Connection detection method, device and equipment, storage medium and computer program product

Abstract

The invention discloses a connection detection method. The method comprises the following steps: acquiring a communication link attribute parameter set of a communication system to be subjected to data connection line connection error analysis; performing statistical processing on the communication link attribute parameter set to obtain data statistical information; wherein each group of information in the data statistical information comprises home terminal equipment attribute information and opposite terminal equipment 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 at least used for indicating the equipment with the data connection line error and / or the port with the data connection line error. The invention further discloses a connection detection device and equipment, a storage medium and a computer program product.

Description

Technical Field

[0001] The present application relates to the field of computer communication technology, and in particular to a connection detection method, device, equipment, storage medium and computer program product. Background Art

[0002] With the rapid development of computer technology, the application of computers is becoming more and more extensive. In order to ensure computer services, the demand for computer infrastructure is also increasing. For example, in order to ensure computer services, many computer applications have data service centers, and a large number of physical connections are used between physical servers and network devices to provide Internet communication services. The specific connection rules, that is, the equipment types and port planning at both ends of the connection, often vary depending on the scale of each data center and the requirements of network design. Therefore, during the hardware integration construction phase, it is difficult to fully check whether the network physical connection is connected correctly. Often, connection problems can only be discovered after subsequent deployment and business launch, such as disconnection, wrong device connection or wrong port connection, which increases the difficulty of operation and maintenance and troubleshooting. At present, there are two main commonly used network connection detection methods. One is spot check, that is, manual inspection of the physical connection of the cable, port indicator light, etc., and the other is full inspection, that is, inspection is carried out based on a pre-prepared full and complete expected connection information table as the inspection basis.

[0003] However, in the above scheme, the manual inspection coverage is low, the inspection effect is limited, and it is limited to the scene with fewer connections. The full inspection requires a clear and detailed understanding of the connection rules in advance, and a full and complete expected connection information table is prepared in advance to realize the inspection. If there is no expected connection information table, the full inspection cannot be realized, which brings a large amount of data preparation work, and the inspection result is highly dependent on the data accuracy of the expected connection information table itself, resulting in a low accuracy rate of the full inspection. Therefore, how to realize simple, fast and practical connection inspection has become a technical problem that needs to be solved urgently.

[0004] Application Contents

[0005] In order to solve the above technical problems, the present application hopes to provide a connection detection method, device, system, storage medium and computer program product, which solves the current problem of low efficiency in detecting whether the connection between physical servers and network devices is wrong, and proposes a connection detection method. By detecting the communication data between the two end devices that need to detect whether the connection is correct, a full analysis is performed to determine whether there is an error in the connection, thereby improving the efficiency of connection error checking.

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

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

[0008] Acquire a communication link attribute parameter set of a communication system for which a data connection line connection error analysis is to be performed;

[0009] Performing 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 attribute information of the local device and attribute information of the opposite device;

[0010] A data line connection error detection is performed based on the data statistical information to obtain a detection result; wherein the detection result is at least used to indicate a device with a data line error and / or a port with a data line error.

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

[0012] Determine a device to be analyzed from the communication link attribute parameter set; wherein the device to be analyzed includes one or more devices;

[0013] Determine a target type of device from the devices to be analyzed, and obtain m local devices; wherein m is an integer greater than or equal to 1;

[0014] The data statistical information is obtained by counting the transmission attribute parameters of the opposite-end device to which each communication port of each local-end device is connected in communication from the communication link attribute parameter set.

[0015] In the above scheme, the transmission attribute parameters of the opposite end device to which each communication port of each local end device is connected in communication are counted from the communication link attribute parameter set to obtain the data statistical information, including:

[0016] From the communication link attribute parameter set, statistics are collected on the transmission attribute parameters of the opposite end device to which each communication port of each of the local end devices is connected for communication, to obtain parameter statistical information;

[0017] The parameter statistical information is preprocessed to obtain the data statistical information.

