Network connection stability testing method and system based on network camera

Abstract

The present invention relates to the field of network technology, and discloses a network connection stability test method and system based on a network camera, the method comprising: constructing a virtual network camera of a physical network camera, identifying an actual access mode of the physical network camera, constructing a multimodal access data stream of the virtual network camera based on the actual access mode, performing a virtual access to the virtual network camera based on the multimodal access data stream, and obtaining access test data of the virtual network camera; using a trained stability analysis model to analyze the network stability coefficient of the virtual network camera, analyzing the network abnormal state of the physical network camera, and constructing a stability analysis report of the physical network camera. The present invention can improve the test effect of the network connection stability of the network camera.

Description

Technical Field

[0001] The present invention relates to the field of network technology, and in particular to a network connection stability testing method and system based on a network camera. Background Art

[0002] Network connection stability testing refers to the process of evaluating the ability of a network system to maintain connectivity and performance under various conditions. The purpose of network connection stability testing is to ensure that the network can meet the expected performance requirements and provide reliable services to users. These tests can be performed before network deployment or regularly during operation to monitor and maintain network stability. Through these tests, problems in the network can be discovered and resolved, network configuration can be optimized, and network reliability and efficiency can be improved.

[0003] At present, the network connection stability test mainly collects the operating data of the network camera through sensor equipment, and analyzes factors such as network bandwidth and network delay through the operating data for evaluation. Due to data limitations, this method can only analyze the network status under the current user's usage status, and cannot analyze the network camera in other operating environments such as a large number of users accessing, user erroneous operations, etc., making the network connection stability analysis of the network camera not comprehensive enough, resulting in poor results in the network connection stability test of the network camera. Summary of the invention

[0004] The present invention provides a network connection stability testing method and system based on a network camera, the main purpose of which is to improve the testing effect on the network connection stability of the network camera.

[0005] To achieve the above object, the present invention provides a network connection stability testing method based on a network camera, comprising:

[0006] Acquire a software stack and a network condition of a physical network camera, construct a virtual network camera of the physical network camera based on the software stack and the network condition, and calculate a consistency coefficient between the physical network camera and the virtual network camera;

[0007] When the consistency coefficient meets the preset consistency threshold, constructing a cloud computing test environment for the virtual network camera, and integrating the virtual network camera into the cloud computing test environment to obtain a virtual network camera to be tested;

[0008] Identify the actual access mode of the physical network camera, construct a multimodal access data stream of the virtual network camera based on the actual access mode, perform a virtual access to the virtual network camera based on the multimodal access data stream, and obtain access test data of the virtual network camera;

[0009] Based on the access test data, the network stability coefficient of the virtual network camera is analyzed using a trained stability analysis model, and based on the network stability coefficient, the network abnormal state of the physical network camera is analyzed, wherein the abnormal state includes an actual network abnormal state and an analyzed network abnormal state;

[0010] Based on the abnormal network status, a stability analysis report of the physical network camera is constructed.

[0011] Optionally, constructing a virtual network camera of the physical network camera based on the software stack and the network condition includes:

[0012] Selecting a virtualization platform for the physical network camera;

[0013] Based on the software stack, construct a virtual camera template of the physical network camera using the virtualization platform;

[0014] Based on the virtual camera template, deploy a virtual camera instance of the physical network camera;

[0015] Based on the network condition, configuring a virtual network of the virtual camera instance;

[0016] Based on the virtual network and the software stack, the parameters of the virtual camera instance are adjusted to obtain a virtual network camera of the physical network camera.

[0017] Optionally, the calculating a consistency coefficient between the physical network camera and the virtual network camera includes:

[0018] Collecting physical camera data and virtual camera data of the physical network camera and the virtual network camera respectively;

[0019] Constructing a physical level list and a virtual level list of the physical camera data and the virtual camera data respectively;

[0020] Performing list matching on the physical level list and the virtual level list to obtain a matching list;

[0021] Calculating a list level difference of the matching list;

[0022] Based on the list level difference and the number of list matching pairs corresponding to the matching list, a consistency coefficient between the physical network camera and the virtual network camera is calculated.

[0023] Optionally, the calculating the consistency coefficient between the physical network camera and the virtual network camera based on the list level difference and the number of list matching pairs corresponding to the matching list includes:

[0024] Calculating the degrees of freedom of the matching list based on the number of matching pairs in the list;

[0025] Based on the list level difference and the degree of freedom, the consistency coefficient between the physical network camera and the virtual network camera is calculated using the following formula:

[0026]

[0027] Where τ represents the consistency coefficient between the physical webcam and the virtual webcam, S i It represents the absolute value of the difference between the i-th list level of the matching list corresponding to the physical network camera and the virtual network camera, Z represents the degree of freedom, and m represents the number of list matching pairs of the matching list corresponding to the physical network camera and the virtual network camera.

[0028] Optionally, identifying the actual access mode of the physical network camera includes:

[0029] Collecting access data of the physical network camera;

[0030] Preprocessing the access data to obtain preprocessed access data;

[0031] identifying access behavior of the preprocessed access data;

[0032] Analyzing the access association relationship of the access behavior;

[0033] The actual access mode of the physical network camera is identified through the access association relationship.

