Network remote monitoring method and device, equipment and storage medium

Abstract

The invention discloses a network remote monitoring method, device and equipment and a storage medium, and the method comprises the steps: collecting the network state data of remote equipment through a network probe, and enabling the network state data to comprise network flow data, network equipment state information and operator network parameters; calculating a network fluctuation coefficient of the remote equipment according to the network state data, and calculating an optimized acquisition frequency of the network probe based on a preset acquisition frequency of the network probe and the network fluctuation coefficient; and acquiring network state data of the remote equipment at the optimized acquisition frequency by using the network probe, and controlling the network probe to transmit the network state data to a data center. According to the scheme, the collection frequency is dynamically optimized according to the network state, the efficiency and sensitivity of data collection are improved, and accurate perception and regional differentiation processing of network anomaly are achieved through calculation of the network fluctuation coefficient.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a network remote monitoring method, apparatus, device and storage medium. Background Art

[0002] With the continuous advancement of globalization, more and more companies are expanding their operations to multiple regions overseas. However, this process presents numerous challenges in network connectivity and remote communications. On the one hand, network infrastructure varies significantly across regions, leading to inconsistent network quality and frequent network fluctuations. On the other hand, the network service stability provided by local operators varies, often resulting in connection interruptions, data loss, access delays, and other issues.

[0003] Existing network monitoring methods mostly rely on centralized data collection and unified rules, making it difficult to flexibly adjust according to regional characteristics, and unable to achieve real-time and accurate perception of changes in network status. When a network failure occurs, there is often a lack of the ability to quickly locate the location and cause of the problem, resulting in a long troubleshooting cycle and delayed response. Due to the lack of an effective global localized network monitoring mechanism, once companies encounter network problems during overseas operations, they are often unable to deal with them in a timely manner, which in turn causes business interruptions, customer loss, and even economic losses. Therefore, there is an urgent need for a remote network monitoring method and system that is oriented towards the differences in network environments in multiple regions and has dynamic self-adaptation capabilities to achieve efficient collection, real-time analysis, and rapid response to network status. Summary of the Invention

[0004] The present application provides a network remote monitoring method, apparatus, equipment and storage medium, which collects real-time network status of remote devices by deploying network probes, dynamically calculates the network fluctuation coefficient by combining network traffic data, network device status information and operator network parameters, and adjusts the collection frequency based on the network fluctuation coefficient, thereby achieving accurate monitoring and flexible regulation of network status.

[0005] In a first aspect, the present application provides a network remote monitoring method, comprising: Using a network probe to collect network status data of a remote device, the network status data includes network traffic data, network device status information, and operator network parameters; Calculating a network fluctuation coefficient of the remote device according to the network status data, and calculating an optimized collection frequency of the network probe based on a preset collection frequency of the network probe and the network fluctuation coefficient; The network status data of the remote device is collected using the network probe at the optimized collection frequency, and the network probe is controlled to transmit the network status data to a data center for the data center to perform visual display of the network status data.

[0006] In a second aspect, the present application provides a network remote monitoring device, comprising: A network status collection module is used to collect network status data of remote devices using network probes. The network status data includes network traffic data, network device status information, and operator network parameters. an acquisition frequency calculation module, configured to calculate a network fluctuation coefficient of the remote device according to the network status data, and calculate an optimized acquisition frequency of the network probe based on a preset acquisition frequency of the network probe and the network fluctuation coefficient; The status data transmission module is used to use the network probe to collect the network status data of the remote device at the optimized collection frequency, control the network probe to transmit the network status data to the data center, and use the data center to visualize the network status data.

[0007] In a third aspect, the present application provides a network remote monitoring device, comprising: one or more processors; The memory stores one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the network remote monitoring method as described in the first aspect.

[0008] In a fourth aspect, the present application provides a storage medium comprising computer-executable instructions, which, when executed by a computer processor, are used to execute the network remote monitoring method as described in the first aspect.

[0009] In this application, network status data of remote devices is collected by network probes deployed on remote network paths. The network fluctuation coefficient of the target device is calculated based on the collected network status data, and the optimized collection frequency is calculated based on the network fluctuation coefficient. The network probes are then controlled to collect updated network status data according to the optimized collection frequency, and the collected data is transmitted to the back-end data center. Through the above solution, dynamic perception and intelligent adjustment of network status can be achieved, improving the real-time and accuracy of network status monitoring, reducing data delays or resource waste caused by unreasonable collection frequency settings, and thus enhancing the adaptability and operation and maintenance efficiency of the network monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a flow chart of a network remote monitoring method provided by an embodiment of the present application; Figure 2 This is a flowchart of the initial collection of network status data provided by an embodiment of the present application; Figure 3 This is a flow chart of network fluctuation coefficient calculation provided by an embodiment of the present application; Figure 4 This is a flowchart of network status data transmission provided by an embodiment of the present application; Figure 5 This is a flow chart of network status data transmission content control provided by an embodiment of the present application; Figure 6 This is a flowchart of network status data transmission content processing provided by an embodiment of the present application; Figure 7 This is a flowchart of network status data visualization provided by an embodiment of the present application; Figure 8 This is a structural diagram of a network remote monitoring device provided by an embodiment of the present application; Figure 9 This is a structural diagram of a network remote monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] To further clarify the objectives, technical solutions, and advantages of this application, specific embodiments of this application are described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are intended only to illustrate this application and are not intended to limit it. It should also be noted that, for ease of description, the drawings only illustrate portions relevant to this application, not all of them. Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts depict the operations (or steps) as being performed sequentially, many of the operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. A process may terminate upon completion of its operations, but may also have additional steps not shown in the accompanying drawings. A process may correspond to a method, function, procedure, subroutine, subprogram, and the like.

[0012] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the data used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than those illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects connected before and after are in an "or" relationship.