[0018] In the above scheme, the data statistical information at least includes the following parameter types: local device identification information parameters, local device port parameters, local device Internet Protocol IP address parameters, local device port transmission rate parameters, peer device identification information parameters of the peer device connected to the local device for communication, peer device port parameters of the peer device connected to the local device for communication, and peer device IP address parameters of the peer device connected to the local device for communication. The data corresponding to each parameter type in the data statistical information is a quantized value of uniform magnitude.

[0019] In the above solution, the data line connection error detection is performed based on the data statistical information to obtain the detection result, including:

[0020] Sort 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;

[0021] Determine a set of data from the ordered data sets of each local device to obtain a standard analysis data set;

[0022] Based on the standard analysis array, standardize the data of the target parameter type in the ordered data set of the corresponding local device to obtain the corresponding data set to be analyzed of the local device;

[0023] A data connection line connection error detection is performed on each data set to be analyzed of the local device and the corresponding ordered data set to obtain the detection result.

[0024] In the above scheme, the target parameter type includes at least: the local device port parameter and the opposite device IP address, and the standard analysis array is based on which the target parameter type data in the ordered data set of the corresponding local device is standardized to obtain the corresponding data set to be analyzed of the local device, including:

[0025] Calculating a 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 array;

[0026] Calculating a second difference between the data of each of the peer device IP addresses in each of the ordered data sets and the data of the peer device IP addresses in the standard analysis array;

[0027] The difference between each first difference value and the corresponding second difference value is calculated to obtain a corresponding third difference value, thereby obtaining the data set to be analyzed.

[0028] In the above scheme, the data connection line connection error detection is performed on each data set to be analyzed and the corresponding ordered data set of the local device to obtain the detection result, including:

[0029] Performing numerical abnormality analysis on each of the data sets to be analyzed, determining the opposite-end device corresponding to the abnormal numerical value, and obtaining abnormal device information having a data connection line connection error with the corresponding local device; wherein the detection result includes the abnormal device information corresponding to each of the local devices;

[0030] Anomaly analysis is performed on the numerical values ​​corresponding to the port parameters of the opposite-end device in each of the ordered data sets to obtain the port connection information of the corresponding local-end device having a data connection line connection error; wherein the detection result includes the port connection information corresponding to each of the local-end devices.

[0031] In the above solution, each set of data in the ordered data set is stored using a data dictionary.

[0032] In the above scheme, the method further includes:

[0033] The detection result is outputted.

[0034] The present application provides a connection detection device, which includes: an acquisition unit, a processing unit and a detection unit; wherein:

[0035] The acquisition unit is used to acquire a 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 opposite device attribute information;

[0037] The detection unit is used to perform data connection line connection error detection based on the data statistical information to obtain a detection result; wherein the detection result is at least used to indicate a device with a data connection line error and / or a port with a data connection line error.

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

[0039] A memory for storing executable information;

[0040] A communication bus, used to realize the communication connection between the communication interface, the processor and the memory;

[0041] The processor is used to execute the connection detection program stored in the memory to implement the steps in any of the above-mentioned connection detection methods.

[0042] The present application provides a storage medium, on which a connection detection program is stored. When the connection detection program is executed, it is used to implement the steps of any of the above-mentioned connection detection methods.

[0043] The present application provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, the steps of any one of the above-mentioned connection detection methods are implemented.

[0044] The embodiment of the present application provides a connection detection method, device, equipment, storage medium and computer program product, which obtains the communication link attribute parameter set of the communication system to be subjected to data connection line link error analysis through an electronic device, performs statistical processing on the communication link attribute parameter set to obtain data statistical information, and performs data connection line connection error detection based on the data statistical information to obtain a 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 line connection error detection on the data statistical information to obtain a detection result, which solves the current problem of low efficiency in detecting whether the connection between the physical server and the network device is wrong, and proposes a connection detection method, which performs full analysis by detecting the communication data between the two end devices that need to detect whether the connection is correct, and determines whether there is an error in the connection, thereby improving the efficiency of the connection error check. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of a flow chart of a connection detection method provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of an application implementation process of a connection detection method provided in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the structure of a connection detection device provided in an embodiment of the present application;

