A method, apparatus, device, storage medium, and computer program product for interconnection detection.

Abstract

The application discloses a connection detection method, which comprises the following steps: obtaining a set of communication link attribute parameters of a communication system to be analyzed for data connection line connection error; performing statistical processing on the set of communication link attribute parameters to obtain data statistical information; wherein each group of information in the data statistical information comprises local device attribute information and opposite end device attribute information; performing data connection line connection error detection based on the data statistical information to obtain a detection result; wherein the detection result is used at least for indicating a device with data connection line error and / or a port with data connection line error. The application also discloses a connection detection device, equipment, storage medium and computer program product.

Description

Technical Field

[0001] This application relates to the field of computer communication technology, and in particular to a connection detection method, apparatus, device, storage medium, and computer program product. Background Technology

[0002] With the rapid development of computer technology, computer applications are becoming increasingly widespread, and the demand for computer infrastructure is also rising to ensure computer services. For example, many computer applications have data service centers to provide internet communication services, using numerous physical cables to connect physical servers and network devices. The specific connection rules, i.e., the types of devices and port planning at both ends of the connection, often vary depending on the size of the data center and the network design requirements. Therefore, it is difficult to perform a full check of network physical connections during the hardware integration and construction phase. Connection problems are often only discovered after deployment and service launch, such as missing cables, incorrect device connections, or incorrect port connections, increasing the difficulty of maintenance and troubleshooting. Currently, there are two main methods for network connection testing: spot checks, which involve manually checking the physical connections of cables and port indicator lights, and full checks, which use a pre-prepared, comprehensive list of expected connection information as the basis for inspection.

[0003] However, the above-mentioned solutions suffer from low coverage and limited effectiveness due to manual inspection, which is confined to scenarios with a small number of connections. Full-scale inspection, on the other hand, requires a thorough understanding of detailed connection rules and a complete table of expected connections beforehand. Without this table, a full-scale inspection is impossible, resulting in a significant workload for data preparation. Furthermore, the inspection results are highly dependent on the accuracy of the data in the expected connection table, potentially leading to a low accuracy rate. Therefore, achieving a simple, fast, and practical connection inspection solution has become a pressing technical challenge.

[0004] Application content

[0005] To address the aforementioned technical problems, this application aims to provide a connection detection method, apparatus, system, storage medium, and computer program product. This solves the problem of low efficiency in detecting errors in connections between physical servers and network devices. A connection detection method is proposed that performs a full analysis of the communication data between the two devices whose connection needs to be checked for correctness, thereby determining whether an error exists in the connection and improving the efficiency of connection error checking.

[0006] The technical solution of this application is implemented as follows:

[0007] This application provides a connection detection method, the method comprising:

[0008] Obtain the communication link attribute parameter set of the communication system to be analyzed for data connection line connection errors;

[0009] The communication link attribute parameter set is statistically processed to obtain data statistics information; wherein, each set of information in the data statistics information includes local device attribute information and remote device attribute information;

[0010] Based on the statistical information, data connection cable connection errors are detected to obtain detection results; wherein, the detection results are at least used to indicate the device with a data connection cable error and / or the port with a data connection cable error.

[0011] In the above scheme, the step of performing statistical processing on the communication link attribute parameter set to obtain data statistical information includes:

[0012] The device to be analyzed is determined from the set of communication link attribute parameters; wherein, the device to be analyzed includes one or more devices;

[0013] From the devices to be analyzed, determine the target type of the devices to obtain m local devices; where m is an integer greater than or equal to 1;

[0014] From the set of communication link attribute parameters, the transmission attribute parameters of the peer device connected to each communication port of each local device are statistically analyzed to obtain the data statistics information.

[0015] In the above scheme, the step of obtaining the data statistics information by statistically analyzing the transmission attribute parameters of the peer device connected to each communication port of each local device from the communication link attribute parameter set includes:

[0016] From the set of communication link attribute parameters, statistical parameters of the peer device connected to each communication port of each local device are collected to obtain parameter statistics information.

[0017] The parameter statistics are preprocessed to obtain the data statistics.

[0018] In the above scheme, the data statistics information includes at least the following parameter types: local device identification information parameter, local device port parameter, local device Internet Protocol (IP) address parameter, local device port transmission rate parameter, peer device identification information parameter of peer device communicating with local device, peer device port parameter of peer device communicating with local device, and peer device IP address parameter of peer device communicating with local device. The data corresponding to each parameter type in the data statistics information is a quantized value of the same order of magnitude.

[0019] In the above scheme, the step of detecting data connection errors based on the data statistics to obtain the detection result includes:

[0020] Sort the data group corresponding to each local device in the data statistics information according to the preset sorting of the local device port to obtain the ordered dataset of each local device.

