling is sorted by timestamp to form a pressure sampling data set for subsequent time series feature analysis. Based on this data set, the sampled data is locally segmented using a moving window method according to the set sliding time window. Each sliding window covers a continuous section of pressure sampling data. Its length is usually selected based on experience, for example, set to data within the past 5 seconds or 10 seconds to balance feature stability and response speed. Within each window, the data is analyzed according to the preset pressure feature extraction rules to extract at least one pressure feature. The pressure feature extraction rules can include various statistical analysis methods, such as the mean, standard deviation, maximum and minimum differences (fluctuation amplitude) of pressure values ​​within a window, the first-order derivative of pressure changes (indicating trend), and frequency domain analysis features (such as the frequency spectrum of micro-vibrations). These pressure features can demonstrate the temporal contact stability and load uniformity of the server interface. This sliding window analysis and feature extraction rule approach enables fine-grained dynamic monitoring of pressure conditions. This makes pressure feature extraction real-time and continuous, improving the accuracy and timeliness of fault diagnosis.

[0060] In a specific embodiment, the interface signal parameter includes at least one of a physical layer parameter, a protocol layer parameter, and an interface status parameter.

[0061] In this embodiment, interface signal parameters are data sets used to characterize the electrical connection and communication status of the server interface, encompassing communication information at multiple levels. Specifically, interface signal parameters include at least one of physical layer parameters, protocol layer parameters, and interface status parameters. Physical layer parameters reflect the electrical properties of the server interface and typically include link voltage, current, signal strength, eye opening, jitter amplitude, and so on. These parameters can characterize the integrity of signal transmission and the stability of the physical connection, and are acquired, for example, by high-speed sampling components or eye diagram analysis modules. Protocol layer parameters primarily reflect the status of the data link during communication, typically including the number of retransmissions, packet loss rate, and CRC error codes. These parameters are typically automatically generated by the driver or protocol stack during operation and can reflect communication anomalies caused by protocol incompatibility, signal interference, or degraded link performance. Interface status parameters provide an abstract description of the overall connection status of the interface, such as link activation, connection speed, duplex mode, and the frequency of interface on- and off-line switching.

[0062] By comprehensively collecting the above three types of parameters, the operating status of the interface can be comprehensively analyzed from different levels and dimensions. For example, physical layer anomalies may indicate loose plugging or excessive contact resistance, protocol layer anomalies may indicate communication interference or device negotiation failure, and frequent changes in interface status may reflect poor interface stability or configuration issues. Therefore, according to actual needs, one or more of the above parameters can be flexibly selected as the interface signal parameter collection object and sent to the processing module for unified analysis and processing. This not only enhances the ability to perceive fault modes, but also lays the foundation for subsequent feature extraction and fault determination, improving the accuracy and applicability of interface anomaly detection.

[0063] In a specific embodiment, after receiving the pressure parameter and the interface signal parameter at the server interface, the method further includes:

[0064] S2101, according to a preset sampling period, time-sorting the physical layer parameters, protocol layer parameters, and interface status parameters of the server interface, and obtaining a physical layer sampling data set, a protocol layer sampling data set, and an interface status sampling data set;

[0065] S2102, according to the preset sliding time window, respectively use the preset physical layer feature extraction rules, protocol layer feature extraction rules and interface status feature extraction rules to perform feature extraction on the physical layer sampling data set, the protocol layer sampling data set and the interface status sampling data set to obtain at least one physical layer feature, at least one protocol layer feature and at least one interface status feature.

[0066] In this embodiment, to improve the granularity and accuracy of server interface signal anomaly detection, the physical layer parameters, protocol layer parameters, and interface status parameters included in the interface signal parameters are processed separately. Specifically, the three types of parameters are received according to a preset sampling period and sorted based on time stamps to form a physical layer sampling dataset, a protocol layer sampling dataset, and an interface status sampling dataset. The physical layer sampling dataset typically includes raw signal sample values, which reflect the physical transmission quality of the link. For example, it may include parameters such as signal strength (such as voltage and current), bit error rate, noise level, and channel occupancy, collected at a fixed sampling frequency. These parameters can indicate whether there is jitter, distortion, or connection anomalies at the link level. The protocol layer parameter sampling dataset includes handshake packet response latency, session packet loss rate, and number of retransmissions. The interface status sampling dataset may include information such as the number of interface online and offline handovers, bandwidth, link activation state change frequency, and number of negotiation failures.