[0013] With the accelerating pace of globalization, more and more companies are actively expanding into overseas markets, developing cross-regional network services and conducting remote business operations. However, significant differences exist in network infrastructure across countries and regions, and network quality varies widely across regions, particularly in bandwidth, latency, packet loss, and stability. These disparities lead to frequent network fluctuations and unstable connections, posing significant challenges to companies' global business deployment and operations.

[0014] In the more common existing implementation methods, due to the varying quality of network services provided by operators across the globe, enterprises often encounter frequent network failures during remote device connections, such as abnormal latency, frequent disconnections, and disconnections. Existing network monitoring methods primarily rely on a centralized, unified detection model, making it difficult to timely perceive the real-time status of specific regional or local networks. They also lack the ability to accurately locate network failures, resulting in an inability to quickly respond and troubleshoot network anomalies. In the absence of an effective global and localized network monitoring mechanism, network problems faced by enterprises are not only difficult to detect in a timely manner, but also difficult to pinpoint the specific location and cause of the failure. This, in turn, impacts remote service quality, delays business processing, and results in a certain degree of economic loss and decreased customer satisfaction.

[0015] Therefore, it is urgent to propose a remote network monitoring method and system that can adapt to the differences in the global network environment, have real-time data collection, dynamically adjust collection strategies, and support data visualization and analysis capabilities, so as to achieve localized monitoring of network status around the world, quickly locate and handle faults, and thus improve the continuity and stability of enterprises' overseas business.

[0016] To address the aforementioned issues, this embodiment provides a network remote monitoring method. This method aims to improve the adaptability and flexibility of the network monitoring system to regional network environment variations, enabling stable operation in a volatile and complex global network environment. Furthermore, it enhances the real-time and accuracy of network fault early warning mechanisms, enabling earlier detection of potential anomalies and assisting in rapid locating the source of faults, thereby significantly reducing the negative impact of network anomalies on business continuity and service quality.

[0017] The network remote monitoring method provided in this embodiment can be executed by a network remote monitoring device. The network remote monitoring device can be implemented through software and / or hardware. The network remote monitoring device can be composed of two or more physical entities, or a single physical entity. For example, the network remote monitoring device can be an operation and maintenance server used to maintain the normal operation of a business.

[0018] The network remote monitoring device is installed with at least one operating system, including but not limited to Android, Linux, and Windows. The network remote monitoring device can install at least one application based on the operating system. The application can be a native application of the operating system or an application downloaded from a third-party device or server. In this embodiment, the network remote monitoring device has at least one application capable of executing the network remote monitoring method.

[0019] For ease of understanding, this embodiment is described by taking an operation and maintenance server as an example of the main body for executing the network remote monitoring method.

[0020] Figure 1 A flowchart of a network remote monitoring method provided by an embodiment of the present application is given. Figure 1 , the network remote monitoring method specifically includes: S110 : Using a network probe to collect network status data of a remote device, the network status data includes network traffic data, network device status information, and operator network parameters.

[0021] In this embodiment, in this step, network status data of the remote device is collected through a network probe. The types of network status data include network traffic data, network device status information, and operator network parameters. Through the network status data, the network status of the remote device can be fully understood, and strong support can be provided for subsequent optimization and fault diagnosis. Network traffic data includes parameters such as data packet size, transmission rate, delay, packet loss rate, etc., which reflect the load and performance of data transmission in the network. The collected network traffic data can also include inbound traffic, outbound traffic, packet loss rate, round-trip delay, and bandwidth utilization. Network device status information refers to various types of data related to the hardware, operating system, and software status of the remote device, which can provide the health status of the device itself. Device status information includes CPU occupancy, memory usage, network interface status, device temperature, and hard disk status. CPU usage is the utilization rate of the remote device's CPU, reflecting the load on computing resources. Memory usage is memory usage, used to determine whether the device has memory bottlenecks. Network interface status indicates the enabled state of the network interface, indicating whether it is faulty or disconnected. Device temperature is the temperature of the remote device; excessively high temperatures may indicate hardware failure. Hard disk status indicates the health of the hard disk, particularly the write lifespan of the SSD. Carrier network parameters provide data related to network service providers, helping to analyze network performance at the carrier level. Carrier network parameters include signal strength, signal quality, and frequency switching. Deploy network probes between remote devices and network devices to ensure they can collect network traffic in real time and monitor network status. The probes collect traffic data, device status information, and carrier parameters in real time and store them on designated servers or cloud databases for further analysis. The collected network status data is transmitted to a central management system via a secure channel for subsequent data processing, analysis, and decision-making. The collected data is formatted and standardized to ensure seamless integration of different types of data for further analysis. By analyzing traffic data and device status information, network bottlenecks can be identified, resource allocation can be optimized, and overall system performance can be improved. By monitoring operator network parameters and device health, faults can be detected in real time and rapid remediation measures can be implemented to avoid system downtime. The stability, speed, and health of remote devices and network connections can be regularly assessed to help operators and managers make decisions.

[0022] Optionally, Figure 2 This is a flow chart of the initial collection of network status data provided by the embodiment of this application. Figure 2 As shown, the steps of initial collection of network status data specifically include S1101-S1103: S1101. Obtain historical network status data of the remote device, and calculate a historical network fluctuation coefficient of the remote device based on the historical network status data.