[0034] Optionally, constructing a multimodal access data stream of the virtual network camera based on the actual access mode includes:

[0035] identifying access pattern characteristics of the actual access pattern;

[0036] Based on the access mode characteristics, determining the access data flow template structure and access data flow template rules of the virtual network camera;

[0037] Based on the access data flow template structure and the access data flow template rule, construct an access data flow template of the virtual network camera;

[0038] Based on the access data stream template, a multimodal access data stream of the virtual network camera is generated.

[0039] Optionally, analyzing the network stability coefficient of the virtual network camera using a trained stability analysis model based on the access test data includes:

[0040] Identifying stable influencing factors of the access test data;

[0041] Determining a factor quantization value of the stability influencing factor based on the access test data;

[0042] Serializing the factor quantization value to obtain a serialized factor quantization value;

[0043] Marking the time node of the quantized value of the serialization factor;

[0044] Calculating the factor difference of the time node based on the standard factor value corresponding to the time node and the serialized factor quantization value;

[0045] Based on the factor difference and the factor quantization value, the network stability coefficient of the virtual network camera is calculated using the stability analysis model.

[0046] Optionally, the calculating the network stability coefficient of the virtual network camera using the stability analysis model based on the factor difference and the serialization factor quantization value includes:

[0047] Identifying a stability impact factor corresponding to the virtual network camera and a stability impact correlation coefficient of the virtual network camera;

[0048] Determining a stability impact factor weight of the stability impact factor based on the stability impact correlation coefficient;

[0049] Based on the factor difference, the factor quantization value and the stability influencing factor weight, the network stability coefficient of the virtual network camera is calculated using the stability calculation function of the stability analysis model:

[0050]

[0051] Where ρ represents the network stability coefficient of the virtual network camera, E c represents the factor quantization value of the cth stable influencing factor of the virtual network camera, D c represents the factor difference of the cth stable influencing factor of the virtual network camera, J c represents the stable influence factor weight of the cth stable influence factor of the virtual network camera, ∈ represents the network accident coefficient, and r represents the number of stable influence factors corresponding to the virtual network camera.

[0052] Optionally, constructing a stability analysis report of the physical network camera based on the abnormal network state includes:

[0053] Analyze the network abnormality characteristics and network abnormality level of the network abnormality state;

[0054] Based on the network abnormality feature, locate the network abnormality cause of the physical network camera;

[0055] Based on the cause of the network anomaly, analyzing the network stabilization strategy of the physical network camera;

[0056] Based on the network anomaly level, the network anomaly cause and the network stability strategy, a stability analysis report of the physical network camera is constructed.

[0057] In order to solve the above problems, the present invention also provides a network connection stability testing system based on a network camera, the system comprising:

[0058] A virtual camera construction module, used to obtain a software stack and a network condition of a physical network camera, construct a virtual network camera of the physical network camera based on the software stack and the network condition, and calculate a consistency coefficient between the physical network camera and the virtual network camera;

[0059] A test environment configuration module, used for constructing a cloud computing test environment for the virtual network camera when the consistency coefficient meets a preset consistency threshold, and integrating the virtual network camera into the cloud computing test environment to obtain a virtual network camera to be tested;

[0060] A virtual multimodal access module, used to identify the actual access mode of the physical network camera, construct a multimodal access data stream of the virtual network camera based on the actual access mode, and perform a virtual access to the virtual network camera based on the multimodal access data stream to obtain access test data of the virtual network camera;

[0061] A network anomaly analysis module, used to analyze the network stability coefficient of the virtual network camera based on the access test data using a trained stability analysis model, and to analyze the network anomaly state of the physical network camera based on the network stability coefficient, wherein the anomaly state includes an actual network anomaly state and an analyzed network anomaly state;

[0062] An analysis report building module is used to build a stability analysis report of the physical network camera based on the abnormal network state.

[0063] The present invention constructs a virtual network camera of the physical network camera based on the software stack and the network conditions to create multiple virtual network cameras for testing and simulating real-world camera deployment; the present invention determines the degree of performance matching between the physical network camera and the virtual network camera by calculating the consistency coefficient between them, thereby improving the reliability of the subsequent virtual camera stability test; further, the present invention constructs a cloud computing test environment for the virtual network camera when the consistency coefficient meets a preset consistency threshold, which can facilitate the network connection stability test of the network camera; the present invention can identify the actual access mode of the physical network camera by identifying the actual access mode of the physical network camera, thereby providing a basis for the subsequent construction of a multimodal access data stream of the virtual network camera; optionally, the present invention constructs a multimodal access data stream of the virtual network camera based on the actual access mode to simulate the real The access requirements of real users to the network camera are used to effectively test and verify the virtual network camera. Furthermore, based on the access test data, the present invention uses a trained stability analysis model to analyze the network stability coefficient of the virtual network camera, which can analyze the network stability coefficient of the virtual network camera, thereby evaluating its stability under multimodal access data streams. Finally, based on the network stability coefficient, the present invention analyzes the network abnormal state of the physical network camera, wherein the abnormal state includes the actual network abnormal state and the analysis network abnormal state. The abnormal state of the physical network camera can be analyzed based on the network stability coefficient, and corresponding measures can be taken to ensure the stability and reliability of the network camera in actual deployment. Based on the network abnormal state, a stability analysis report of the physical network camera is constructed. A detailed physical network camera stability analysis report can be constructed to provide valuable reference for solving network abnormal states. Therefore, the network connection stability testing method and system based on the network camera proposed in the present invention can improve the testing effect of the network connection stability of the network camera. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic diagram of a flow chart of a network connection stability testing method based on a network camera provided by an embodiment of the present invention;