[0067] Then, according to the window length and window step of the sliding time window, slide along the physical layer sampling data set, protocol layer sampling data set, and interface status sampling data set in sequence. In the time period corresponding to each sliding window, the preset feature extraction rules are respectively used to extract features. Specifically, the physical layer sampling data set is processed using the physical layer feature extraction rules to obtain at least one physical layer feature including signal quality, bit error rate, voltage fluctuation, etc.; the protocol layer sampling data set is processed using the protocol layer feature extraction rules to obtain at least one protocol layer feature including connection status, handshake process abnormality, etc.; the interface status sampling data set is processed using the interface status feature extraction rules to obtain at least one interface status feature including device connection status, port response delay, etc. This embodiment realizes the acquisition of multi-dimensional features by sliding the time window and extracting features in layers according to the feature extraction rules of each layer, thereby improving the accuracy and real-time performance of interface fault identification.

[0068] Specifically, physical layer characteristics, protocol layer characteristics, and interface status characteristics are obtained, including:

[0069] S21021, extracting at least one of jitter mean square value, bit error rate, or eye opening as a physical layer feature using a preset physical layer feature extraction rule based on the physical layer sampling data set in the current time window;

[0070] S21022: Extract, from the protocol layer sampling data set within the current time window, at least one of the error code occurrence count, the protocol frame retransmission ratio, or the handshake failure rate as a protocol layer feature using a preset protocol layer feature extraction rule;

[0071] S21023: Based on the interface status sampling data set in the current time window, a preset interface status feature extraction rule is used to extract at least one of the link width variation range, the power consumption fluctuation range, or the bandwidth utilization fluctuation range as the interface status feature.

[0072] In this embodiment, to more comprehensively analyze the signal quality, protocol stability, and connection status changes of the server interface during operation, physical layer features, protocol layer features, and interface status features are extracted from the various types of raw data collected within the current sliding time window. Feature extraction rules are operational rules that convert the sampled data within the sliding time window into comparable and determinable features.

[0073] Physical layer sampling data sets (such as jitter time series, bit error count series, eye diagram sampling data, level amplitude sampling data, and clock amplitude sampling data) are processed using physical layer feature extraction rules to extract features such as signal quality fluctuation, duration of continuous distortion, and instantaneous voltage drop to determine whether the interface has poor contact or signal integrity degradation. Specifically, this includes calculating the mean square jitter value based on the jitter time series, the bit error rate per unit time based on the bit error count series, the eye opening degree for eye diagram sampling data, the high and low level amplitudes for level amplitude sampling data, and the clock stability for clock amplitude sampling data.

[0074] The protocol layer sampling data set (such as protocol error code counts, protocol frame retransmission counts, link state machine transition counts, cumulative uncorrected error counts, and cumulative error correction code counts) is processed through protocol layer feature extraction rules to extract protocol layer features such as the number of error code occurrences per unit time, the frame retransmission ratio per unit time, the number of link state machine transitions per unit time, the uncorrected error rate, and the error correction success rate. These features are used to analyze whether there are protocol compatibility issues or device communication failures. The number of failures during the handshake process can also be extracted to calculate the handshake failure rate, which is used to measure the reliability of the connection establishment phase.

[0075] Interface status sampling data sets (such as link rate, link width, power consumption status, power management level, and dynamic bandwidth utilization) are processed using interface status feature extraction rules to extract features such as link rate offset per unit time, link width variation, power consumption fluctuation, number of power management level changes, and bandwidth utilization fluctuation, thereby identifying instances of decreased interface stability. By extracting and processing these features separately, diagnostic information at various signal levels can be retained, enabling more accurate server interface fault diagnosis. It should be noted that the collected data and extracted features can be flexibly configured by those skilled in the art based on their needs.