[0048] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0050] The embodiment of the present application provides a connection detection method, referring to Figure 1 As shown, the method is applied to an electronic device, and the method comprises the following steps:

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

[0052] In an embodiment of the present application, the data connection line is a physical data line for communication connection between data transmission interfaces, and is used to transmit data between interfaces. The communication link attribute parameter set includes one or more connection attribute parameters when connecting between data transmission interfaces. The device to be analyzed is a device that uses a data connection line for communication connection in a connection detection system to be performed, including at least one electronic device with a data transmission interface. The electronic device can be a device with data analysis and calculation functions in the communication system, or it can be a device with data analysis and calculation functions independent of the communication system, which can be determined by actual conditions.

[0053] Exemplarily, after the data connection line is connected to the data transmission interface, the connection detection system is operated to enable the connected data connection line to transmit data. In this way, a statistical analysis is performed on the transmitted data to obtain a communication link attribute parameter set for the data connection line connection error analysis.

[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 information includes attribute information of the local device and attribute information of the opposite device.

[0056] In the embodiment of the present application, the local device is usually a data transmitter, and the opposite device is usually a data receiver. The communication link attribute parameter set is statistically processed, for example, the data in the communication link attribute parameter set is grouped according to a certain grouping characteristic or multiple grouping characteristics to obtain data statistical information.

[0057] Exemplarily, 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 opposite device attribute information as the statistical feature.

[0058] Step 103: Perform data line connection error detection based on the data statistical information to obtain a detection result.

[0059] The detection result is at least used to indicate a device with a data connection line error and / or a port with a data connection line error.

[0060] In an embodiment of the present application, data statistical information is analyzed to implement data cable connection error detection, thereby obtaining corresponding detection results. In some application scenarios, the detection result only needs to indicate the device with a data cable error, in some application scenarios, the detection result only needs to indicate the port with a data cable connection error, and in some application scenarios, the detection result can specifically indicate which devices and ports have data cable connection errors. In this way, data cable connection personnel can quickly locate the wrongly connected data cable and perform connection correction, reducing the possibility of data transmission errors or system malfunctions due to data cable connection errors, and improving the user experience.

[0061] Based on the foregoing embodiments, in other embodiments of the present 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 of device from the device to be analyzed to obtain m local devices; wherein m is an integer greater than or equal to 1; from the communication link attribute parameter set, counting the transmission attribute parameters of the opposite device to which each communication port of each local device is connected for communication, to obtain data statistical information.

[0062] In an embodiment of the present application, a target type device may have a target type attribute. The target type may refer to a device that is a data output device and needs to be connected by a data connection line, for example, the target type is a switch type. In this way, firstly, the device is identified from the collected communication link attribute parameter set to obtain the device to be analyzed, and then the device to be analyzed is grouped according to the target type of data output and input to obtain one or more local devices, and then each communication port of each local device is obtained from the communication link attribute parameter set, that is, the opposite device to which the aforementioned data transmission interface is connected, and the transmission attribute parameters of the corresponding opposite device are obtained, so that data statistical information can be obtained. That is, the data statistical information at least includes the device information of the local device and the relevant information of the opposite device corresponding to each communication port of the local device.

[0063] Based on the foregoing embodiment, in other embodiments of the present application, the step of counting the transmission attribute parameters of the opposite end device to which each communication port of each local end device is connected in communication from the communication link attribute parameter set to obtain data statistical information can be implemented by the following steps:

[0064] From the communication link attribute parameter set, the transmission attribute parameters of the opposite end device connected to each communication port of each local end device are counted to obtain parameter statistical information; the parameter statistical information is preprocessed to obtain data statistical information.