[0023] For example, historical network status data for the remote device within a preset time range is retrieved from local storage or a remote database. This historical network status data includes network traffic data, network device status information, and operator network parameters, along with metadata such as data collection timestamps, collection frequency, and abnormal event identifiers. After extracting the historical data, the data is formatted, missing values ​​are filled, outliers are removed, and noise is smoothed to ensure that the data quality meets the requirements for subsequent analysis. Fluctuation analysis is performed on each dimensional indicator in the historical network status data. The time series volatility of indicators such as packet loss rate, round-trip latency, and bandwidth utilization change rate is analyzed; the statistical fluctuation amplitude of historical CPU utilization, device temperature, and memory usage is analyzed; and the temporal trends and abnormal jumps of signal strength, signal quality, and frequency band switching frequency are analyzed. Each dimensional indicator is normalized to a uniform dimension, and statistical methods such as weighted sliding variance, range, and coefficient of variation are used to measure the degree of fluctuation in each dimension. The system inputs these multiple dimensional volatility indicators into a comprehensive evaluation model and constructs a historical network fluctuation coefficient for the remote device using a weighted fusion or multi-factor clustering algorithm. This coefficient quantifies the stability of a device's network status over a specific time period. The weighting coefficient can be optimized based on historical event response records, such as abnormal alarms and fault recovery times, making the model more adaptable. The calculated historical network fluctuation coefficient is output as a time series and stored in an analysis database, providing supporting data for subsequent modules such as optimized acquisition frequency calculation, trend forecasting, and dynamic resource scheduling.

[0024] S1102: Calculate the initial collection frequency of the network probe according to the preset collection frequency of the network probe and the historical network fluctuation coefficient.

[0025] For example, the network probe first reads the preset collection frequency set during factory configuration or initial deployment. This frequency represents the interval at which the network probe performs periodic collections before any optimization or adjustments are made. This preset collection frequency is typically set based on the data change rate in typical scenarios, but in actual applications, it needs to be adaptively adjusted based on the historical network status of the target device. To ensure that the collection frequency matches the degree of fluctuation in the device's network status, a mapping relationship is constructed from the historical fluctuation coefficient to the frequency. This mapping model is implemented using interval mapping functions, linear interpolation, piecewise polynomial fitting, or a rule-based decision matrix. When the historical fluctuation coefficient is high, the mapping model increases the collection frequency to enhance the real-time and granularity of status awareness; when the historical fluctuation coefficient is low, the collection frequency is appropriately reduced to reduce network load and system resource consumption. This mapping relationship includes multiple adjustment factors, such as the minimum frequency threshold, maximum frequency upper limit, change step size, and smoothing adjustment coefficient, to ensure a stable, reliable, and dynamically controllable collection strategy. The historical network fluctuation coefficient is used as input and substituted into the mapping model to obtain the initial collection frequency for the current network probe. This initial collection frequency serves as a reference sampling rate before the network probe enters the actual operation phase. If there are multiple historical fluctuation coefficients, a comprehensive fluctuation index can be generated through weighted average, maximum criterion or trend extrapolation method to further improve the representativeness of the initial frequency. After the initial frequency is determined, the system verifies its boundaries to ensure that it meets the minimum sampling period limit and frequency mutation protection mechanism defined by the system. If it is detected that the frequency jump amplitude exceeds the threshold, the system can automatically apply a smooth transition strategy, such as using an exponentially weighted sliding average to smoothly adjust the frequency change to avoid causing instantaneous jitter of the device load or excessive network pressure. Through the above process, the network probe can obtain a collection frequency configuration that is more in line with the historical status characteristics of the target device at the beginning of operation, thereby reducing system resource consumption while ensuring the timeliness and integrity of key network status data.

[0026] S1103: Use the network probe to collect network status data of the remote device at the initial collection frequency.

[0027] For example, after receiving the initial collection frequency, the network probe updates the timers, trigger thresholds, and cyclic task tables related to sampling scheduling in its internal scheduling module to ensure that the data collection process is strictly executed at the newly set frequency. The starting time point of the collection cycle can be timestamped and aligned with the system clock of the remote device to achieve cross-system data synchronization and correlation analysis. The network probe activates the network interface monitoring module and periodically captures the network status data of the remote device at the set frequency. The collected network status data includes network traffic data, such as the send / receive packet rate, packet loss event count, network throughput rate, etc.; network device status information, such as system operating parameters such as CPU occupancy, memory usage, and device temperature; and operator network parameters, such as signal strength, signal-to-noise ratio, frequency of frequency band switching, and other communication indicators. The collection module has multi-threaded parallel processing capabilities and can schedule collection tasks for different data dimensions, improving collection efficiency and timeliness. After each collection cycle, the probe automatically performs data integrity verification on the acquired data. Data integrity verification includes field format verification, reasonableness judgment of numerical intervals, and identification and marking of outliers. At the same time, the acquisition module can also perform basic pre-processing operations as needed, such as sliding window averaging, mutation value masking, unit normalization conversion, etc., to improve subsequent processing efficiency and data availability. The data that passes the verification will be stored in the local buffer, and a timestamp and device unique identification code will be added to form a structured data packet to prepare for subsequent data upload, calculation and visual analysis. If multiple acquisition channels are enabled at the same time, the probe can package and aggregate the data stream or cache the channel classification to achieve bandwidth control and balanced management of data packet size. Through this process, the network probe can achieve high-reliability data collection in the initial stage, form a baseline data set, and support the dynamic optimization and adjustment of the subsequent sampling frequency and network status trend modeling and analysis.

[0028] S120: Calculate a network fluctuation coefficient of the remote device according to the network status data, and calculate an optimized collection frequency of the network probe based on a preset collection frequency of the network probe and the network fluctuation coefficient.