[0021] A set of data is determined from the ordered dataset of each local device to obtain a standard analysis data set;

[0022] Based on the standard analysis array, the target parameter type data in the ordered dataset of the corresponding local device is standardized to obtain the corresponding local device dataset to be analyzed.

[0023] For each local device, the dataset to be analyzed and the corresponding ordered dataset are subjected to data connection line connection error detection to obtain the detection result.

[0024] In the above scheme, the target parameter type includes at least: the local device port parameter and the peer device IP address. The standardization process, based on the standard analysis array, of the target parameter type data in the ordered dataset of the corresponding local device to obtain the corresponding local device's dataset to be analyzed includes:

[0025] Calculate a first difference between the data of each local device port parameter in each ordered dataset and the data of the local device port parameters in the standard analysis array;

[0026] Calculate a second difference between the data of each peer device IP address in each ordered dataset and the data of the peer device IP address in the standard analysis array;

[0027] Calculate the difference between each first difference and the corresponding second difference to obtain the corresponding third difference, thereby obtaining the dataset to be analyzed.

[0028] In the above scheme, the step of performing data connection error detection on the dataset to be analyzed and the corresponding ordered dataset of each local device to obtain the detection result includes:

[0029] Numerical anomaly analysis is performed on each dataset to be analyzed to determine the peer device corresponding to the abnormal value, and to obtain abnormal device information where there is a data connection error with the corresponding local device; wherein, the detection result includes the abnormal device information corresponding to each local device;

[0030] Anomaly analysis is performed on the values ​​corresponding to the port parameters of the peer device in each ordered dataset to obtain the port connection information of the local device where the data connection line is connected incorrectly; wherein, the detection result includes the port connection information corresponding to each local device.

[0031] In the above scheme, each group of data in the ordered dataset is stored using a data dictionary.

[0032] The method in the above scheme further includes:

[0033] Output the detection results.

[0034] This application provides a wiring detection device, the device comprising: an acquisition unit, a processing unit, and a detection unit; wherein:

[0035] The acquisition unit is used to acquire the communication link attribute parameter set of the communication system for which data connection line connection error analysis is to be performed.

[0036] The processing unit is used to perform statistical processing on the communication link attribute parameter set to obtain data statistical information; wherein, each set of information in the data statistical information includes local device attribute information and remote device attribute information;

[0037] The detection unit is used to detect data connection errors based on the data statistics information and obtain detection results; wherein, the detection results are used to indicate at least the devices with data connection errors and / or the ports with data connection errors.

[0038] This application provides an electronic device, the device comprising: a communication interface, a memory, a processor, and a communication bus; wherein:

[0039] Memory, used to store executable information;

[0040] The communication bus is used to implement communication connections between the communication interface, processor, and memory.

[0041] A processor is configured to execute a connection detection program stored in memory, implementing the steps of the connection detection method as described in any of the preceding claims.

[0042] This application provides a storage medium storing a connection detection program, which, when executed, implements the steps of the connection detection method as described in any of the preceding claims.

[0043] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the connection detection method as described in any of the preceding claims.

[0044] This application provides a connection detection method, apparatus, device, storage medium, and computer program product. It acquires a set of communication link attribute parameters of a communication system for which data connection error analysis is to be performed using an electronic device. The communication link attribute parameter set is statistically processed to obtain statistical information, and data connection error detection is performed based on this statistical information to obtain detection results. In this way, the electronic device performs statistical analysis on the device's communication link attribute parameter set to obtain statistical information, and further performs data connection error detection based on this statistical information to obtain detection results. This solves the problem of low efficiency in current connection error detection between physical servers and network devices, and proposes a connection detection method that performs a full analysis of the communication data between the two devices whose connection needs to be checked for correctness to determine whether there are errors in the connection, thus improving the efficiency of connection error checking. Attached Figure Description

[0045] Figure 1 A schematic flowchart of a connection detection method provided in an embodiment of this application;

[0046] Figure 2 This is a schematic diagram illustrating the application implementation process of a connection detection method provided in an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the structure of a wiring detection device provided in an embodiment of this application;

[0048] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0049] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0050] Embodiments of this application provide a connection detection method, referring to... Figure 1 As shown, the method is applied to an electronic device, and the method includes the following steps:

[0051] Step 101: Obtain the communication link attribute parameter set of the communication system for which data connection line connection error analysis is to be performed.

[0052] In this embodiment, the data connection line is a physical data line used to establish communication connections between data transmission interfaces for data transmission between interfaces. The communication link attribute parameter set includes one or more connection attribute parameters for connecting data transmission interfaces. The device to be analyzed is a device in the connection detection system that uses a data connection line for communication, including at least one electronic device with a data transmission interface. The electronic device can be a device with data analysis and calculation functions within the communication system, or it can be a device with data analysis and calculation functions independent of the communication system, depending on the specific circumstances.