[0076] Next, in step S21, determining whether the interface signal parameters meet the preset fault judgment rules is further described here. Based on the above embodiment, the method includes:

[0077] S2121: Obtain at least one physical layer feature corresponding to the physical layer parameter. If any physical layer feature is outside its corresponding physical layer threshold range, the interface signal parameter satisfies a preset fault judgment rule.

[0078] S2122: Obtain at least one protocol layer feature corresponding to the protocol layer parameter. If any protocol layer feature is outside the corresponding protocol layer threshold range, the interface signal parameter satisfies a preset fault judgment rule.

[0079] S2123: Obtain at least one interface status feature corresponding to the interface status parameter. If any interface status feature is not within its corresponding status threshold range, the interface signal parameter satisfies a preset fault judgment rule.

[0080] In this embodiment, at least one physical layer feature corresponding to the physical layer parameter is obtained. If the physical layer feature is not within the corresponding physical layer threshold range, the interface signal parameter meets the preset fault judgment rule. For example, in a certain acquisition cycle, the bit error rate extracted is 2.5×10 -5 , the corresponding preset bit error rate threshold range is 0 to 1.0×10 -5 By comparing the current value with the range, it is determined that the bit error rate exceeds the upper limit, the physical layer feature is judged to be abnormal, and the interface signal parameters are determined to meet the fault judgment rules. Regarding protocol layer features, such as the retransmission rate of the Transmission Control Protocol (TCP) connection, the average handshake delay, or the confirmation character delay time. If the retransmission rate of the Transmission Control Protocol transmission in a certain period is 7.3%, and the preset upper limit of the retransmission rate threshold of the protocol layer is 5%, it can be concluded that the current retransmission behavior is abnormal, and there is a trend of interruption or packet loss in the protocol communication, and it is determined that the fault judgment rules are met. Interface status characteristics include the number of link disconnections per unit time, the frequency of interface online and offline, etc. For example, the number of link online and offline switches detected in a 30-second sliding window is 11 times, and the set interface status stability threshold is within 3 times. The current data exceeds this range, so the interface status parameter is considered abnormal.

[0081] In general, based on the item-by-item comparison of the extracted characteristic values ​​with the corresponding preset thresholds, when any characteristic value is not within its corresponding reasonable interval, it can be determined that the interface signal parameters meet the preset fault judgment rules. This judgment method has strong practicality and accuracy, and can achieve early identification of fault problems in the server interface in multiple dimensions of physical, protocol and connection status, and provide a quantitative basis for subsequent fault location and maintenance recommendations. This embodiment does not rely on subjective judgment, and realizes automated analysis through objective data comparison, thereby improving the efficiency and accuracy of interface anomaly identification.

[0082] Figure 4 The flowchart of the method for marking abnormal data provided in an embodiment of the present application, based on the above embodiment, includes the following steps after the pressure parameters and / or interface signal parameters meet the preset fault judgment rules:

[0083] S41, marking a pressure feature that is not within a preset pressure threshold range as an abnormal pressure feature;

[0084] S42: Mark the physical layer features that are not within the preset physical layer threshold range, the protocol layer features that are not within the preset protocol layer threshold range, and the interface status features that are not within the preset status threshold range as abnormal signal features.

[0085] In this embodiment, after acquiring multiple types of features, a threshold comparison is performed on each type of feature, and the features are marked as abnormal features accordingly. Specifically, the multiple pressure features are judged one by one to see if their values ​​are within the corresponding preset pressure threshold range. For example, if the value of a pressure feature is 1.2N, and the normal threshold range of this feature is 0.3N to 1.0N, then this pressure feature does not meet the threshold requirement and is marked as an abnormal pressure feature. For physical layer features, protocol layer features, and interface status features, the corresponding threshold range is also used to perform the judgment. For example, if the bit error rate of a physical layer feature is 2.8%, and the preset upper limit of the physical layer bit error rate is 1.0%, then this feature is marked as an abnormal physical layer feature. All physical layer features, protocol layer features, and interface status features that are not within the corresponding preset threshold range are uniformly marked as abnormal signal features. Furthermore, when each type of abnormal feature is marked, it is accompanied by the feature category to which it belongs, the threshold boundary exceeded or below, and the abnormality degree indicator. Through this embodiment, abnormal status annotation of server interface related features is achieved, and an abnormal feature set is constructed, providing a structured and quantifiable basis for subsequent fault identification, type judgment and maintenance suggestions.