[0065] In an embodiment of the present application, the electronic device performs statistical analysis on the communication link attribute parameter set, obtains the transmission attribute parameters of the opposite device for each communication port of each local device to obtain parameter statistical information. Since the parameter statistical information includes a large amount of data, including duplicate data, error data and other unnecessary data, the parameter statistical information needs to be preprocessed, such as data cleaning, incomplete data removal, etc., to obtain data statistical information that can be used for subsequent data line connection error analysis.

[0066] Based on the foregoing embodiments, in other embodiments of the present application, the data statistical information includes at least the following parameter types: local device identification information parameters, local device port parameters, local device Internet Protocol (IP) address parameters, local device port transmission rate parameters, peer device identification information parameters of the peer device connected to the local device for communication, peer device port parameters of the peer device connected to the local device for communication, and peer device IP address parameters of the peer device connected to the local device for communication, and the data corresponding to each parameter type in the data statistical information is a quantized value of uniform magnitude.

[0067] In the embodiment of the present application, the local device identification information parameter is the device identification information used to uniquely identify the local device, such as the name, number, serial number, etc. 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 the present application, step 103 can be implemented by the following steps: sorting the data groups 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 for each local device; determining a group of data from the ordered data set of each local device to obtain a standard analysis data set; based on the standard analysis array, standardizing the data of the target parameter type in the ordered data set of the corresponding local device to obtain the data set to be analyzed of the corresponding local device; performing data connection line connection error detection on the data set to be analyzed of each local device and the corresponding ordered data set to obtain a detection result.

[0069] In the embodiment of the present application, the preset sorting order of the ports can be sorted in the order of the port numbers from large to small, or can be sorted in the order of the port numbers from small to large. The standard analysis data group can be any set of data in the ordered data set of each local device, which can be set according to the actual scenario. The target parameter type is a parameter with a greater impact on the data connection line connection selected from the ordered data set according to actual needs in order to reduce the amount of data processing.

[0070] For the data in the data statistical information, take each local device as a unit, sort the data of each local device using the port preset sorting order to obtain an ordered data set for each local device; then determine a group of data from the ordered data set of each local device, for example, determine the first group of data as the standard analysis data set; use the standard analysis data set to standardize the data in the ordered data set of the corresponding local device to obtain the data set to be analyzed for each local device, so as to ensure that the data analysis is in an ordered univariate data form suitable for processing by machine learning; finally, use the data connection line connection error detection method to detect the data set to be analyzed for each local device and the corresponding ordered data set, thereby obtaining a detection result including whether there is a data connection line connection error for each local device.

[0071] Based on the foregoing embodiments, in other embodiments of the present application, the target parameter type includes at least: a local device port parameter and an opposite device IP address, and the step is based on a standard analysis array to perform standardization on the data of the target parameter type in the ordered data set of the corresponding local device to obtain the corresponding data set to be analyzed for the local device, which can be achieved by the following steps: calculating a 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 array; calculating a second difference between the data of each opposite device IP address in each ordered data set and the data of the opposite 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 data set to be analyzed.

[0072] Based on the foregoing embodiments, in other embodiments of the present application, the step of performing data line connection error detection on the data set to be analyzed and the corresponding ordered data set of each local device to obtain a detection result can be achieved by the following steps: performing numerical anomaly analysis on each data set to be analyzed, determining the opposite device corresponding to the abnormal numerical value, and obtaining abnormal device information of a data line connection error with the corresponding local device; wherein the detection result includes the abnormal device information corresponding to each local device; performing an anomaly analysis on the numerical value corresponding to the port parameter of the opposite device in each ordered data set, and obtaining the port connection information of the corresponding local device with a data line connection error; wherein the detection result includes the port connection information corresponding to each local device.

[0073] In an embodiment of the present application, when performing numerical anomaly analysis on each data set to be analyzed, it is determined whether there are abnormal values. When there are abnormal values, the corresponding opposite-end device can be determined; further, the abnormal port parameters are determined to obtain the port connection information. In the abnormal determination process of abnormal port parameters, since the amount of data is relatively large, cluster analysis can be used to implement it. In some application scenarios, it can also be implemented by calculating similarity. The specific method can be determined by 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 embodiment, in other embodiments of the present application, each group of data in the ordered data set is stored using a data dictionary.