[0065] Figure 2 A functional module diagram of a network connection stability testing system based on a network camera provided by an embodiment of the present invention;

[0066] Figure 3 A schematic diagram of the structure of an electronic device of a network connection stability testing system based on a network camera provided in one embodiment of the present invention;

[0067] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0069] The embodiment of the present application provides a network connection stability testing method based on a network camera. The execution subject of the network connection stability testing method based on a network camera includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the network connection stability testing method based on a network camera can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0070] Reference Figure 1 FIG. 1 is a flow chart of a network connection stability testing method based on a network camera according to an embodiment of the present invention. In this embodiment, the network connection stability testing method based on a network camera includes:

[0071] S1. Obtain a software stack and a network condition of a physical network camera, construct a virtual network camera of the physical network camera based on the software stack and the network condition, and calculate a consistency coefficient between the physical network camera and the virtual network camera.

[0072] It should be explained that the physical webcam refers to an actual webcam device with physical hardware, including camera sensors, lenses, processors, memory, storage and other hardware components; the software stack refers to all software running on the physical webcam, including operating systems, device drivers, applications and firmware; the network conditions refer to the characteristics of the network to which the camera is connected, including network performance indicators such as bandwidth, latency, jitter, and packet loss rate.

[0073] The present invention constructs a virtual network camera of the physical network camera based on the software stack and the network conditions to create multiple virtual network cameras for testing and simulating real-world camera deployment. This method provides flexibility and scalability, allowing simulation of various test scenarios without affecting physical hardware. The virtual network camera refers to a virtual device created on a virtualization platform that simulates the functions of a real physical network camera.

[0074] In detail, the constructing a virtual network camera of the physical network camera based on the software stack and the network condition includes:

[0075] Selecting a virtualization platform for the physical network camera;

[0076] Based on the software stack, construct a virtual camera template of the physical network camera using the virtualization platform;

[0077] Based on the virtual camera template, deploy a virtual camera instance of the physical network camera;

[0078] Based on the network condition, configuring a virtual network of the virtual camera instance;

[0079] Based on the virtual network and the software stack, the parameters of the virtual camera instance are adjusted to obtain a virtual network camera of the physical network camera.

[0080] Among them, the virtualization platform refers to a software or hardware system that provides virtualization functions, such as VMware vSphere, Microsoft Hyper-V, OpenStack, KVM (Kernel-based Virtual Machine), Docker and other platforms; the virtual camera template refers to a reusable virtual machine or container configuration file created based on the software stack of the physical network camera. The template includes the installation of the operating system, the settings of the driver, application and firmware, as well as the network configuration, etc.; the virtual camera instance refers to a specific virtual machine or container instance created based on the virtual camera template; the virtual network refers to a logical network environment created on the virtualization platform, which provides network connection for the virtual camera instance. The virtual network includes components such as virtual switches, routers, network address translation (NAT) instances, and can simulate real network topologies and conditions.

[0081] Furthermore, the virtual network of the virtual camera instance configured based on the network conditions can be implemented through devices such as a virtual switch, a network address translation (NAT), and a virtual router.

[0082] Optionally, the present invention determines the performance matching degree between the physical network camera and the virtual network camera by calculating the consistency coefficient between them, thereby improving the reliability of the subsequent virtual camera stability test. The consistency coefficient refers to the degree of consistency between the physical network camera and the virtual network camera, and its value ranges from -1 to 1, and the closer the value is to 1, the higher the consistency.

[0083] In detail, the calculating the consistency coefficient between the physical network camera and the virtual network camera includes:

[0084] Collecting physical camera data and virtual camera data of the physical network camera and the virtual network camera respectively;

[0085] Constructing a physical level list and a virtual level list of the physical camera data and the virtual camera data respectively;

[0086] Performing list matching on the physical level list and the virtual level list to obtain a matching list;

[0087] Calculating a list level difference of the matching list;

[0088] Based on the list level difference and the number of list matching pairs corresponding to the matching list, a consistency coefficient between the physical network camera and the virtual network camera is calculated.

[0089] The physical camera data refers to performance indicator data collected from a physical network camera, such as delay, packet loss rate, bandwidth and other data. The virtual camera data refers to performance indicator data collected from a virtual network camera. These data are generated by simulating a virtual environment of a physical camera and are used to evaluate the performance of a virtual camera. The physical level list refers to a level list created by converting each data point into a level according to the physical camera data. The level is usually determined by the value of the data point. For example, the data points can be sorted from low to high according to the size of the delay and the level is assigned. The virtual level list refers to a level list created by converting each data point into a level according to the virtual camera data. The construction method of this level list is similar to that of the physical level list. The matching list refers to matching the physical level list with the virtual level list to obtain a matched list. In the matching list, each physical data point is matched with the corresponding virtual data point. The list level difference refers to the difference between the physical level and the virtual level in the list. The number of list matching pairs refers to the number of data point pairs in the matching list, that is, the number of pairs of matching physical data points with virtual data points.