[0086] In one embodiment, the acquisition of the fault type of the server interface in step S22 is further described herein. Based on the above embodiment, the following steps are included:

[0087] S221, obtaining abnormal pressure characteristics and / or abnormal signal characteristics based on the pressure parameters and / or interface signal parameters corresponding to the server interface, and constructing a search feature group corresponding to the server interface based on the abnormal pressure characteristics and / or abnormal signal characteristics;

[0088] S222: According to the search feature group, retrieve and obtain the fault type corresponding to the server interface from a preset fault type database.

[0089] In this embodiment, the pressure parameters and interface signal parameters acquired at the server interface are processed to extract abnormal pressure features and / or abnormal signal features, which serve as the basis for subsequent fault identification. Abnormal pressure features typically include contact load fluctuations exceeding a preset threshold, abnormal micro-vibration frequencies, or pressure variation trends that do not conform to a preset pattern. Abnormal signal features, on the other hand, include characteristic data at the physical, protocol, and status layers that exceeds normal ranges, such as signal strength below a set lower limit, excessive protocol response latency, or frequent link status fluctuations. Abnormal pressure features and abnormal signal features are combined in a preset format to form a retrieval feature group. This retrieval feature group describes the abnormal behavior exhibited by the current server interface. By constructing a structured, fault-oriented feature set, accurate input conditions can be formed, ensuring accurate comparison with classified fault types in the database. Subsequently, using this retrieval feature group as a query condition, a preset fault type database is accessed. The database pre-stores interface fault cases and their corresponding abnormal feature patterns. By matching the feature items in the current retrieval feature group, the most closely or completely matching historical fault types can be selected. Finally, the determined server interface fault type is output, which realizes the fusion modeling and type identification of multi-source interface abnormal data, and improves the accuracy of fault judgment and response efficiency.

[0090] In one embodiment, an implementation of step S23 is provided herein. Based on the above embodiment, the implementation includes:

[0091] S231, searching a preset maintenance suggestion database based on the interface identifier and fault type of the server interface to determine a set of maintenance suggestion items;

[0092] S232, based on the priority of the maintenance suggestion item, filter and obtain the maintenance suggestion item with the highest priority from the maintenance suggestion item set as the target maintenance suggestion;

[0093] S233, generating an alarm message carrying an interface identifier, a fault type, and a target maintenance suggestion, and sending the alarm message to a human-computer interaction interface through a preset communication interface.

[0094] In this embodiment, after obtaining the fault type of the server interface, to further improve the efficiency and accuracy of the fault response, the server interface's interface identifier (i.e., determining the server interface type) is used as a search criterion to search a preset maintenance suggestion database. Interface types may include Peripheral Component Interconnect Express (PCIe), memory interface, and Serial Advanced Technology Attachment (SATA), while fault types may include "poor contact," "protocol negotiation failure," and "frequent link fluctuations." Based on these two criteria, a composite search is performed to obtain a set of maintenance suggestion entries that match the current scenario, with each entry corresponding to a solution for a specific fault scenario.