[0075] In the embodiment of the present application, data is stored in a data dictionary, which is conducive to subsequent quick query.

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

[0077] Output the detection results.

[0078] In an embodiment of the present application, the electronic device outputs the test result, and the test result can be output to an output module of the electronic device for output, and the output module can be a display screen, or an audio module, or a display screen and an audio module; in some application scenarios, the electronic device can also output the test result to a removable portable device that is easy for the inspector to carry, such as a smart phone, a control terminal, or a head-mounted display device. The output information of the test result can be in text form, image form, audio form, or even in photoelectric form, that is, the port indicator light or data line indicating that there may be a data line connection error is processed by light emission. It can be implemented according to the actual application scenario, and is not specifically limited here.

[0079] Based on the above embodiments, the embodiments of the present application provide a connection detection method that performs unsupervised self-learning on actual connection conditions without pre-set rules and planning tables to discover anomalies in network connections and outputs them as inspection results. The connection detection method can be used in a data center system, for example, where there are a large number of physical connections between physical servers and network devices. During hardware integration construction, the specific implementation process for connection detection can refer to Figure 2 As shown in the flowchart in, the specific steps include:

[0080] Step a11: data collection.

[0081] The data collection process specifically includes the following collection process: collecting login information of each switch and server in the tested environment; fully capturing switch data: logging into each switch through the Secure Shell (SSH) protocol used to securely access and manage remote computers, capturing port information and the Link Layer Discovery Protocol (LLDP) information corresponding to each port; fully capturing server data: accessing each server through the out-of-band management Redfish protocol, obtaining the Media Access Control (MAC) address information of each network card and each network port, and using the network card and network port serial number as the corresponding port name; correlating the data captured from each switch with the data captured from each server, and recording them in the form of a list, so that they can be merged into a full table of current actual connection status. For example, as shown in Table 1, in the current actual connection status table, the switch is used as the local device, and the content in the table includes key information such as the IP of the local device, the port name of the local device, the IP of the opposite server or the IP of the opposite switch, the port name of the opposite device, and the device name of the local device and the device name of the opposite device, the device type of the local device and the device type of the opposite device. It should be noted that the current actual connection status table corresponds to the aforementioned parameter statistical information.

[0082]

[0083] Step a12: data cleaning and preprocessing.

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

[0085] Step a121, clean the data content in the current actual connection status table, remove incomplete data, such as content without peer device information, and 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, wherein the data in the files in the file list of each local device can be sorted in sequence according to the port name of the local device.

[0087] Step a123: The file list of each local device is further split according to the different data transmission rates of the ports to obtain a file list of different data transmission rates for each local device.

[0088] Step a124, for each local device with different data transmission rates, continue to split the file list according to the IP of the opposite device, split the repeated opposite devices into a new file list, and use suffixes to distinguish them. Each file finally obtained can use the IP of the local device, the transmission rate of the port and a non-repeating set of opposite devices as the file name to identify the file.

[0089] For example, the TXT texts with file names of 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 can be obtained; thus, the data statistics information corresponding to the above can be obtained. Correspondingly, the file content in each file can be recorded in the form of a data dictionary. For example, the file content of the file named 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 conversion.

[0096] Among them, the file content in each file obtained in step a12 is subjected to data feature extraction and difference calculation to realize data feature extraction and conversion. The core point of the implementation is to process the information of the network connection into a numerical form, that is, to convert the port name in the file into a pure digital expression, and to convert the IP address in the file into a pure digital expression, so as to perform difference calculation on the converted numbers to form an ordered single variable data suitable for processing by machine learning. Among them, the specific implementation process of step a13 can be as follows:

[0097] Step a131, sort the n data dictionaries in the file in ascending order according to the port name local_port of the local device.

[0098] Step a132: Convert the port name of the local device into a numerical format num(local_port).