[0090] Further, the calculating the consistency coefficient between the physical network camera and the virtual network camera based on the list level difference and the number of list matching pairs corresponding to the matching list includes:

[0091] Calculating the degrees of freedom of the matching list based on the number of matching pairs in the list;

[0092] Based on the list level difference and the degree of freedom, the consistency coefficient between the physical network camera and the virtual network camera is calculated using the following formula:

[0093]

[0094] Where τ represents the consistency coefficient between the physical webcam and the virtual webcam, S i It represents the absolute value of the difference between the i-th list level of the matching list corresponding to the physical network camera and the virtual network camera, Z represents the degree of freedom, and m represents the number of list matching pairs of the matching list corresponding to the physical network camera and the virtual network camera.

[0095] The degrees of freedom represent how many level differences are unknown when comparing the physical level list and the virtual level list. The calculation of the degrees of freedom helps to determine the statistical significance of the Kendall coefficient of harmony, and then determine whether the consistency between the physical webcam and the virtual webcam is significant.

[0096] S2. When the consistency coefficient meets a preset consistency threshold, a cloud computing test environment for the virtual network camera is constructed, and the virtual network camera is integrated into the cloud computing test environment to obtain a virtual network camera to be tested.

[0097] The present invention can facilitate the network connection stability test of the network camera by constructing the cloud computing test environment of the virtual network camera when the consistency coefficient meets the preset consistency threshold.

[0098] It should be explained that the consistency threshold refers to a pre-set standard or limit when evaluating the consistency between a virtual webcam and a physical webcam, and the cloud computing test environment refers to a simulated real environment built on a cloud computing platform for testing and verifying the stability of a virtual webcam. In detail, the cloud computing test environment may refer to cloud service providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

[0099] In detail, the integration of the virtual network camera into the cloud computing test environment can be achieved by connecting the virtual camera instance of the virtual network camera to other network components in the test environment, such as a video management server, a storage system, etc., and configuring the network interface to ensure that the virtual camera instance can send and receive data streams.

[0100] S3. Identify the actual access mode of the physical network camera, construct a multimodal access data stream of the virtual network camera based on the actual access mode, perform a virtual access to the virtual network camera based on the multimodal access data stream, and obtain access test data of the virtual network camera.

[0101] The present invention can identify the actual access mode of the physical network camera by identifying the actual access mode of the physical network camera, providing a basis for the subsequent construction of a multimodal access data stream of a virtual network camera. The actual access mode refers to a typical mode of user access behavior observed in the physical network camera.

[0102] In detail, the identifying the actual access mode of the physical network camera includes:

[0103] Collecting access data of the physical network camera;

[0104] Preprocessing the access data to obtain preprocessed access data;

[0105] identifying access behavior of the preprocessed access data;

[0106] Analyzing the access association relationship of the access behavior;

[0107] The actual access mode of the physical network camera is identified through the access association relationship.

[0108] Among them, the access data refers to the original data about user access behavior collected from physical network cameras, which usually include access time, access duration, access path, access content and other information; the pre-processed access data refers to the data after the original access data is cleaned, formatted and standardized, which includes removing invalid or duplicate data, filling missing values, converting data format and other steps; the access behavior refers to the user's operation and activities on the network camera, such as watching videos, controlling the camera, querying data, etc.; the access association relationship refers to the correlation or correlation between user access behaviors, which includes analyzing the user's access behavior at different time points, the switching mode between different cameras, and the relationship between access behavior and specific events.

[0109] Furthermore, the access association relationship of the access behavior may be analyzed using data mining and machine learning techniques.

[0110] Optionally, the present invention constructs a multimodal access data stream of the virtual network camera based on the actual access mode to simulate the access requirements of real users to the network camera, thereby effectively testing and verifying the virtual network camera. The multimodal access data stream includes access instructions such as video stream, control command, and data query.

[0111] In detail, the constructing of the multimodal access data stream of the virtual network camera based on the actual access mode includes:

[0112] identifying access pattern characteristics of the actual access pattern;

[0113] Based on the access mode characteristics, determining the access data flow template structure and access data flow template rules of the virtual network camera;

[0114] Based on the access data flow template structure and the access data flow template rule, construct an access data flow template of the virtual network camera;

[0115] Based on the access data stream template, a multimodal access data stream of the virtual network camera is generated.