[0053] For example, after connecting a data cable to the data transmission interface, the connection detection system will operate to enable the connected data cable to transmit data. By statistically analyzing the transmitted data, the communication link attribute parameter set for analyzing connection errors of the data cable can be obtained.

[0054] Step 102: Perform statistical processing on the communication link attribute parameter set to obtain data statistical information.

[0055] Each set of information in the data statistics includes the attribute information of the local device and the attribute information of the remote device.

[0056] In this embodiment, the local device is typically the data sender, and the remote device is typically the data receiver. Statistical processing is performed on the communication link attribute parameter set, for example, grouping the data in the communication link attribute parameter set according to a certain grouping characteristic or multiple grouping characteristics to obtain data statistical information.

[0057] For example, according to the data transmission characteristics, the attribute information in the communication link attribute parameter set is grouped and statistically processed to obtain data statistical information with the local device attribute information minus the remote device attribute information as the statistical feature.

[0058] Step 103: Detect data connection errors based on data statistics and obtain the detection results.

[0059] The test results are used to indicate at least the devices and / or ports with faulty data connection lines.

[0060] In this embodiment, statistical data is analyzed to detect data cable connection errors, thereby obtaining corresponding detection results. In some application scenarios, the detection results only need to indicate the device with the data cable error; in others, they only need to indicate the port with the error; and in still others, the results can specifically identify which devices and which ports have the incorrect data cable connections. This allows personnel to quickly locate and correct faulty data cables, reducing the likelihood of data transmission errors or system malfunctions due to incorrect connections, thus improving the user experience.

[0061] Based on the foregoing embodiments, in other embodiments of this application, step 102 can be implemented by the following steps: determining the device to be analyzed from the communication link attribute parameter set; wherein the device to be analyzed includes one or more devices; determining the target type device from the device to be analyzed to obtain m local devices; wherein m is an integer greater than or equal to 1; and statistically analyzing the transmission attribute parameters of the peer device connected to each communication port of each local device from the communication link attribute parameter set to obtain data statistics information.

[0062] In this embodiment, the target type device can be an attributed device of the target type. The target type can refer to a data output device that needs to be connected via a data cable, such as a switch type. Thus, firstly, device identification is performed from the collected communication link attribute parameter set to obtain the device to be analyzed. Then, according to the target type of data output and input, the device to be analyzed is grouped to obtain one or more local devices. Next, from the communication link attribute parameter set, each communication port of each local device (i.e., the peer device connected to the aforementioned data transmission interface) is obtained, and the corresponding transmission attribute parameters of the peer device are acquired. In this way, data statistical information can be obtained. That is, the data statistical information includes at least the device information of the local device and the relevant information of the peer device corresponding to each communication port of the local device.

[0063] Based on the foregoing embodiments, in other embodiments of this application, the step of statistically analyzing the transmission attribute parameters of the peer device connected to each communication port of each local device from the communication link attribute parameter set to obtain data statistical information can be achieved by the following steps:

[0064] From the set of communication link attribute parameters, the transmission attribute parameters of the peer device connected to each communication port of each local device are statistically analyzed to obtain parameter statistics; the parameter statistics are then preprocessed to obtain data statistics.

[0065] In this embodiment, the electronic device performs statistical analysis on the communication link attribute parameter set to obtain the transmission attribute parameters of the peer device connected to each communication port of each local device, thus obtaining parameter statistical information. Since the parameter statistical information contains a lot of data, including duplicate data and erroneous data, it is necessary to preprocess the parameter statistical information, such as data cleaning and incomplete data removal, to obtain data statistical information that can be used for subsequent data connection line connection error analysis.

[0066] Based on the foregoing embodiments, in other embodiments of this application, the data statistics information includes at least the following parameter types: local device identification information parameter, local device port parameter, local device Internet Protocol (IP) address parameter, local device port transmission rate parameter, peer device identification information parameter of peer device communicating with local device, peer device port parameter of peer device communicating with local device, and peer device IP address parameter of peer device communicating with local device. The data corresponding to each parameter type in the data statistics information is a quantized value of the same order of magnitude.

[0067] In this embodiment, the local device identification information parameter is device identification information used to uniquely identify the local device, such as the name, number, serial number, or other identity identification information of the local device. The local device port parameter is the port identification information of the communication port of the local device, such as the port number, port name, etc.

[0068] Based on the foregoing embodiments, in other embodiments of this application, step 103 can be implemented by the following steps: sorting the data group corresponding to each local device in the data statistics information according to the preset sorting of the local device port to obtain an ordered dataset for each local device; determining a set of data from the ordered dataset of each local device to obtain a standard analysis data group; standardizing the target parameter type data in the ordered dataset of the corresponding local device based on the standard analysis array to obtain the dataset to be analyzed for the corresponding local device; and performing data connection line connection error detection on the dataset to be analyzed and the corresponding ordered dataset of each local device to obtain the detection result.