[0029] In this embodiment, the network fluctuation coefficient is used to measure the degree of dynamic change in the current network environment of the remote device. Its calculation is based on three core parameter dimensions in the network status data: traffic fluctuation indicators calculated based on network traffic data, such as packet loss rate, round-trip delay, and bandwidth usage change rate; device operation status indicators evaluated based on network device status information, such as CPU usage, temperature, memory usage, etc.; Link stability indicators based on operator network parameter assessments, such as RSRP, RSRQ, and frequency switching frequency, are used. By normalizing and weighting multiple heterogeneous indicators, a sliding window mean, standard deviation, and mutation detection algorithm are introduced to construct a composite fluctuation measurement model that reflects network state continuity and disturbance intensity. This model outputs a dynamically updated floating-point value, defined as the network fluctuation coefficient, ranging from 0 to 1. Higher values ​​indicate more unstable or drastic network fluctuations. The preset collection frequency refers to the standard sampling frequency used by network probes under default stable network conditions, balancing collection accuracy and system resource overhead. To address real-time changes in network status, the collection frequency is optimized using a dynamic adjustment strategy. When the fluctuation coefficient exceeds a specified upper threshold, the system enters high-frequency monitoring mode, automatically increasing the collection frequency to quickly respond to transient fluctuations or sudden network anomalies. When the fluctuation coefficient falls below a specified lower threshold, the system enters low-frequency energy-saving mode, proactively reducing the collection frequency to avoid redundant use of computing resources and network bandwidth. The calculation formula for the optimized collection frequency is:

[0030] in, To optimize the acquisition frequency; To preset the basic collection frequency; To adjust the sensitivity coefficient, it can be set by system configuration; is the network volatility coefficient.

[0031] To further enhance the intelligence of network perception, certain embodiments incorporate the following auxiliary factors into the calculation process for optimizing the acquisition frequency: current device load, such as processor utilization or I / O load; sensitivity classification of network protocol types; application-layer tolerance for sampling latency; and historical fluctuation trends and periodic pattern recognition. Through this dynamic frequency control mechanism, network probes can achieve on-demand resource scheduling and precise sampling while ensuring data integrity, thereby improving responsiveness to abnormal network conditions and overall system efficiency.

[0032] Optionally, Figure 3 This is a flow chart of network fluctuation coefficient calculation provided by the embodiment of this application. Figure 3 As shown, the steps of calculating the network fluctuation coefficient specifically include S1201-S1204: S1201. Calculate the packet loss rate, round-trip delay, and bandwidth occupancy change rate of the remote device based on the network traffic data, and obtain the traffic fluctuation coefficient of the remote device based on the packet loss rate, the round-trip delay, and the bandwidth occupancy change rate.

[0033] Exemplarily, the packet loss rate, round-trip delay, and bandwidth occupancy change rate of the remote device are calculated based on the network traffic data, and a multi-dimensional volatility assessment model is constructed based on this, and the traffic fluctuation coefficient is output to measure the stability and dynamic change degree of the network transmission link. Among them, the packet loss rate refers to the proportional difference between the actual number of data packets received and the theoretical number of data packets sent per unit time, reflecting the data reliability of the transmission link; the round-trip delay refers to the total delay experienced by the data packet from the remote device to the response received, reflecting the network response performance; the bandwidth occupancy change rate refers to the relative change in bandwidth utilization per unit time, reflecting the volatility and congestion of bandwidth resources. In the specific calculation process, the window sliding statistics method is first used to extract the sampling sequence of the above three indicators in a continuous time period, and then the standard deviation, skewness and kurtosis analysis of each sequence is performed to identify potential mutation points and fluctuation trends. In order to enhance the adaptability of the model, a dynamic smoothing factor based on the exponentially weighted sliding average is introduced to reduce the interference of instantaneous spikes on the volatility assessment results. Finally, the traffic fluctuation coefficient is calculated using the following formula

[0034] in, is the variation of packet loss rate in the current window; is the dynamic standard deviation of the round-trip delay; is the bandwidth occupancy change rate; , , The weight coefficient corresponding to each indicator supports static configuration or adaptive adjustment based on the application scenario.

[0035] The flow fluctuation coefficient range is A higher value indicates worse network transmission link stability and greater fluctuations. This coefficient will serve as a key factor in optimizing the collection frequency, scheduling reporting cadence, or enabling exception handling logic.

[0036] S1202: Extract the CPU occupancy rate, device temperature, and memory usage rate of the remote device from the network device status information, and obtain a device status coefficient of the remote device based on the CPU occupancy rate, the device temperature, and the memory usage rate.

[0037] For example, the CPU occupancy rate, core temperature and memory usage rate of the remote device are extracted from the network device status information to construct a device operation load modeling index system, and the device status coefficient is calculated based on this to quantify the resource utilization intensity and operation stability of the remote device. Among them, the CPU occupancy rate reflects the activity level of the processing unit's current processing tasks, and is the core indicator for measuring the computing load of the device; the device temperature mainly refers to the operating temperature level of the core area of ​​the main control chip, which is a direct reflection of the combined influence of the operating environment and load intensity; the memory usage rate indicates the proportion of the system's allocated memory, and is related to the device's concurrent processing capabilities and resource scheduling efficiency for complex tasks. First, a periodic sampling method is used to construct a time series data set of the above three types of parameters, and sliding window averaging and median filtering techniques are used for denoising to ensure the stability and anti-interference ability of the input data. Subsequently, normalization is performed on each parameter, and its value is mapped to To improve the state coefficient's ability to respond to sudden anomalies, an operation level assessment model based on a discriminant tree is introduced to enhance the weight of high-risk states such as CPU usage exceeding 80%, core temperature exceeding the warning threshold, and memory usage approaching saturation, thus forming a differentiated weighting mechanism. The calculation model is defined as follows:

[0038] in, Indicates the standardized index value; , , The weighted coefficients for CPU usage, device temperature, and memory usage in the comprehensive evaluation are respectively, and can be dynamically configured based on the device type or deployment scenario. is the CPU usage, is the device temperature, is the memory usage, The value range is A higher value indicates greater resource load pressure on the device and a higher risk of potential instability. The device status coefficient can serve as a core criterion in network probe collection strategies, fault warning mechanisms, and service quality assessment processes, providing fundamental support for resource-aware network performance control strategies.

[0039] S1203: Extract the signal strength and signal quality received by the remote device and the frequency band switching frequency of the remote device from the operator network parameters, and obtain an operator network coefficient based on the signal strength, the signal quality and the frequency band switching frequency.