[0116] Among them, the access mode features refer to the key features extracted in the process of identifying the actual access mode, which can reflect the specific behaviors and patterns of users accessing network cameras. The access data flow template structure refers to the organization method of multimodal access data flow, which defines the combination and order of different access modes in the access data flow. The structural design should be able to simulate the actual access behavior of users, including the playback of video streams, the sending of control commands, the initiation of data queries, etc. The access data flow template rules refer to the logical relationships and constraints between access modes defined in the multimodal access data flow. The rules may include access time intervals, access path sequence, relevance of access content, etc., to ensure the rationality and authenticity of the access data flow. The access data flow template refers to a specific access data flow configuration constructed based on the access data flow template structure and the access data flow template rules. The template defines the detailed parameters of the multimodal access data flow, such as the size of the video stream, the type of control commands, the frequency of data queries, etc.

[0117] Furthermore, the generation of the multimodal access data stream of the virtual network camera based on the access data stream template can be generated by tools such as Selenium, JMeter, SoapUI, etc.

[0118] In the present invention, the access test data refers to the data recorded and collected about the performance and response of the virtual network camera under simulated access behavior, such as access mode, access time, response time, video quality and other data.

[0119] In detail, the virtual access to the virtual network camera based on the multimodal access data stream includes accessing the virtual network camera by generating a multimodal access data stream, simulating the user's access time, access frequency, access path, etc., to simulate the real access scenario and monitor the performance indicators during the access process, such as response time, video fluency, control command execution time and other data, and using these performance indicator data as access test data.

[0120] S4. Based on the access test data, the network stability coefficient of the virtual network camera is analyzed using a trained stability analysis model. Based on the network stability coefficient, the network abnormal state of the physical network camera is analyzed, wherein the abnormal state includes an actual network abnormal state and an analyzed network abnormal state.

[0121] The present invention can analyze the network stability coefficient of the virtual network camera based on the access test data and the trained stability analysis model, thereby evaluating its stability under multimodal access data flow. The network stability coefficient refers to the network stability of the virtual network camera.

[0122] In detail, the analyzing the network stability coefficient of the virtual network camera based on the access test data using the trained stability analysis model includes:

[0123] Identifying stable influencing factors of the access test data;

[0124] Determining a factor quantization value of the stability influencing factor based on the access test data;

[0125] Serializing the factor quantization value to obtain a serialized factor quantization value;

[0126] Marking the time node of the quantized value of the serialization factor;

[0127] Calculating the factor difference of the time node based on the standard factor value corresponding to the time node and the serialized factor quantization value;

[0128] Based on the factor difference and the factor quantization value, the network stability coefficient of the virtual network camera is calculated using the stability analysis model.

[0129] Among them, the stability influencing factor refers to the variables or indicators that play a key role in the performance stability analysis of the virtual network camera. These factors may include response time, video fluency, control command execution time, data query response time, etc. The factor quantification value refers to the conversion of the stability influencing factor into a quantifiable value for the convenience of analysis and comparison. The response time can be quantified into a specific time value, and the video fluency can be quantified into a fluency index, etc. The time node refers to a time point related to a certain access event in the access test data. The factor difference refers to the difference between the serialized factor quantization value and the standard factor value at the time node. The standard factor value refers to the ideal state or reference value of the performance stability of the virtual network camera at the time node.

[0130] Further, the calculating the network stability coefficient of the virtual network camera using the stability analysis model based on the factor difference and the serialization factor quantization value includes:

[0131] Identifying a stability impact factor corresponding to the virtual network camera and a stability impact correlation coefficient of the virtual network camera;

[0132] Determining a stability impact factor weight of the stability impact factor based on the stability impact correlation coefficient;

[0133] Based on the factor difference, the factor quantization value and the stability influencing factor weight, the network stability coefficient of the virtual network camera is calculated using the stability calculation function of the stability analysis model:

[0134]

[0135] Where ρ represents the network stability coefficient of the virtual network camera, E c represents the factor quantization value of the cth stable influencing factor of the virtual network camera, D c represents the factor difference of the cth stable influencing factor of the virtual network camera, J c represents the stable influence factor weight of the cth stable influence factor of the virtual network camera, ∈ represents the network accident coefficient, and r represents the number of stable influence factors corresponding to the virtual network camera.

[0136] The network accident coefficient refers to the probability of a network device or service failing within a specific period of time, such as a network outage, line short circuit, or other failure.

[0137] The present invention analyzes the network abnormal state of the physical network camera based on the network stability coefficient, wherein the abnormal state includes the actual network abnormal state and the analysis network abnormal state. The abnormal state of the physical network camera can be analyzed based on the network stability coefficient, and corresponding measures can be taken to ensure the stability and reliability of the network camera in actual deployment. The actual network abnormal state refers to the network problem or abnormal situation encountered during the actual operation of the physical network camera, and the analysis network abnormal state refers to the possible network problem or abnormal situation inferred by analyzing the network stability coefficient and related network performance indicators. In detail, the calculated network stability coefficient is compared with a preset benchmark or threshold. If the network stability coefficient is lower than the threshold, or has a significant difference from the benchmark, this may indicate that the network camera has a network abnormal state. The analysis network abnormal state can be inferred based on statistical analysis of the network stability coefficient data, trend prediction, pattern recognition and other methods.

[0138] S5. Based on the abnormal network status, construct a stability analysis report of the physical network camera.

[0139] The present invention constructs a stability analysis report of the physical network camera based on the abnormal network state, and can construct a detailed stability analysis report of the physical network camera, thereby providing a valuable reference for solving the abnormal network state.