[0069] In this embodiment, the preset sorting order of ports can be either sorted in descending order of port number or in ascending order of port number. The standard analysis data set can be any set of data from the ordered dataset of each local device, and can be set according to the actual scenario. The target parameter type is the parameter with a significant impact on data connection lines selected from the ordered dataset to reduce data processing volume, based on actual needs.

[0070] For the data in the statistical information, the data of each local device is sorted according to the port's preset sorting order to obtain an ordered dataset for each local device. Then, a set of data is selected from the ordered dataset of each local device, for example, the first set of data is selected as the standard analysis data set. Using the standard analysis data set, the data in the ordered dataset of the corresponding local device is standardized to obtain the dataset to be analyzed for each local device, so as to ensure that the data analysis is in an ordered univariate data form suitable for machine learning processing. Finally, the dataset to be analyzed and the corresponding ordered dataset of each local device are detected by a data connection error detection method, so as to obtain the detection results including whether there are data connection errors in each local device.

[0071] Based on the foregoing embodiments, in other embodiments of this application, the target parameter type includes at least: local device port parameters and peer device IP addresses. The steps are based on a standard analysis array, standardizing the target parameter type data in the ordered dataset of the corresponding local device to obtain the corresponding local device's dataset to be analyzed. This can be achieved by the following steps: calculating a first difference between the data of each local device port parameter in each ordered dataset and the data of the local device port parameters in the standard analysis array; calculating a second difference between the data of each peer device IP address in each ordered dataset and the data of the peer device IP address in the standard analysis array; calculating the difference between each first difference and the corresponding second difference to obtain a corresponding third difference, thereby obtaining the dataset to be analyzed.

[0072] Based on the foregoing embodiments, in other embodiments of this application, the step of performing data connection error detection on the dataset to be analyzed and the corresponding ordered dataset of each local device to obtain the detection result can be implemented by the following steps: performing numerical anomaly analysis on each dataset to be analyzed, determining the peer device corresponding to the abnormal value, and obtaining abnormal device information with data connection error to the corresponding local device; wherein, the detection result includes abnormal device information corresponding to each local device; performing anomaly analysis on the values ​​corresponding to the port parameters of the peer device in each ordered dataset to obtain port connection information of the corresponding local device with data connection error; wherein, the detection result includes port connection information corresponding to each local device.

[0073] In this embodiment, when performing numerical anomaly analysis on each dataset to be analyzed, it is determined whether there are any abnormal values. If abnormal values ​​are found, the corresponding peer device can be identified. Further, abnormal port parameters are determined to obtain port connection information. Due to the large amount of data, clustering analysis can be used to determine the anomalies in the port parameters. In some application scenarios, similarity calculation can also be used. The specific method can be determined based on the actual application scenario and is not specifically limited here. It should be noted that the above two analysis processes can be implemented using machine learning methods.

[0074] Based on the foregoing embodiments, in other embodiments of this application, each group of data in the ordered dataset is stored using a data dictionary.

[0075] In this embodiment, data is stored using a data dictionary, which facilitates rapid subsequent retrieval.

[0076] Based on the foregoing embodiments, in other embodiments of this application, the method further includes:

[0077] Output the detection results.

[0078] In this embodiment, the electronic device outputs the detection result, which can be done by outputting the result to an output module of the electronic device. The output module can be a display screen, an audio module, or both. In some application scenarios, the electronic device can also output the detection result to a portable device easily carried by the inspector, such as a smartphone, control terminal, or head-mounted display. The output information of the detection result can be in text, image, audio, or even photoelectric form, i.e., an indicator light showing a possible data cable connection error or the data cable illuminating. The specific implementation can be tailored to the actual application scenario and is not specifically limited here.

[0079] Based on the foregoing embodiments, this application provides a connection detection method that does not require pre-set rules and planning tables to perform unsupervised self-learning on actual connection conditions to detect anomalies in network connections and output the results as inspection findings. This connection detection method can be used, for example, in data center systems where there are numerous physical connections between physical servers and network devices. The specific implementation process for connection detection during hardware integration and construction can be referred to... Figure 2 The flowchart shown in the document includes the following steps:

[0080] Step a11: Data acquisition.

[0081] The data acquisition process specifically includes the following steps: collecting login information for each switch and server in the tested environment; capturing all switch data: logging into each switch using the Secure Shell (SSH) protocol for secure access and management of remote computers, capturing port information and the corresponding Link Layer Discovery Protocol (LLDP) information for each port; capturing all server data: accessing each server using the out-of-band management Redfish protocol, obtaining the Media Access Control (MAC) address information for each network card and port, and using the network card and port number as the corresponding port name; and performing a correlation query between the data captured from each switch and the data captured from each server, recording the results in a list format, thus merging them into a complete table of the current actual connection status. For example, as shown in Table 1, the current actual connection status table uses the switch as the local device. The table includes key information such as the local device's IP address, port name, the IP address of the peer server or the peer switch, and the peer device's port name, as well as auxiliary information such as the device names of the local and peer devices, and the device types of the local and peer devices. It should be noted that the current actual connection status table corresponds to the aforementioned parameter statistics.