[0040] For example, the signal strength, signal quality index and frequency switching frequency received by the remote device are extracted from the operator network parameters to construct an operator-side wireless link stability evaluation model, and the operator network coefficient is calculated based on the model to reflect the stability of the external communication environment and the quality fluctuation trend. Among them, the signal strength reflects the receiving power level at the location of the device and is a basic indicator of wireless coverage quality; the signal quality reflects the anti-interference ability of the link and directly affects the data integrity and throughput of the uplink and downlink; the frequency switching frequency characterizes the frequency domain switching behavior under the network scheduling strategy. Frequent switching may indicate problems such as the edge status of the macro cell, load balancing fluctuations or interference with neighboring cell coverage. The periodically recorded RSSI, SINR or RSRQ and frequency band switching event logs are obtained from the operator network acquisition module. The time series data is smoothed using an exponentially weighted moving average to filter out occasional spike noise and improve the stability of the link quality trend assessment. The physical layer parameters such as RSSI and SINR are normalized according to the standard communication performance level classification model and mapped to Numerical range; in extreme deterioration scenarios such as signal strength below -95dBm or SINR below 5dB, an exponential weighting method is used to amplify the risk and enhance the model's sensitivity to abnormally weak coverage. The number of frequency band switches per unit time is counted to construct a switching frequency vector; for situations where the switching frequency exceeds twice the historical median, a "burst frequency hopping" factor correction mechanism is introduced to improve the model's ability to characterize network instability events. Finally, the operator network coefficient The comprehensive calculation model is as follows:

[0041] in: is the standardization processing function; , , The weight parameters of each indicator can be adaptively adjusted according to the differences in the communication environment or business needs of the deployment area; is the exponential measurement function of the frequency of band switching; The value range is The higher the value, the more unstable the current operator's network environment is.

[0042] and The calculation formula is as follows:

[0043] in, This is the normal threshold for frequency switching, which is usually determined by the operator's network design or industry standards. A switching frequency above this threshold will have a significant negative impact. This is an exponential decay factor that determines the impact of band switching frequency exceeding a threshold on network stability. This factor can be adjusted based on different scenarios and environments. If the band switching frequency is below the threshold, this factor is not weighted. Otherwise, as the band switching frequency increases, the penalty factor gradually increases, indicating that network performance during band scheduling is degraded.

[0044] Operator network parameters, as an important component of network fluctuation assessment, provide a basic judgment basis for network probe frequency adjustment, QoS dynamic management, and edge computing task scheduling, and help build an intelligent network optimization framework based on environmental perception.

[0045] S1204: Calculate the network fluctuation coefficient of the remote device according to the traffic fluctuation coefficient, the device status coefficient, and the operator network coefficient.

[0046] Exemplarily, based on the traffic fluctuation coefficient, device status coefficient and operator network coefficient, the network fluctuation coefficient of the remote device is comprehensively evaluated and calculated to fully reflect its performance fluctuation risk in the current network environment. Based on the influence weights of the traffic fluctuation coefficient, device status coefficient and operator network coefficient, the network fluctuation coefficient is calculated by weighted summation. The various coefficients have been standardized in the previous steps, so there is no need to repeat the normalization. For each coefficient, an appropriate weight factor is assigned according to its importance in affecting the performance of the remote device. These weights can be determined through expert experience or machine learning methods. Assuming that the traffic fluctuation coefficient has a greater impact on device performance, and the device status coefficient and operator network coefficient have a smaller impact, the following weights can be assigned:

[0047] Thus, the network fluctuation coefficient The comprehensive calculation formula is:

[0048] Calculated network fluctuation coefficient Represents the comprehensive volatility of the remote device in the current network environment. If the coefficient is high, it means that the network environment faced by the device is unstable and the risk of performance fluctuation is high; if the coefficient is low, it means that the network environment is stable and the device performance is less affected by external fluctuations. , which can evaluate the performance fluctuations of the device in the current network environment, and then dynamically adjust the collection frequency of network probes, optimize network resource scheduling, adjust service quality strategies, etc.

[0049] S130: Utilize the network probe to collect network status data of the remote device at the optimized collection frequency, and control the network probe to transmit the network status data to a data center, so that the data center can visualize the network status data.

[0050] In this embodiment, a network probe collects network status data from remote devices at an optimized collection frequency. The probe is then controlled to transmit the data to a data center for visualization. In the previous steps, the optimized collection frequency was calculated based on factors such as the network fluctuation coefficient. This frequency is dynamically adjusted based on the actual network status of the remote device, device load, and the carrier network conditions to ensure that the network probe neither wastes resources when collecting data nor misses critical network status changes due to a low collection frequency. The optimized collection frequency is determined by an algorithmic model. In high-volatility environments, the collection frequency is increased to obtain more network status data in real time, ensuring that the data fully reflects network fluctuations. In low-volatility environments, the collection frequency is reduced to reduce resource consumption on the network probe and the remote device while ensuring that the collected data remains representative. The network probe periodically collects network status data from the remote device based on the calculated optimized collection frequency. Network status data includes, but is not limited to, network traffic data, device status data such as CPU utilization and memory usage, and carrier network parameters such as signal strength and quality. This data comprehensively reflects the device's operating status in the current network environment. During the data collection process, network probes must possess efficient filtering and screening capabilities to ensure that only the most critical data that impacts network status is collected. Furthermore, probes must respond in real time to sudden changes in network conditions and dynamically adjust their collection strategies based on the frequency of collection. Data collected by network probes is transmitted in real time to the data center via efficient communication protocols. The data center receives and stores this network status data for subsequent processing. To prevent data loss during data transmission, network probes implement appropriate retransmission mechanisms and error detection features in the transmission protocol to ensure that each data collection is successfully uploaded to the data center. Once the data arrives at the data center, the back-end processing system analyzes, processes, and integrates this raw data into a visual display. For example, the network status of remote devices can be displayed through charts, heat maps, and dashboards, helping operations personnel monitor network health in real time. Data visualization can display key metrics such as the current network load, latency, and packet loss rate of remote devices, facilitating the timely identification of network performance bottlenecks and potential failures. Accumulated data over time can be used for trend analysis, helping operations personnel understand network performance trends and conduct early warning and capacity planning. The in-depth information provided by data visualization can help technicians quickly locate the source of problems when they occur, shortening response time. Data visualization systems can be combined with intelligent analysis modules to provide intelligent alarms and decision support based on real-time data through data mining and machine learning technologies. The system can automatically trigger alarms based on abnormal fluctuations in network status data or make automated decisions based on pre-set rules.