[0099] Step a133: convert the IP address of the peer device into a numerical format num(remote_ip).

[0100] Step a134, determine the first piece of data of the local device after sorting as the reference value.

[0101] Step a135, starting from the second data, the corresponding port name is differentially processed with the port name num(local_port)1 in the first data, until the calculation ends with the last data in the file. The 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 number of all data included in the corresponding file.

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

[0103] Step a137, perform secondary difference calculation on the calculation results in step a135 and step 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 secondary difference result diff_resulti in the result file.

[0104] For example, the result file named 10.xx1_10G_1.txt may be named 10.xx1_10G_1_diff.txt, and the content of the 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 determination.

[0113] Among them, step a14 can be implemented by the following steps:

[0114] Step a141, determine whether the connection at the device level is abnormal.

[0115] Among them, outlier search is performed on the diff_result value list in each of the above result files. The search conditions used in the search process can be the frequency of occurrence, or the consistency of the previous and next values. In some application scenarios, it can also be combined with outlier recognition algorithms such as Z-Score to obtain recognition results. According to the recognition results and index values, the device with connection errors can be determined.

[0116] Step a142: Determine whether the port-level connection is abnormal.

[0117] Among them, cluster analysis is performed on the remote_port value of the port name of the opposite device included in the file obtained in step a124, and the occurrence ratio of the remote_port value in the corresponding file is counted to determine that the data connection line corresponding to the port name of one or more opposite devices whose occurrence ratio is less than the abnormal value of the preset threshold value may be incorrectly connected. In this way, the above-mentioned detection result can be obtained based on steps a141 and a142.

[0118] Step a15: Output the result.

[0119] The data connection lines with connection errors determined in step a14 can be marked in the current actual connection result table, so that the user can check and verify with the planning and design requirements and make corresponding corrections. After marking, prompts can be processed in the form of text, voice, or text and voice.

[0120] In this way, by capturing the entire amount of data on actual network connections, reasonably grouping the data, converting it into numerical values ​​and calculating the difference, and then combining it with machine learning methods to make abnormal judgments, a complete, passive data network connection inspection result judgment process is completed. This allows automated inspection of network connections in the absence of passive data without the need for connection rules and connection information tables as input data, thereby reducing reliance on preset data and manual input. In addition, in the automated inspection process, it is possible to convert traditional text-based network connection information into data features expressed in numerical form.

[0121] The connection detection method provided in the embodiment of the present application obtains the communication link attribute parameter set of the communication system to be subjected to data connection line connection error analysis through an electronic device, performs statistical processing on the communication link attribute parameter set to obtain data statistical information, and performs data connection line connection error detection based on the data statistical information to obtain a 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 line connection error detection on the data statistical information to obtain a detection result, which solves the current problem of low efficiency in detecting whether the connection between the physical server and the network device is wrong, and proposes a connection detection method, which performs full analysis by detecting the communication data between the two end devices that need to detect whether the connection is correct, and determines whether there is an error in the connection, thereby improving the efficiency of the connection error check.

[0122] Based on the above embodiments, the embodiments of the present application provide a connection detection device, which can be applied to Figure 1 In the connection detection method provided in the corresponding embodiment, 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] An acquisition unit 21 is used to acquire a communication link attribute parameter set of a communication system for which a data connection line connection error analysis is to be performed;

[0124] The 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 attribute information of the local device and attribute information of the opposite device;

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

[0126] In other embodiments of the present application, the processing unit includes: a first determination module and a statistical module; wherein:

[0127] A first determination module is used to determine a device to be analyzed from a communication link attribute parameter set; wherein the device to be analyzed includes one or more devices;

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

[0129] The statistics module is used to count the transmission attribute parameters of the opposite-end device connected to each communication port of each local-end device from the communication link attribute parameter set to obtain data statistical information.

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

[0131] From the communication link attribute parameter set, statistics are collected on the transmission attribute parameters of the opposite end device to which each communication port of each local end device is connected in communication, to obtain parameter statistical information;

[0132] Preprocess the parameter statistical information to obtain data statistical information.