[0095] To improve execution efficiency, after obtaining a set of maintenance recommendation items, they are further filtered based on the priority field contained in each item. The priority field is set when the maintenance recommendation database is constructed and reflects the recommended order for each item during processing, typically expressed as a numerical grade or weighted score. The maintenance recommendation item with the highest priority is selected from the set as the target maintenance recommendation for the current server interface. For example, for a fault type of poor interface contact, if three maintenance recommendations exist: reseating the interface, replacing the interface card slot, and inspecting the motherboard solder joints, reseating the interface will be prioritized as the target maintenance recommendation. Finally, the server interface identification information, the corresponding fault type, and the determined target maintenance recommendation are packaged into an alarm message. This alarm message includes the interface identifier (such as the device ID and interface number) where the anomaly occurred, the fault classification (such as link failure, protocol anomaly, etc.), and the recommended action. This alarm message is pushed in real time to a human-computer interaction interface via a pre-set communication interface for operation and maintenance personnel to view and execute. This embodiment enables the automatic generation and push of maintenance recommendations based on the fault context, improving operation and maintenance response speed and addressing the issue of inefficient interface anomaly troubleshooting.

[0096] Based on the above embodiments, for obtaining the fault type, in addition to obtaining the fault type by querying the database, another implementation method is provided herein, including: if the pressure parameters and / or interface signal parameters meet the preset fault judgment rules, then according to the pressure parameters and interface signal parameters, the fault type of the server interface is obtained through a pre-trained fault type detection model.

[0097] In this embodiment, a pre-trained fault type detection model based on machine learning is used to identify current data. During the training phase, the model uses a large amount of historical interface fault data. This data sample contains multi-dimensional features such as pressure change patterns, signal disturbance characteristics, protocol error statistics, and connection status fluctuations. Through supervised training, a mapping relationship between features and fault types is established, resulting in a strong ability to distinguish fault types. Specifically, the pressure parameters and interface signal parameters collected from the current server interface are input into the pre-trained model (or abnormal pressure characteristics and abnormal signal characteristics are input). The model first normalizes the input data and performs feature mapping conversion, mapping it into a vector representation recognizable within the model structure. The model then sequentially calculates the matching degree for each typical fault type and ultimately outputs the fault type label that best matches the current features. For example, if the input pressure features show frequent instantaneous load fluctuations, and the interface signal parameters exhibit a high cyclic redundancy check (CRC) error rate at the physical layer or an abnormal increase in the number of protocol layer negotiations, the model will identify this set of features as a poor contact type interface fault. It can be seen that inputting the collected multi-source parameters into a fully trained fault type detection model realizes the automatic identification of server interface fault types, which not only improves the accuracy of identification but also shortens the response time of fault type identification.

[0098] Specifically, the fault type detection model employs a lightweight deep neural network model based on multimodal feature fusion to identify server interface fault types. This model takes abnormal pressure and signal features as input, comprehensively considers the contact state and signal transmission state of the server interface, and utilizes a neural network to perform fault classification analysis. Abnormal pressure features include indicators such as the mean contact load fluctuation and micro-vibration kurtosis, while abnormal signal features include signal amplitude changes at the physical layer, error code frequency at the protocol layer, and connection stability parameters at the state layer. The model performs channel encoding on pressure and signal features separately, and each feature type is enhanced through a separate fully connected encoding network to ensure a unified vector representation for each feature. The two encoded vectors are then fed into a fusion layer, which employs an attention mechanism to weight abnormal features from different sources, highlighting fault characteristics and enhancing the model's ability to distinguish between faults. The fusion results are fed into the fault classification layer, which consists of two fully connected networks and outputs the corresponding fault type label.

[0099] The fault type detection model undergoes supervised training using labeled server interface anomaly samples. The training data includes historical fault data collected from actual server operating environments and various typical anomaly samples injected through a simulated environment, ensuring the model's strong generalization capabilities. After model training, it is deployed in the server's processing module to perform real-time fault classification and judgment for each data anomaly event. This model improves the accuracy of fault identification and the automation of processing, addressing the poor real-time performance and high error rates associated with traditional approaches that rely on log analysis and manual judgment. It should be noted that determining the server interface fault type using a pre-trained fault type detection model is a complementary approach to obtaining the fault type from a database. The database is based on actual, identified fault scenarios and has strong practical significance. Therefore, using a database to determine the fault type is preferred. If the fault type obtained from the fault type database matches the pressure parameters and / or interface signal parameters below a preset matching threshold, the fault type detection model can be used to determine the fault type. Those skilled in the art can flexibly adjust the settings based on their needs; these examples are merely illustrative.