[0082]

[0083] Step a12: Data cleaning and preprocessing.

[0084] The specific process of data cleaning and splitting preprocessing for the current actual connection status table can be described as follows:

[0085] Step a121: Clean the data in the current actual connection status table, remove incomplete data, such as data without peer device information, to obtain a cleaned data table.

[0086] Step a122: Split the cleaned data table according to the local device to obtain a file list corresponding to each local device. The data in the file list of each local device can be sorted in order according to the port name of the local device.

[0087] Step a123: For the file list of each local device, further split it according to the different data transmission rates of the ports to obtain file lists with different data transmission rates for each local device.

[0088] Step a124: For the file list of different data transmission rates of each local device, continue to split it according to the IP of the peer device. Split the duplicate peer devices into a new file list and distinguish them by suffix. Finally, each file can be identified by the local device's IP, the port's transmission rate and a unique set of peer devices as the file name.

[0089] For example, you can obtain TXT text files with filenames 10.xx1_10G_1.txt, 10.xx1_10G_2.txt, 10.xx1_40G_1.txt, 10.xx2_10G_1.txt, 10.xx2_10G_2.txt, 10.xx2_40G_1.txt, 10.xx3_10G_1.txt, and 10.xx3_40G_1.txt; thus, you can obtain the corresponding data statistics information. Correspondingly, the content of each file can be recorded in the form of a data dictionary. For example, the content of the file with filename 10.xx1_10G_1.txt is:

[0090] {"local_ip":"10.xx11","local_port":"1G / 0 / 01","remote_ip":"10.xy1","remote_port":"bmc","remote_type":"server"}

[0091] {"local_ip":"10.xx11","local_port":"1G / 0 / 02","remote_ip":"10.xy2","remote_port":"bmc","remote_type":"server"}

[0092] {"local_ip":"10.xx11","local_port":"1G / 0 / 03","remote_ip":"10.xy3","remote_port":"bmc","remote_type":"server"}

[0093] {"local_ip":"10.xx11","local_port":"1G / 0 / 04","remote_ip":"10.xy4","remote_port":"bmc","remote_type":"server"}

[0094] {"local_ip":"10.xx11","local_port":"1G / 0 / 05","remote_ip":"10.xy5","remote_port":"bmc","remote_type":"server"}.

[0095] Step a13: Data feature extraction and transformation.

[0096] Specifically, the file content obtained in step a12 is processed to extract data features and perform difference calculations, thus achieving data feature extraction and transformation. The core of this implementation is to process the network connection information into numerical form, that is, converting port names and IP addresses in the files into purely numerical expressions. Then, difference calculations are performed on the converted numbers to obtain ordered, univariate data suitable for machine learning processing. The specific implementation process of step a13 is as follows:

[0097] Step a131: Sort the n data dictionaries in the file in ascending order according to the local device's port name, local_port.

[0098] Step a132: Convert the port name of the local device to the numeric format num(local_port).

[0099] Step a133: Convert the IP address of the peer device into the numeric format num(remote_ip).

[0100] Step a134: Determine the first data item of the sorted local device as the baseline value.

[0101] Step a135: Starting from the second data entry, perform a difference operation between the corresponding port name and the port name num(local_port)1 in the first data entry, until the calculation ends at the last data entry in the file. This process can be recorded as: diff_local_porti = num(local_port)i - num(local_port)1, where i = 2, 3, ..., n, and n represents the total number of data entries in the corresponding file.

[0102] Step a136: Starting from the second data entry, perform a difference calculation on the IP address of the corresponding peer device and the IP address of the peer device in the first data entry until the last data entry in the file is reached. This process can be recorded as: diff_remote_ipi = num(remote_ip)i - num(remote_ip)1.

[0103] Step a137: Perform a second difference calculation on the calculation results in steps a135 and a136. The calculation process can be recorded as: diff_resulti = diff_remote_ipi - diff_local_porti, and save the data dictionary index n and the calculated second difference result diff_resulti in the result file.

[0104] For example, the result file named 10.xx1_10G_1.txt can be renamed to 10.xx1_10G_1_diff.txt, and the content of this file is:

[0105] 0#0#

[0106] 0#1#

[0107] 0#2#

[0108] 0#3#

[0109] 0#4#

[0110] 0#5#

[0111] 0#6#

[0112] Step a14: Result judgment.

[0113] Step a14 can be achieved by the following steps:

[0114] Step a141: Determine if there are any connection anomalies at the device level.