[0133] In other embodiments of the present application, the data statistical information includes at least the following parameter types: local device identification information parameters, local device port parameters, local device Internet Protocol IP address parameters, local device port transmission rate parameters, peer device identification information parameters of the peer device connected to the local device for communication, peer device port parameters of the peer device connected to the local device for communication, and peer device IP address parameters of the peer device connected to the local device for communication. The data corresponding to each parameter type in the data statistical information is a quantized value of uniform magnitude.

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

[0135] A sorting module, used to sort 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;

[0136] A second determination module is used to determine a group of data from the ordered data set of each local device to obtain a standard analysis data group;

[0137] A processing module, used for performing standardization processing on the data of the target parameter type in the ordered data set of the corresponding local device based on the standard analysis array, to obtain the data set to be analyzed of the corresponding local device;

[0138] The detection module is used to perform data connection line connection error detection on the data set to be analyzed and the corresponding ordered data set of each local device to obtain a detection result.

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

[0140] Calculate a 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 array;

[0141] Calculate a second difference between the data of each peer device IP address in each ordered data set and the data of the peer device IP address in the standard analysis array;

[0142] The difference between each first difference value and the corresponding second difference value is calculated to obtain the corresponding third difference value, thereby obtaining the data set to be analyzed.

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

[0144] Performing numerical anomaly analysis on each data set to be analyzed, determining the opposite-end device corresponding to the abnormal numerical value, and obtaining abnormal device information having a data connection line connection error with the corresponding local device; wherein the detection result includes the abnormal device information corresponding to each local device;

[0145] Anomaly analysis is performed on the values ​​corresponding to the port parameters of the opposite-end device in each ordered data set to obtain port connection information of the corresponding local-end device having a data connection line connection error; wherein the detection result includes the port connection information corresponding to each local-end device.

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

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

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

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

[0150] The connection detection device provided in the embodiment of the present application obtains the communication link attribute parameter set of the communication system to be subjected to data connection line connection error analysis through an electronic device, performs statistical processing on the communication link attribute parameter set to obtain data statistical information, and performs data connection line connection error detection based on the data statistical information to obtain a 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 line connection error detection on the data statistical information to obtain a detection result, which solves the current problem of low efficiency in detecting whether the connection between the physical server and the network device is wrong, and proposes a connection detection method, which performs full analysis by detecting the communication data between the two end devices that need to detect whether the connection is correct, and determines whether there is an error in the connection, thereby improving the efficiency of the connection error check.

[0151] Based on the above embodiments, the embodiments of the present application provide an electronic device, which can be applied to Figure 1 In the connection detection method provided in the corresponding embodiment, 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] A memory 32, for storing executable information;

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

[0154] The processor 33 is used to execute the connection detection program stored in the memory 32 to implement the following Figure 1 The implementation process of the connection detection method provided in the corresponding embodiment will not be repeated here.

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

[0156] Based on the foregoing embodiments, the embodiments of the present application further provide a computer program product, including a computer program, which 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 appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of hardware embodiments, software embodiments, or embodiments in combination with software and hardware. Moreover, the present application may adopt 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, etc.) that contain computer-usable program code.

[0158] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0159] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0160] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

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

Claims

1. A connection detection method, characterized in that: The method comprises: Acquire a communication link attribute parameter set of a communication system for which a data connection line connection error analysis is to be performed; Performing 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 attribute information of the local device and attribute information of the opposite device; A data line connection error detection is performed based on the data statistical information to obtain a detection result; wherein the detection result is at least used to indicate a device with a data line error and / or a port with a data line error.

2. The method according to claim 1, characterized in that The data statistical information includes at least the following parameter types: local device identification information parameters, local device port parameters, local device Internet Protocol IP address parameters, local device port transmission rate parameters, peer device identification information parameters of the peer device connected to the local device for communication, peer device port parameters of the peer device connected to the local device for communication, and peer device IP address parameters of the peer device connected to the local device for communication. The data corresponding to each parameter type in the data statistical information is a quantized value of uniform magnitude.