[0100] Figure 5 This is a flow chart of the interface health analysis method provided in the embodiment of the present application. Figure 5 Shown, including:

[0101] S51, obtaining interface pressure parameters, physical layer parameters, protocol layer parameters, and interface status parameters of the server interface within a preset time window;

[0102] S52, based on the interface pressure parameters, physical layer parameters, protocol layer parameters, and interface status parameters, respectively, using pressure feature extraction rules, physical layer feature extraction rules, protocol layer feature extraction rules, and interface status feature extraction rules to obtain pressure stability features, interface signal fluctuation features, protocol error code distribution features, and connection status change frequency features through feature extraction;

[0103] S53, based on the pressure stability characteristics, interface signal fluctuation characteristics, protocol error code distribution characteristics, and connection status change frequency characteristics, a health calculation rule is used to generate a health score for each server interface;

[0104] S54, when the health score of the server interface is lower than the preset health score threshold, an early warning notification is issued and sent to the human-computer interaction interface.

[0105] In this embodiment, within a preset time window, the target server interface is continuously collected and recorded for its corresponding interface pressure parameters, physical layer parameters, protocol layer parameters and interface status parameters to form a time series data set to ensure that subsequent analysis has a sufficient contextual basis. Next, the above four types of data are structured according to the feature extraction rules applicable to different types of data. For example, for pressure parameters, the pressure feature extraction rules are used to calculate the pressure mean, standard deviation and its fluctuation frequency within the time window, and finally the pressure stability characteristics representing its stability are obtained; for physical layer parameters, the signal amplitude change, the slope of the bit error rate curve, etc. are analyzed to extract the interface signal fluctuation characteristics; for protocol layer parameters, the distribution density and renegotiation frequency of various error codes are statistically analyzed to generate protocol error code distribution characteristics; for interface status parameters, the frequency characteristics of connection status changes are extracted by calculating the frequency of connection interruptions, the number of online and offline switching, etc.

[0106] After feature extraction is completed, the four features are input into the health scoring model according to the preset health calculation rules, and a weighted summation method is used to generate the health score of the server interface in the current time window, for example, between 0 and 100. In the scoring rules, the weights of each feature can be flexibly configured according to the server model, interface type or user needs to improve the adaptability and accuracy of the health analysis. When the health score of a certain interface is detected to be lower than the set health score threshold (for example, lower than 60 points), it is determined that it may be in an abnormal or degraded state. At this time, the early warning mechanism will be automatically triggered to construct information including interface identification, scoring details and abnormal characteristics, and display it to the operation and maintenance personnel through the preset human-computer interaction interface. By setting up a multi-dimensional feature fusion and scoring mechanism, a comprehensive quantitative analysis and evaluation of the server interface status is achieved, effectively improving the timeliness of abnormal discovery and the foresight of operation and maintenance response.

[0107] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method.

[0108] An embodiment of the present application also provides a server, including the above-mentioned detection system.

[0109] The server includes a parameter acquisition module and a processing module; the parameter acquisition module is used to acquire the pressure parameters and interface signal parameters at the server interface, and send the pressure parameters and interface signal parameters to the processing module; the processing module is used to receive the pressure parameters and interface signal parameters at the server interface, and determine whether the pressure parameters and / or interface signal parameters meet the preset fault judgment rules; and is also used to obtain the fault type of the server interface by searching a preset fault type database based on the pressure parameters and / or interface signal parameters if the pressure parameters and / or interface signal parameters meet the preset fault judgment rules, and send the interface identifier, fault type and target maintenance suggestion corresponding to the fault type of the server interface to the human-computer interaction interface.