[0140] In detail, the constructing of the stability analysis report of the physical network camera based on the abnormal state of the network includes:

[0141] Analyze the network abnormality characteristics and network abnormality level of the network abnormality state;

[0142] Based on the network abnormality feature, locate the network abnormality cause of the physical network camera;

[0143] Based on the cause of the network anomaly, analyzing the network stabilization strategy of the physical network camera;

[0144] Based on the network anomaly level, the network anomaly cause and the network stability strategy, a stability analysis report of the physical network camera is constructed.

[0145] Among them, the network anomaly feature refers to the specific manifestation of the network anomaly state, including the type of anomaly, time of occurrence, duration, scope of impact, etc. The network anomaly level refers to the importance and urgency of the network anomaly state, and the level can be divided into minor, medium, severe, etc. to reflect the impact of the network anomaly on the performance of the network camera. The network anomaly cause refers to the cause of the network anomaly state, which may include hardware failure, software error, network configuration problem, external interference, etc. The network stability strategy refers to the measures and solutions taken to improve the stability of the network camera, including hardware upgrades, software updates, network configuration optimization, regular maintenance and other solutions.

[0146] Furthermore, the analysis of the network anomaly characteristics and network anomaly levels of the network anomaly state may be implemented through tools such as Nagios and Zabbix.

[0147] The present invention constructs a virtual network camera of the physical network camera based on the software stack and the network conditions to create multiple virtual network cameras for testing and simulating real-world camera deployment; the present invention determines the degree of performance matching between the physical network camera and the virtual network camera by calculating the consistency coefficient between them, thereby improving the reliability of the subsequent virtual camera stability test; further, the present invention constructs a cloud computing test environment for the virtual network camera when the consistency coefficient meets a preset consistency threshold, which can facilitate the network connection stability test of the network camera; the present invention can identify the actual access mode of the physical network camera by identifying the actual access mode of the physical network camera, thereby providing a basis for the subsequent construction of a multimodal access data stream of the virtual network camera; optionally, the present invention constructs a multimodal access data stream of the virtual network camera based on the actual access mode to simulate the real The access requirements of real users to the network camera are used to effectively test and verify the virtual network camera. Furthermore, based on the access test data, the present invention uses a trained stability analysis model to analyze the network stability coefficient of the virtual network camera, which can analyze the network stability coefficient of the virtual network camera, thereby evaluating its stability under multimodal access data streams. Finally, based on the network stability coefficient, the present invention analyzes the network abnormal state of the physical network camera, wherein the abnormal state includes the actual network abnormal state and the analysis network abnormal state. The abnormal state of the physical network camera can be analyzed based on the network stability coefficient, and corresponding measures can be taken to ensure the stability and reliability of the network camera in actual deployment. Based on the network abnormal state, a stability analysis report of the physical network camera is constructed. A detailed physical network camera stability analysis report can be constructed to provide valuable reference for solving network abnormal states. Therefore, the network connection stability testing method based on the network camera proposed in the present invention can improve the testing effect of the network connection stability of the network camera.

[0148] like Figure 2 , is a functional module diagram of a network connection stability testing system based on a network camera provided in one embodiment of the present invention.

[0149] The network connection stability test system 200 based on a network camera of the present invention can be installed in an electronic device. According to the functions to be implemented, the network connection stability test system 200 based on a network camera can include a virtual camera construction module 201, a test environment configuration module 202, a virtual multimodal access module 203, a network anomaly analysis module 204 and an analysis report construction module 205. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0150] In this embodiment, the functions of each module / unit are as follows:

[0151] The virtual camera construction module 201 is used to obtain the software stack and network conditions of the physical network camera, construct a virtual network camera of the physical network camera based on the software stack and the network conditions, and calculate the consistency coefficient between the physical network camera and the virtual network camera;

[0152] The test environment configuration module 202 is used to construct a cloud computing test environment for the virtual network camera when the consistency coefficient meets a preset consistency threshold, and integrate the virtual network camera into the cloud computing test environment to obtain a virtual network camera to be tested;

[0153] The virtual multimodal access module 203 is used to identify the actual access mode of the physical network camera, construct a multimodal access data stream of the virtual network camera based on the actual access mode, and perform a virtual access to the virtual network camera based on the multimodal access data stream to obtain access test data of the virtual network camera;

[0154] The network anomaly analysis module 204 is used to analyze the network stability coefficient of the virtual network camera based on the access test data using the trained stability analysis model, and analyze the network anomaly state of the physical network camera based on the network stability coefficient, wherein the anomaly state includes the actual network anomaly state and the analyzed network anomaly state;

[0155] The analysis report building module 205 is used to build a stability analysis report of the physical network camera based on the network abnormal state.

[0156] In detail, the modules described in the network connection stability testing system 200 based on the network camera in the embodiment of the present invention adopt the same technical means as the network connection stability testing method based on the network camera described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0157] An embodiment of the present invention provides an electronic device for implementing a network connection stability testing method based on a network camera.