3. The method according to claim 2, characterized in that The performing data line connection error detection based on the data statistical information to obtain a detection result includes: Sort 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; Determine a set of data from the ordered data sets of each local device to obtain a standard analysis data set; Based on the standard analysis array, standardize the data of the target parameter type in the ordered data set of the corresponding local device to obtain the corresponding data set to be analyzed of the local device; A data connection line connection error detection is performed on each data set to be analyzed of the local device and the corresponding ordered data set to obtain the detection result.

4. The method according to claim 3, characterized in that The target parameter type at least includes: the local device port parameter and the opposite device IP address, and the standard analysis array is based on which the target parameter type data in the ordered data set of the corresponding local device is standardized to obtain the corresponding data set to be analyzed of the local device, including: Calculating a 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 array; Calculating a second difference between the data of each of the peer device IP addresses in each of the ordered data sets and the data of the peer device IP addresses in the standard analysis array; The difference between each first difference value and the corresponding second difference value is calculated to obtain a corresponding third difference value, thereby obtaining the data set to be analyzed.

5. The method according to claim 3, characterized in that: The performing data connection line connection error detection on each data set to be analyzed of the local device and the corresponding ordered data set to obtain the detection result includes: Performing numerical abnormality analysis on each of the data sets to be analyzed, determining the opposite-end device corresponding to the abnormal numerical value, and obtaining abnormal device information having a data connection line connection error with the corresponding local device; wherein the detection result includes the abnormal device information corresponding to each of the local devices; Anomaly analysis is performed on the numerical values ​​corresponding to the port parameters of the opposite-end device in each of the ordered data sets to obtain the port connection information of the corresponding local-end device having a data connection line connection error; wherein the detection result includes the port connection information corresponding to each of the local-end devices.

6. The method according to claim 3, characterized in that Each set of data in the ordered data set is stored using a data dictionary.

7. The method according to claim 1, characterized in that The statistical processing of the communication link attribute parameter set to obtain data statistical information includes: Determine a device to be analyzed from the communication link attribute parameter set; wherein the device to be analyzed includes one or more devices; Determine a target type of device from the devices to be analyzed, and obtain m local devices; wherein m is an integer greater than or equal to 1; The data statistical information is obtained by counting the transmission attribute parameters of the opposite-end device to which each communication port of each local-end device is connected in communication from the communication link attribute parameter set.

8. The method according to claim 7, characterized in that The obtaining of the data statistical information by counting the transmission attribute parameters of the opposite end device connected to each communication port of each local end device from the communication link attribute parameter set includes: From the communication link attribute parameter set, statistics are collected on the transmission attribute parameters of the opposite end device to which each communication port of each of the local end devices is connected for communication, to obtain parameter statistical information; The parameter statistical information is preprocessed to obtain the data statistical information.

9. The method according to claim 1, characterized in that: The method further comprises: The detection result is outputted.

10. A connection detection device, characterized in that: The device comprises: an acquisition unit, a processing unit and a detection unit; wherein: The acquisition unit is used to acquire a communication link attribute parameter set of the communication system for which data connection line connection error analysis is to be performed; 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 opposite device attribute information; The detection unit is used to perform data connection line connection error detection based on the data statistical information to obtain a detection result; wherein the detection result is at least used to indicate a device with a data connection line error and / or a port with a data connection line error.

11. An electronic device, characterized in that: The device comprises: a communication interface, a memory, a processor and a communication bus; wherein: A memory for storing executable information; A communication bus, used to realize the communication connection between the communication interface, the processor and the memory; A processor is used to execute a connection detection program stored in a memory to implement the steps in the connection detection method according to any one of claims 1 to 9.

12. A storage medium, characterized in that: The storage medium stores a connection detection program, which is used to implement the steps of the connection detection method according to any one of claims 1 to 9 when executed.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the steps of the connection detection method according to any one of claims 1 to 9.

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