[0051] Optionally, Figure 4 This is a flow chart of network status data transmission provided by the embodiment of the present application. Figure 4 As shown, the network status data transmission steps specifically include S1301-S1302: S1301: Obtain a preset transmission frequency of the network probe, and calculate an optimized transmission frequency of the network probe based on the preset transmission frequency and the network fluctuation coefficient.

[0052] For example, the network probe's communication scheduling module loads the preset transmission frequency stored in the default configuration file. This preset transmission frequency represents the base frequency for data uploads to the data center when the network status is stable or uncertain. The transmission frequency is typically measured in times / minute or packets / second and is dynamically reloadable, supporting remote delivery and local reconfiguration. Based on the network fluctuation coefficient updated in real time during the current acquisition cycle, the scheduling unit invokes a transmission frequency mapping function to establish a control model between fluctuation intensity and data transmission requirements. After initially calculating the optimized transmission frequency, it further performs boundary condition assessment and correction to ensure that the frequency value falls within the protocol requirements or the acceptable range of physical resources. A minimum transmission frequency limit ensures uninterrupted basic visualization information; a maximum transmission frequency limit prevents excessive data uploads from causing network congestion or cloud overload; and an adjustable granularity limit discretizes the frequency step size based on the system clock step size and buffer capacity. The optimized frequency parameters, after boundary correction, are written to the network probe's transmission control queue and the associated scheduling task table and timer configuration are updated. The system also triggers a status update event and records the estimated expected data throughput at the new frequency into the status monitoring module, providing an auxiliary reference for subsequent load balancing or multi-device synchronization scheduling.

[0053] S1302: Control the network probe to transmit the network status data to the data center at the optimized transmission frequency.

[0054] For example, after receiving the new optimized transmission frequency parameters, the network probe loads the task trigger period corresponding to this frequency through the scheduling controller and reconstructs the data reporting schedule. The scheduler uses a combination of event-driven and timed triggering to ensure that data transmission is executed stably and at an optimal pace within the resource scheduling cycle. During each collection cycle, the network probe's built-in data buffer management module structures and caches network status data collected based on the optimized collection frequency. It also performs data consistency checks, deduplication, and variable field extraction. It prioritizes differential transmission data packets, including only data fields with significant deviations from the previous transmission cycle or policy flag changes in the current reporting task, effectively reducing data redundancy. After data packet construction is complete, the network transmission module performs frame format encapsulation and link status determination based on data center communication protocols such as MQTT, HTTPS, and WebSocket. If the current link lacks upload capability, such as due to severe packet loss, excessive latency, or network outage, the cache delay upload mechanism is implemented, data integrity prediction, fragment reconstruction plan generation, and alert triggering, leading to a low-frequency retry state. The network status data is reported to the data center through the transmission channel. After the data center feeds back an ACK response or status code, the probe confirms that the data transmission is successful and updates the local data buffer index and status table. If no confirmation response is received within the timeout period, the system will re-upload according to the failure retransmission strategy and dynamically adjust the subsequent frequency to avoid persistent network conflicts. During the execution of the upload task, the probe will continuously monitor the link bandwidth occupancy rate, round-trip delay and retransmission rate, and use the monitoring results as link load feedback factors, and periodically synchronize them to the data center scheduling platform. This feedback information will be used to optimize the frequency calculation, transmission window allocation and inter-node collaborative scheduling strategy for future tasks. Through the above mechanism, the network probe can dynamically control the transmission behavior based on the frequency optimization strategy while ensuring the real-time visualization needs of the data center, realizing a bandwidth-friendly, state-aware data reporting mechanism, which is suitable for typical scenarios with multiple device access and frequent network environment fluctuations.

[0055] Optionally, Figure 5 This is a flow chart of network status data transmission content control provided by the embodiment of the present application. Figure 5 As shown, the steps of controlling the content of the network status data transmission specifically include S1303-S1305: S1303: When the network fluctuation coefficient is less than a first fluctuation threshold, control the network probe to transmit the network status data to a data center.

[0056] For example, the network probe periodically evaluates the current network fluctuation coefficient and compares it with a preset first fluctuation threshold. When the network fluctuation coefficient falls below the threshold, the network status is determined to have entered a stable range, ensuring sufficient bandwidth and transmission reliability, thereby triggering a complete data transmission strategy. Network status data collected by the network probe within the stable range is no longer simplified or delayed, but is instead packaged at its original granularity. Transmitted data includes full network traffic data, such as per-port and per-protocol traffic; complete network device status information, such as CPU, memory, and temperature, updated periodically; and full-parameter carrier network metrics, such as raw signal strength, quality, and frequency band change records. The probe enables parallel data upload channels and adopts a block-based data transmission protocol to increase upload speeds under good link conditions. It also ensures that all transmitted data types maintain timestamp alignment to meet the data center's requirements for high-precision network status visualization. The network probe uses a heartbeat mechanism to notify the data center in real time that it has entered stable data upload mode. After receiving the complete network status data, the data center automatically increases the chart refresh rate and diagnostic analysis depth for the corresponding remote device, enhancing real-time visualization capabilities and predictive accuracy. While the network remains stable, the probe continuously monitors the fluctuation coefficient and transmission success rate. If the fluctuation coefficient exceeds the first fluctuation threshold or the link latency increases significantly, the probe immediately terminates the full data upload and switches back to compressed or triggered upload mode to avoid link congestion and data accumulation. By controlling data transmission, the probe can proactively transmit complete network data when the network is stable, improving the data center's ability to restore remote device status and the accuracy of visual analysis, while also balancing system resource efficiency and real-time performance.