[0115] In this process, outlier searches are performed on the list of diff_result values ​​in each of the aforementioned result files. The search criteria used during the search can be the frequency of occurrence or the consistency of adjacent values. In some application scenarios, outlier detection algorithms such as Z-Score can also be used for identification to obtain the identification results. Based on the identification results and index values, devices with wiring errors can be identified.

[0116] Step a142: Determine port-level connection anomalies.

[0117] Specifically, cluster analysis is performed on the remote_port values ​​of the remote devices included in the files obtained in step a124. The proportion of their occurrence in the corresponding files is calculated, and one or more outliers with a proportion less than a preset threshold are identified as having a possible faulty data connection for their port names. Thus, the aforementioned detection results can be obtained based on steps a141 and a142.

[0118] Step a15: Output the results.

[0119] Specifically, data connection lines with connection errors identified in step a14 can be marked in the current actual connection result table. This facilitates user verification and rectification based on planning and design requirements. After marking, prompts can be provided via text, voice, or a combination of text and voice.

[0120] In this way, by capturing all the actual network connection data, grouping the data appropriately, converting it into numerical values ​​and calculating the difference, and then combining it with machine learning methods for anomaly detection, a complete process for determining the results of a network connection inspection without raw data is completed. This achieves automated network connection inspection without the need for connection rules and connection information tables as input data, reducing reliance on pre-set data and manual input. Furthermore, during the automated inspection process, traditional text-based network connection information is transformed into data features expressed in numerical form.

[0121] The connection detection method provided in this application acquires the communication link attribute parameter set of the communication system to be analyzed for data connection errors through an electronic device, performs statistical processing on the communication link attribute parameter set to obtain data statistical information, and performs data connection error detection based on the data statistical information to obtain the detection result. In this way, the electronic device performs statistical analysis on the communication link attribute parameter set of the device to obtain data statistical information, and further performs data connection error detection on the data statistical information to obtain the detection result. This solves the problem of low efficiency in current connection error detection between physical servers and network devices, and proposes a connection detection method that performs full analysis on the communication data between the two devices whose connection needs to be checked for correctness to determine whether there are errors in the connection, thus improving the efficiency of connection error checking.

[0122] Based on the foregoing embodiments, embodiments of this application provide a connection detection device, which can be applied to... Figure 1 In the connection detection method provided in the corresponding embodiments, refer to Figure 3 As shown, the connection detection device 2 may include: an acquisition unit 21, a processing unit 22, and a detection unit 23; wherein:

[0123] Acquisition unit 21 is used to acquire the communication link attribute parameter set of the communication system to be analyzed for data connection line connection errors;

[0124] Processing unit 22 is used to perform statistical processing on the communication link attribute parameter set to obtain data statistical information; wherein, each set of information in the data statistical information includes local device attribute information and remote device attribute information;

[0125] The detection unit 23 is used to detect data connection line errors based on data statistical information and obtain detection results; wherein, the detection results are used to indicate at least the device with a data connection line error and / or the port with a data connection line error.

[0126] In other embodiments of this application, the processing unit includes: a first determining module and a statistics module; wherein:

[0127] The first determining module is used to determine the device to be analyzed from the set of communication link attribute parameters; wherein the device to be analyzed includes one or more devices;

[0128] The first determining module is also used to determine the target type of the device from the devices to be analyzed, and obtain m local devices; where m is an integer greater than or equal to 1;

[0129] The statistics module is used to collect statistics on the transmission attribute parameters of the peer device connected to each communication port of each local device from the communication link attribute parameter set, and obtain data statistics information.

[0130] In other embodiments of this application, the statistics module is specifically used to implement the following steps:

[0131] From the set of communication link attribute parameters, statistical information on the transmission attribute parameters of the peer device connected to each communication port of each local device is obtained by statistically analyzing the parameter statistics.

[0132] Preprocessing the parameter statistics yields the data statistics.

[0133] In other embodiments of this application, the data statistics information includes at least the following parameter types: local device identification information parameter, local device port parameter, local device Internet Protocol (IP) address parameter, local device port transmission rate parameter, peer device identification information parameter of peer device communicating with local device, peer device port parameter of peer device communicating with local device, and peer device IP address parameter of peer device communicating with local device. The data corresponding to each parameter type in the data statistics information is a quantized value of the same order of magnitude.

[0134] In other embodiments of this application, the detection unit includes: a sorting module, a second determining module, a processing module, and a detection module; wherein:

[0135] The sorting module is used to sort the data group corresponding to each local device in the data statistics information according to the preset sorting of the local device port, so as to obtain the ordered dataset of each local device.

[0136] The second determination module is used to determine a set of data from the ordered dataset of each local device to obtain a standard analysis data set.

[0137] The processing module is used to standardize the target parameter type data in the ordered dataset of the corresponding local device based on the standard analysis array, so as to obtain the corresponding local device's dataset to be analyzed.

[0138] The detection module is used to detect data connection errors in the dataset to be analyzed and the corresponding ordered dataset of each local device, and obtain the detection results.