[0158] See also Figure 3 As shown, the electronic device may include a processor 30, a memory 31, a communication bus 32 and a communication interface 33, and may also include a computer program stored in the memory 31 and executable on the processor 30, such as a network connection stability testing method program based on a webcam.

[0159] In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory (for example, executing a network connection stability test program based on a webcam, etc.), and calls data stored in the memory to execute various functions of the electronic device and process data.

[0160] The memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory may be an internal storage unit of an electronic device in some embodiments, such as a mobile hard disk of the electronic device. The memory may also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory may also include both an internal storage unit of the electronic device and an external storage device. The memory may be used not only to store application software and various types of data installed in the electronic device, such as the code of a network connection stability test program based on a network camera, but also to temporarily store data that has been output or is to be output.

[0161] The communication bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory and at least one processor, etc.

[0162] The communication interface is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0163] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0164] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0165] The network connection stability test program based on the network camera stored in the memory of the electronic device is a combination of multiple instructions, and when running in the processor, it can achieve:

[0166] Acquire a software stack and a network condition of a physical network camera, construct a virtual network camera of the physical network camera based on the software stack and the network condition, and calculate a consistency coefficient between the physical network camera and the virtual network camera;

[0167] When the consistency coefficient meets the preset consistency threshold, constructing a cloud computing test environment for the virtual network camera, and integrating the virtual network camera into the cloud computing test environment to obtain a virtual network camera to be tested;

[0168] Identify the actual access mode of the physical network camera, construct a multimodal access data stream of the virtual network camera based on the actual access mode, perform a virtual access to the virtual network camera based on the multimodal access data stream, and obtain access test data of the virtual network camera;

[0169] Based on the access test data, the network stability coefficient of the virtual network camera is analyzed using a trained stability analysis model, and based on the network stability coefficient, the network abnormal state of the physical network camera is analyzed, wherein the abnormal state includes an actual network abnormal state and an analyzed network abnormal state;

[0170] Based on the abnormal network status, a stability analysis report of the physical network camera is constructed.

[0171] Specifically, the specific implementation method of the processor for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0172] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0173] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0174] Acquire a software stack and a network condition of a physical network camera, construct a virtual network camera of the physical network camera based on the software stack and the network condition, and calculate a consistency coefficient between the physical network camera and the virtual network camera;

[0175] When the consistency coefficient meets the preset consistency threshold, constructing a cloud computing test environment for the virtual network camera, and integrating the virtual network camera into the cloud computing test environment to obtain a virtual network camera to be tested;

[0176] Identify the actual access mode of the physical network camera, construct a multimodal access data stream of the virtual network camera based on the actual access mode, perform a virtual access to the virtual network camera based on the multimodal access data stream, and obtain access test data of the virtual network camera;

[0177] Based on the access test data, the network stability coefficient of the virtual network camera is analyzed using a trained stability analysis model, and based on the network stability coefficient, the network abnormal state of the physical network camera is analyzed, wherein the abnormal state includes an actual network abnormal state and an analyzed network abnormal state;

[0178] Based on the abnormal network status, a stability analysis report of the physical network camera is constructed.

[0179] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0180] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0181] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0182] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0183] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0184] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0185] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system claim can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A network connection stability testing method based on a network camera, characterized in that: The method comprises: Acquire a software stack and a network condition of a physical network camera, construct a virtual network camera of the physical network camera based on the software stack and the network condition, and calculate a consistency coefficient between the physical network camera and the virtual network camera; When the consistency coefficient meets the preset consistency threshold, constructing a cloud computing test environment for the virtual network camera, and integrating the virtual network camera into the cloud computing test environment to obtain a virtual network camera to be tested; Identify the actual access mode of the physical network camera, construct a multimodal access data stream of the virtual network camera based on the actual access mode, perform a virtual access to the virtual network camera based on the multimodal access data stream, and obtain access test data of the virtual network camera; Based on the access test data, the network stability coefficient of the virtual network camera is analyzed using a trained stability analysis model, and based on the network stability coefficient, the network abnormal state of the physical network camera is analyzed, wherein the abnormal state includes an actual network abnormal state and an analyzed network abnormal state; Based on the abnormal network status, a stability analysis report of the physical network camera is constructed.

2. The network connection stability testing method based on a network camera as claimed in claim 1, characterized in that: The step of constructing a virtual network camera of the physical network camera based on the software stack and the network condition includes: Selecting a virtualization platform for the physical network camera; Based on the software stack, construct a virtual camera template of the physical network camera using the virtualization platform; Based on the virtual camera template, deploy a virtual camera instance of the physical network camera; Based on the network condition, configuring a virtual network of the virtual camera instance; Based on the virtual network and the software stack, the parameters of the virtual camera instance are adjusted to obtain a virtual network camera of the physical network camera.

3. The network connection stability testing method based on a network camera as claimed in claim 1, characterized in that: The calculating the consistency coefficient between the physical network camera and the virtual network camera includes: Collecting physical camera data and virtual camera data of the physical network camera and the virtual network camera respectively; Constructing a physical level list and a virtual level list of the physical camera data and the virtual camera data respectively; Performing list matching on the physical level list and the virtual level list to obtain a matching list; Calculating a list level difference of the matching list; Based on the list level difference and the number of list matching pairs corresponding to the matching list, a consistency coefficient between the physical network camera and the virtual network camera is calculated.