[0110] Figure 6 This is a schematic diagram of the structure of the detection device provided in the embodiment of the present application. Figure 6 As shown, an embodiment of the present application further provides a server interface detection device, the detection device 6 comprising:

[0111] The data receiving unit 61 is used to receive the pressure parameters and interface signal parameters at the server interface and determine whether the pressure parameters and / or interface signal parameters meet the preset fault judgment rules;

[0112] The fault identification unit 62 is configured to obtain the fault type of the server interface by searching a preset fault type database based on the pressure parameters and / or interface signal parameters if the pressure parameters and / or interface signal parameters meet the preset fault judgment rules;

[0113] The fault alarm unit 63 is configured to send the interface identifier of the server interface, the fault type, and the target maintenance suggestion corresponding to the fault type to the human-computer interaction interface.

[0114] For the description of the features in the embodiment corresponding to the detection device 6, reference can be made to the relevant description of the embodiment corresponding to the detection method, which will not be repeated here.

[0115] Figure 7 This is a schematic diagram of the structure of the electronic device provided in this application. Figure 7 As shown, the electronic device 7 provided in this embodiment includes: at least one processor 71 and a memory 72. Optionally, the electronic device 7 further includes a communication component 73. The processor 71, the memory 72 and the communication component 73 are connected via a bus 74.

[0116] During the specific implementation process, at least one processor 71 executes the computer-executable instructions stored in the memory 72, so that the at least one processor 71 executes the above-mentioned detection method embodiment.

[0117] The specific implementation process of the processor 71 can be found in the above-mentioned method embodiment. Its implementation principle and technical effects are similar, and will not be repeated here in this embodiment.

[0118] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the application may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0119] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0120] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0121] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above detection method embodiments when run.

[0122] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0123] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above detection method embodiments are implemented.

[0124] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above-mentioned detection method embodiments are implemented.

[0125] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0126] The above is a detailed introduction to the server interface detection method, system, server, device, medium and product provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only intended to help understand the method and core ideas of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A server interface detection method, characterized in that: include: receiving pressure parameters and interface signal parameters at the server interface, and determining whether the pressure parameters and / or the interface signal parameters meet preset fault judgment rules; If the pressure parameter and / or the interface signal parameter meets the preset fault judgment rule, the fault type of the server interface is obtained by searching a preset fault type database according to the pressure parameter and / or the interface signal parameter; The interface identifier of the server interface, the fault type, and the target maintenance suggestion corresponding to the fault type are sent to a human-computer interaction interface.

2. The detection method according to claim 1, wherein Determining whether the pressure parameter satisfies a preset fault judgment rule includes: At least one pressure feature corresponding to the pressure parameter is obtained. If any of the pressure features is not within its corresponding preset pressure threshold range, the pressure parameter meets the preset fault judgment rule.

3. The detection method according to claim 2, characterized in that After receiving the pressure parameter and the interface signal parameter at the server interface, the method further includes: According to a preset sampling period, the pressure parameters corresponding to the server interface are time-sorted to obtain a pressure sampling data set; According to a preset sliding time window and a preset pressure feature extraction rule, feature extraction is performed on the pressure sampling data set to obtain at least one pressure feature.

4. The detection method according to claim 2, characterized in that The interface signal parameters include at least one of physical layer parameters, protocol layer parameters and interface status parameters.

5. The detection method according to claim 4, characterized in that After receiving the pressure parameter and the interface signal parameter at the server interface, the method further includes: According to the preset sampling period, the physical layer parameters, protocol layer parameters and interface status parameters of the server interface are time-sorted respectively to obtain the physical layer sampling data set, the protocol layer sampling data set and the interface status sampling data set; According to a preset sliding time window, respectively using a preset physical layer feature extraction rule, a preset protocol layer feature extraction rule, and an interface state feature extraction rule, feature extraction is performed on the physical layer sampling data set, the protocol layer sampling data set, and the interface state sampling data set to obtain at least one physical layer feature, at least one protocol layer feature, and at least one interface state feature; Then, determining whether the interface signal parameters meet the preset fault judgment rules includes: obtaining at least one physical layer feature corresponding to the physical layer parameter; if any of the physical layer features is not within its corresponding physical layer threshold range, then the interface signal parameter satisfies a preset fault judgment rule; or obtaining at least one protocol layer feature corresponding to the protocol layer parameter; if any of the protocol layer features is not within the corresponding protocol layer threshold range, then the interface signal parameter satisfies a preset fault judgment rule; or, At least one interface status feature corresponding to the interface status parameter is obtained. If any of the interface status features is not within a corresponding status threshold range, the interface signal parameter satisfies a preset fault judgment rule.