[0139] In other embodiments of this application, the target parameter type includes at least: local device port parameters and peer device IP address, and the processing module is specifically used to implement the following steps:

[0140] Calculate the first difference between the data of each local device port parameter in each ordered dataset and the data of the local device port parameters in the standard analysis array;

[0141] Calculate the second difference between the data for each pair of end device IP addresses in each ordered dataset and the data for the pair of end device IP addresses in the standard analysis array;

[0142] Calculate the difference between each first difference and the corresponding second difference to obtain the corresponding third difference, thus obtaining the dataset to be analyzed.

[0143] In other embodiments of this application, the detection module is specifically used to implement the following steps:

[0144] Numerical anomaly analysis is performed on each dataset to be analyzed to identify the peer device corresponding to the abnormal value and obtain information on the abnormal device that has a data connection error with the corresponding local device; the detection results include the abnormal device information corresponding to each local device.

[0145] Anomaly analysis is performed on the values ​​of the port parameters of the peer device in each ordered dataset to obtain the port connection information of the local device where the data connection cable is connected incorrectly; the detection results include the port connection information of each local device.

[0146] In other embodiments of this application, each group of data in the ordered dataset is stored using a data dictionary.

[0147] In other embodiments of this application, the connection detection device further includes: an output unit; wherein:

[0148] The output unit is used to output the detection results.

[0149] It should be noted that the process of information interaction between units and modules in this embodiment can be referred to the description in other embodiments, and will not be repeated here.

[0150] The connection detection device provided in this application acquires the communication link attribute parameter set of the communication system to be analyzed for data connection errors through an electronic device, performs statistical processing on the communication link attribute parameter set to obtain data statistical information, and performs data connection error detection based on the data statistical information to obtain the detection result. In this way, the electronic device performs statistical analysis on the communication link attribute parameter set of the device to obtain data statistical information, and further performs data connection error detection on the data statistical information to obtain the detection result. This solves the problem of low efficiency in current connection error detection between physical servers and network devices, and proposes a connection detection method that performs full analysis on the communication data between the two devices whose connection needs to be checked for correctness to determine whether there are errors in the connection, thus improving the efficiency of connection error checking.

[0151] Based on the foregoing embodiments, embodiments of this application provide an electronic device that can be applied to... Figure 1 In the connection detection method provided in the corresponding embodiments, refer to Figure 4 As shown, the electronic device 3 may include: a communication interface 31, a memory 32, a processor 33, and a communication bus 34; wherein:

[0152] Memory 32 is used to store executable information;

[0153] The communication bus 34 is used to realize the communication connection between the communication interface 31, the processor 33 and the memory 32;

[0154] Processor 33 is used to execute the connection detection program stored in memory 32, to achieve, for example... Figure 1 The implementation process of the connection detection method provided in the corresponding embodiments will not be repeated here.

[0155] Based on the foregoing embodiments, embodiments of this application provide a computer-readable storage medium, simply referred to as a storage medium, which stores one or more programs that can be executed by one or more processors to implement the reference. Figure 1 The implementation process of the connection detection method provided in the corresponding embodiments will not be repeated here.

[0156] Based on the foregoing embodiments, this application also provides a computer program product, including a computer program that can be executed by the processor 33 of the electronic device 3 to complete any of the foregoing method steps.

[0157] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0158] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0159] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0160] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0161] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application.

Claims

1. A method of wire detection, the method comprising: The method comprises: obtaining a set of communication link attribute parameters of a communication system to be analyzed for data connection line connection error; statistically processing the set of communication link attribute parameters to obtain data statistical information; wherein each group of information in the data statistical information comprises local device attribute information and opposite end device attribute information, and the data corresponding to each parameter type in the data statistical information is a quantized value of a uniform order of magnitude; based on the data statistical information, detecting data connection line connection error to obtain a detection result; wherein the detection result at least indicates the device with data connection line error and / or the port with data connection line error; wherein the detection of data connection line connection error based on the data statistical information to obtain a detection result comprises: sorting the data group corresponding to each local device in the data statistical information according to the preset sorting of the local device port to obtain an ordered data set of each local device; determining a group of data from the ordered data set of each local device to obtain a standard analysis data group; based on the standard analysis data group, standardizing the data of the target parameter type in the ordered data set of the corresponding local device to obtain an analysis data set of the corresponding local device; detecting data connection line connection error for each analysis data set of the local device and the corresponding ordered data set to obtain the detection result; wherein the target parameter type at least includes the local device port parameter and the opposite end device IP address, and the standardization of the data of the target parameter type in the ordered data set of the corresponding local device based on the standard analysis data group to obtain the analysis data set of the corresponding local device comprises: calculating the first difference between the data of each local device port parameter in each ordered data set and the data of the local device port parameter in the standard analysis data group; calculating the second difference between the data of each opposite end device IP address in each ordered data set and the data of the opposite end device IP address in the standard analysis data group; calculating the difference between each first difference and the corresponding second difference to obtain the corresponding third difference, thereby obtaining the analysis data set.