[0057] Optionally, Figure 6 This is a flowchart of network status data transmission content processing provided by the embodiment of the present application. Figure 6 As shown, the steps of processing the network status data transmission content specifically include S13031-S13032: S13031. When the network fluctuation coefficient is less than a third fluctuation threshold, control the network probe to transmit the network status data to a data center.

[0058] Exemplarily, when the network fluctuation coefficient is lower than the third fluctuation threshold, the network probe is controlled to upload the network status data to the data center in a complete content transmission mode. The network probe caches and organizes the network status data in each collection cycle, including network traffic data, network equipment status information, and operator network parameters, and packages them into structured messages. Based on the fact that the current fluctuation coefficient is in a low-variability range, it is determined that the current network is in a stable state, and the probe reporting mode is triggered to switch to a full upload mode to ensure that the central system can obtain the most comprehensive network operation status. According to the current optimized transmission frequency of the probe and matching the bandwidth resources, a medium- and high-speed transmission channel is selected to ensure that the complete data is uploaded within the preset delay limit without causing data accumulation or bandwidth congestion. After receiving the data, the data center will put the full data into the warehouse in time sequence, and start a low-priority batch processing task to extract key fields for data archiving, trend modeling, and long-term performance evaluation to provide data support for subsequent policy adjustments.

[0059] S13032. When the network fluctuation coefficient is greater than or equal to the third fluctuation threshold and less than the first fluctuation threshold, calculate the status data average value and the status data variance of the network status data, and extract the status data abnormal value of the network status data; transmit the status data average value, the status data variance and the status data abnormal value to the data center.

[0060] Exemplarily, when the network fluctuation coefficient is greater than or equal to the third fluctuation threshold and less than the first fluctuation threshold, the network status data statistical analysis module is activated to perform structured processing on the network status data collected by the network probe during the current period. The statistical mean and second-order central moment of key fields are calculated to reflect the central tendency and fluctuation level of the current network status. A multi-model combination strategy, such as the Z-score method, sliding window mutation detection, or the IQR method, is used to identify outliers in each field of the status data. Anomalous samples are extracted and annotated with corresponding timestamps, field names, and anomaly levels to form an outlier vector set. The mean, variance, and outlier data of the status data are differentially encoded, prioritizing high-weighted fields that significantly influence the evolution of network status trends. The data to be uploaded is organized into structured summary packages. Based on the current network fluctuation level, the network probe selects a medium-priority channel to upload these statistical feature packages to the data center. During this transmission process, redundant packet headers and checksums are introduced to prevent sudden fluctuations from affecting packet transmission integrity, ensuring that the data center can fully interpret the statistical feature information. After receiving the statistical feature data, the data center automatically triggers a medium-frequency refresh mechanism, mapping the mean and variance to the current network status heat map. It also writes outlier annotations to the diagnostic log and incorporates them into the weighting factors for subsequent intelligent scheduling decisions. This step is suitable for status monitoring with certain fluctuations, avoiding the redundancy caused by uploading full data, while also enabling quantitative modeling of network status fluctuation trends and sensitivity warnings.

[0061] S1304: When the network fluctuation coefficient is greater than or equal to a first fluctuation threshold and less than a second fluctuation threshold, control the network probe to transmit the network traffic data to a data center.

[0062] For example, a network probe compares the current network fluctuation coefficient in real time. If it falls between the first and second fluctuation thresholds, the network is judged to be in a moderate fluctuation phase. While it has some transmission capacity, it is insufficient to support full, high-frequency data transmission, and a limited critical data upload strategy is implemented. The probe uses a hierarchical data collection and caching mechanism, extracting only the network flow data portion of the network status data and uploading it as a core indicator of current link performance. The collected network flow data is restructured, redundant fields are removed, and lightweight compression algorithms such as LZ4 and ZSTD are used to reduce data load, ensuring stable data transmission under moderate network conditions without excessive bandwidth consumption. The network probe dynamically adjusts transmission batches and sending cadence based on the currently available uplink bandwidth to avoid data congestion caused by short-term bandwidth compression. Furthermore, the probe leverages historical bandwidth trend forecasts to plan data upload windows in advance. The data center utilizes an incremental fusion strategy for flow data from the probe, combining it with existing historical status curves for trend modeling and anomaly detection. This ensures that network behavior visualization is still possible even when receiving only flow-level data. When the network enters a moderate fluctuation range, the system prioritizes the transmission of traffic data, taking into account data representativeness and system load balance, and provides support for subsequent fluctuation trend judgment and scheduling strategy optimization.

[0063] S1305: When the network fluctuation coefficient is greater than or equal to a second fluctuation threshold, control the network probe to transmit the network fluctuation coefficient to a data center.

[0064] For example, a network probe determines the current network fluctuation coefficient in real time. If the coefficient is greater than or equal to a second fluctuation threshold, the network is deemed to be in a severe fluctuation phase. This phase is typically characterized by high packet loss, extreme bandwidth compression, frequent channel switching, and a lack of continuous, high-frequency data upload capabilities. The probe then initiates an emergency transmission strategy, shutting down all non-essential data channels and retaining only the abnormal signal upload path. High-priority transmission queues, minimum packet size control, and QoS marking ensure that abnormal network fluctuation coefficient information can be quickly delivered to the data center under extreme network conditions. In some embodiments, in addition to the current network fluctuation coefficient, the probe can construct a structured report containing summary information, including the current acquisition frequency and actual packet loss rate, the proportion of network fluctuation factors, and the fluctuation coefficient gradient over the recent period. This summary data packet is encapsulated in an extremely compressed format to minimize data size to accommodate low-quality links. Upon receiving the fluctuation coefficient information uploaded by the probe, the data center marks the remote device as an unstable node, reducing its data dependency on the device. If the device continues to recover for more than a preset time window, a link outage alert is sent to the operations and maintenance system. Under severe fluctuation conditions, the probe will automatically suspend the regular collection and transmission tasks of the current network status data, and only monitor the link quality in a low-frequency polling manner. When the fluctuation coefficient falls below the second fluctuation threshold, it will automatically exit the suspended state and restart the normal collection process.