4. The network connection stability testing method based on a network camera as claimed in claim 3, characterized in that: The calculating the consistency coefficient between the physical network camera and the virtual network camera based on the list level difference and the number of list matching pairs corresponding to the matching list includes: Calculating the degrees of freedom of the matching list based on the number of matching pairs in the list; Based on the list level difference and the degree of freedom, the consistency coefficient between the physical network camera and the virtual network camera is calculated using the following formula: Where τ represents the consistency coefficient between the physical webcam and the virtual webcam, S i It represents the absolute value of the difference between the i-th list level of the matching list corresponding to the physical network camera and the virtual network camera, Z represents the degree of freedom, and m represents the number of list matching pairs of the matching list corresponding to the physical network camera and the virtual network camera.

5. The network connection stability testing method based on a network camera as claimed in claim 1, characterized in that: The identifying the actual access mode of the physical network camera comprises: Collecting access data of the physical network camera; Preprocessing the access data to obtain preprocessed access data; identifying access behavior of the preprocessed access data; Analyzing the access association relationship of the access behavior; The actual access mode of the physical network camera is identified through the access association relationship.

6. The network connection stability testing method based on a network camera as claimed in claim 1, characterized in that: The step of constructing a multimodal access data stream of the virtual network camera based on the actual access mode includes: identifying access pattern characteristics of the actual access pattern; Based on the access mode characteristics, determining the access data flow template structure and access data flow template rules of the virtual network camera; Based on the access data flow template structure and the access data flow template rule, construct an access data flow template of the virtual network camera; Based on the access data stream template, a multimodal access data stream of the virtual network camera is generated.

7. The network connection stability testing method based on a network camera as claimed in claim 1, characterized in that: The analyzing the network stability coefficient of the virtual network camera using the trained stability analysis model based on the access test data includes: Identifying stable influencing factors of the access test data; Determining a factor quantization value of the stability influencing factor based on the access test data; Serializing the factor quantization value to obtain a serialized factor quantization value; Marking the time node of the quantized value of the serialization factor; Calculating the factor difference of the time node based on the standard factor value corresponding to the time node and the serialized factor quantization value; Based on the factor difference and the factor quantization value, the network stability coefficient of the virtual network camera is calculated using the stability analysis model.

8. The network connection stability testing method based on a network camera as claimed in claim 7, characterized in that: The calculating the network stability coefficient of the virtual network camera by using the stability analysis model based on the factor difference and the serialization factor quantization value includes: Identifying a stability impact factor corresponding to the virtual network camera and a stability impact correlation coefficient of the virtual network camera; Determining a stability impact factor weight of the stability impact factor based on the stability impact correlation coefficient; Based on the factor difference, the factor quantization value and the stability influencing factor weight, the network stability coefficient of the virtual network camera is calculated using the stability calculation function of the stability analysis model: Where ρ represents the network stability coefficient of the virtual network camera, E c represents the factor quantization value of the cth stable influencing factor of the virtual network camera, D c represents the factor difference of the cth stable influencing factor of the virtual network camera, J c represents the stable influence factor weight of the cth stable influence factor of the virtual network camera, ∈ represents the network accident coefficient, and r represents the number of stable influence factors corresponding to the virtual network camera.

9. The network connection stability testing method based on a network camera as claimed in claim 1, characterized in that: The step of constructing a stability analysis report of the physical network camera based on the abnormal network state includes: Analyze the network abnormality characteristics and network abnormality level of the network abnormality state; Based on the network abnormality feature, locate the network abnormality cause of the physical network camera; Based on the cause of the network anomaly, analyzing the network stabilization strategy of the physical network camera; Based on the network anomaly level, the network anomaly cause and the network stability strategy, a stability analysis report of the physical network camera is constructed.

10. A network connection stability testing system based on a network camera, characterized in that: The system is used to perform the network connection stability testing method based on a network camera as described in any one of claims 1 to 9, and comprises: A virtual camera construction module, used to obtain a software stack and a network condition of a physical network camera, construct a virtual network camera of the physical network camera based on the software stack and the network condition, and calculate a consistency coefficient between the physical network camera and the virtual network camera; A test environment configuration module, used for constructing a cloud computing test environment for the virtual network camera when the consistency coefficient meets a preset consistency threshold, and integrating the virtual network camera into the cloud computing test environment to obtain a virtual network camera to be tested; A virtual multimodal access module, used to identify the actual access mode of the physical network camera, construct a multimodal access data stream of the virtual network camera based on the actual access mode, and perform a virtual access to the virtual network camera based on the multimodal access data stream to obtain access test data of the virtual network camera; A network anomaly analysis module, used to analyze the network stability coefficient of the virtual network camera based on the access test data using a trained stability analysis model, and to analyze the network anomaly state of the physical network camera based on the network stability coefficient, wherein the anomaly state includes an actual network anomaly state and an analyzed network anomaly state; An analysis report building module is used to build a stability analysis report of the physical network camera based on the abnormal network state.

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