2. The method of claim 1, wherein, The data statistical information at least includes the following parameter types: local device identification information parameter, local device port parameter, local device Internet Protocol (IP) address parameter, transmission rate parameter of local device port, opposite end device identification information parameter of opposite end device in communication connection with the local device, opposite end device port parameter of opposite end device in communication connection with the local device, and opposite end device IP address parameter of opposite end device in communication connection with the local device.

3. The method of claim 1, wherein, The detection of data connection line connection error for each analysis data set of the local device and the corresponding ordered data set to obtain the detection result comprises: The numerical anomaly analysis is performed on each of the to-be-analyzed data sets, a corresponding opposite end device of an abnormal numerical value is determined, and abnormal device information of a data connection line connection error existing in the corresponding local end device is obtained; wherein, the detection result includes the abnormal device information corresponding to each of the local end devices; The numerical anomaly analysis is performed on the numerical value corresponding to the opposite end device port parameter in each of the ordered data sets, and port connection information of a data connection line connection error existing in the corresponding local end device is obtained; wherein, the detection result includes the port connection information corresponding to each of the local end devices.

4. The method of claim 1, wherein, Each group of data in the ordered data set is stored by using a data dictionary.

5. The method of claim 1, wherein, The statistical processing is performed on the communication link attribute parameter set, and data statistical information is obtained, including: A to-be-analyzed device is determined from the communication link attribute parameter set; wherein, the to-be-analyzed device includes one or more devices; A target type device is determined from the to-be-analyzed device, and m local end devices are obtained; wherein, m is an integer greater than or equal to 1; The transmission attribute parameter of the opposite end device of each communication port communication connection of each of the local end devices is counted from the communication link attribute parameter set, and the data statistical information is obtained.

6. The method of claim 5, wherein, The transmission attribute parameter of the opposite end device of each communication port communication connection of each of the local end devices is counted from the communication link attribute parameter set, and the data statistical information is obtained. The transmission attribute parameter of the opposite end device of each communication port communication connection of each of the local end devices is counted from the communication link attribute parameter set, and parameter statistical information is obtained. The method further includes:

7. The method of claim 1, wherein, The detection result is output. The device includes an acquisition unit, a processing unit, and a detection unit; wherein:

8. A wire detection apparatus, characterized by comprising: The acquisition unit is configured to acquire a communication link attribute parameter set of a communication system to be analyzed for a data connection line connection error; The processing unit is configured to perform statistical processing on the communication link attribute parameter set, and obtain data statistical information; wherein, each group of information in the data statistical information includes local end device attribute information and opposite end device attribute information, and the data in each parameter type in the data statistical information is a quantized numerical value of a uniform order of magnitude; The detection unit is configured to perform data connection line connection error detection based on the data statistical information, and obtain a detection result; wherein, the detection result at least indicates a device with a data connection line error and / or a port with a data connection line error; The detection unit is specifically configured to implement the following steps: Each local end device corresponding data group in the data statistical information is sorted according to a local end device port preset order, and an ordered data set of each of the local end devices is obtained; A group of data is determined from each of the ordered data sets of the local end devices, and a standard analysis data group is obtained; The data of a target parameter type in the ordered data set of the corresponding local end device is standardized based on the standard analysis data group, and a to-be-analyzed data set of the corresponding local end device is obtained; ​ The data connection line connection error detection is performed on the to-be-analyzed data set of each of the local devices and the corresponding ordered data set, and a detection result is obtained; The target parameter types include at least the local device port parameters and the opposite device IP addresses. When the detection unit performs the standardization processing on the data of the target parameter types in the ordered data set of the corresponding local device based on the standard analysis array to obtain the to-be-analyzed data set of the corresponding local device, the following steps are implemented: A first difference value between the data of each of the local device port parameters in each of the ordered data sets and the data of the local device port parameters in the standard analysis array is calculated. A second difference value between the data of each of the opposite device IP addresses in each of the ordered data sets and the data of the opposite device IP addresses in the standard analysis array is calculated. A third difference value is obtained by calculating a difference value between each of the first difference values and the corresponding second difference values, so that the to-be-analyzed data set is obtained.

9. An electronic device, comprising: The device includes a communication interface, a memory, a processor, and a communication bus. The memory is configured to store executable information. The communication bus is configured to realize communication connection among the communication interface, the processor, and the memory. The processor is configured to execute the connection detection program stored in the memory to realize the steps in the connection detection method according to any one of claims 1 to 7.

10. A storage medium, characterized by The storage medium stores the connection detection program, and the connection detection program is executed to realize the steps in the connection detection method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, realizes the steps in the connection detection method according to any one of claims 1 to 7.

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