6. The detection method according to claim 5, characterized in that After the pressure parameter and / or the interface signal parameter meets the preset fault judgment rule, the method further includes: Marking a pressure signature that is outside a preset pressure threshold range as an abnormal pressure signature; and / or Physical layer features that are not within a preset physical layer threshold range, protocol layer features that are not within a preset protocol layer threshold range, and interface status features that are not within a preset status threshold range are all marked as abnormal signal features.

7. The detection method according to claim 6, characterized in that The acquiring the fault type of the server interface by searching a preset fault type database according to the pressure parameter and / or the interface signal parameter includes: Acquire abnormal pressure characteristics and / or abnormal signal characteristics according to the pressure parameters and / or interface signal parameters corresponding to the server interface, and construct a retrieval feature group corresponding to the server interface according to the abnormal pressure characteristics and / or abnormal signal characteristics; According to the retrieval feature group, the fault type corresponding to the server interface is retrieved from the preset fault type database.

8. The detection method according to claim 1, wherein The sending the interface identifier of the server interface, the fault type, and the target maintenance suggestion corresponding to the fault type to the human-computer interaction interface includes: Searching a preset maintenance suggestion database according to the interface identifier and the fault type of the server interface to determine a set of maintenance suggestion entries; According to the priority of the maintenance suggestion item, selecting the maintenance suggestion item with the highest priority from the set of maintenance suggestion items as the target maintenance suggestion; Generate an alarm message carrying the interface identifier, the fault type and the target maintenance suggestion, and send the alarm message to the human-computer interaction interface through a preset communication interface.

9. The detection method according to claim 1, wherein Also includes: If the pressure parameter and / or the interface signal parameter meets the preset fault judgment rule, the fault type of the server interface is obtained according to the pressure parameter and the interface signal parameter through a pre-trained fault type detection model.

10. The detection method according to claim 5, characterized in that: The acquiring of at least one physical layer feature, at least one protocol layer feature, and at least one interface status feature includes: Extracting at least one of the jitter mean square value, the bit error rate, or the eye opening as a physical layer feature using a preset physical layer feature extraction rule based on a physical layer sampling data set within a current time window; According to the protocol layer sampling data set in the current time window, using the preset protocol layer feature extraction rules, at least one of the error code occurrence count, protocol frame retransmission ratio, or handshake failure rate is extracted as the protocol layer feature; According to the interface state sampling data set in the current time window, a preset interface state feature extraction rule is adopted to extract at least one of the link width variation range, power consumption fluctuation range or bandwidth utilization fluctuation range as the interface state feature.

11. A server interface detection system, characterized in that: include: Parameter acquisition module and processing module; The parameter acquisition module is used to acquire pressure parameters and interface signal parameters at the server interface, and send the pressure parameters and interface signal parameters to the processing module; The processing module is configured to receive a pressure parameter and an interface signal parameter at the server interface, and determine whether the pressure parameter and / or the interface signal parameter meet a preset fault judgment rule; The processing module is also used to obtain the fault type of the server interface by searching a preset fault type database based on the pressure parameters and / or the interface signal parameters if the pressure parameters and / or the interface signal parameters meet the preset fault judgment rules, and send the interface identifier of the server interface, the fault type and the target maintenance suggestion corresponding to the fault type to the human-computer interaction interface.

12. A server, characterized in that: Comprising the detection system as claimed in claim 11.

13. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the detection method according to any one of claims 1 to 10 when executing the computer program.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the detection method according to any one of claims 1 to 10 when executed by a processor.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the detection method according to any one of claims 1 to 10 are implemented.

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