[0065] Optionally, Figure 7 This is a flowchart of network status data visualization provided by the embodiment of this application. Figure 7 As shown, the steps of visualizing the network status data specifically include S131-S134: S131. Obtain the geographical area corresponding to the network probe on the map interface.

[0066] For example, the longitude and latitude coordinate information provided by the GPS module or the third-party positioning module is obtained through the probe acquisition module, and the original positioning data is subjected to denoising filtering and precision correction to ensure that the geographic location coordinates meet the meter-level accuracy requirements. Based on the high-precision map service interface, the longitude and latitude information is mapped to the standard administrative division or grid area code to obtain the geographic unit identifier of the current location of the network probe, including the province, city, district / county and microgrid number. The probe location information is dynamically bound to the node identifier in the map interface component to achieve a one-to-one correspondence between the probe instance and the geographic visual element, supporting the real-time presentation of subsequent data in the map interface and regional aggregation statistics.

[0067] S132: When the network fluctuation coefficient is less than a first fluctuation threshold, render the geographical area on the map interface using a set first color.

[0068] Exemplarily, the network fluctuation coefficient reported by the target probe during the current cycle is numerically determined. If it is less than a first fluctuation threshold, a region status flag update event is triggered, marking the geographic region as a stable state region. A color code corresponding to the stable state is retrieved from the visualization configuration dictionary. This first color can be a preset, low-intrusion hue, such as green, light blue, or light gray, to enhance user recognition efficiency on the interface. In the layer management module of the map interface, a graphics rendering interface is invoked to fill or stroke the target geographic region with the first color. Rendering effects include, but are not limited to, solid color fills, transparency control, and edge smoothing. The rendering state of the current region is stored in a visualization state cache, and an index mapping is established between region identifiers and color states to improve subsequent rendering efficiency and avoid repeated calculations and interface flicker. For example, if the network fluctuation coefficient is 0.12 and the first fluctuation threshold is set to 0.3, the region corresponding to the probe is marked as a stable state region. The system automatically reads green as the first color from the configuration and fills and renders the target region on the map with 50% transparency, allowing operations personnel to quickly identify the distribution of areas with good network status from a global perspective.

[0069] S133: When the network fluctuation coefficient is greater than or equal to a first fluctuation threshold and less than a second fluctuation threshold, render the geographical area on the map interface using a set second color.

[0070] Exemplarily, a numerical interval is determined for the current network fluctuation coefficient of the probe. If the fluctuation coefficient ∈ [first fluctuation threshold, second fluctuation threshold), the corresponding geographic area status is marked as a medium fluctuation area and pushed to the rendering task scheduling queue. According to the system's preset visualization level mapping strategy, the second color code corresponding to the medium fluctuation state is retrieved from the color strategy library. The color generally uses a medium warning intensity tone, such as yellow, orange, etc., and can be combined with transparency, color temperature or texture to enhance visual perception. In the layer control module of the map interface, the regional polygon rendering interface is called, and the second color is used to perform a layer overlay operation on the target geographic area. After the rendering is completed, the color rendering status of the current geographic area is written to the status cache module, and the timed refresh mechanism is turned on to automatically update the rendering layer when the fluctuation status changes, to ensure that the map interface display is highly consistent with the actual network status. For example, when the network fluctuation coefficient is 0.45, and the system sets the first fluctuation threshold to 0.3 and the second fluctuation threshold to 0.6, the system determines the coefficient as a medium fluctuation level, automatically selects yellow as the second color, and fills the corresponding area with 60% transparency on the map interface, assisting operation and maintenance personnel to quickly identify potential network instability areas, facilitating early warning response and resource scheduling.

[0071] S134. When the network fluctuation coefficient is greater than or equal to the second fluctuation threshold, the geographical area is rendered on the map interface using a set third color, wherein the network fluctuation levels corresponding to the first color, the second color, and the third color increase in sequence.

[0072] Exemplarily, when the network fluctuation coefficient reaches or exceeds the second fluctuation threshold, the system will mark the current area as a high-fluctuation area according to the set fluctuation level mapping rules, generate a high-priority map rendering event, and push it to the map layer scheduling module. The third color corresponding to the high fluctuation level is matched from the color level table. The third color usually uses strong warning colors such as red, dark red or high-saturation purple-red, supplemented by opaque filling to highlight the visual effect, and associates the prompt information label with the flashing animation configuration. After the map interface loads the geographic coordinate boundary of the target area, the high-priority rendering thread is enabled, and the area layer is filled with the third color. The rendering status is synchronously written to the status management module, and the fluctuation status monitoring mark is set. If the fluctuation status decreases in the subsequent periodic acquisition, the system automatically triggers the re-rendering task and returns the area to a medium or low fluctuation color level to ensure the real-time accuracy of the map display. For example, if the network fluctuation coefficient of a probe collection point is 0.72, and the second fluctuation threshold configured by the system is 0.6, the system will determine that it is in a high fluctuation state, call the map rendering module to fill the target area with red, and display a continuously flashing red fluctuation icon in the center of the area, so that the operation and maintenance personnel of the monitoring center can quickly locate areas with high incidence of network anomalies.

[0073] Based on the above embodiments, Figure 8 This is a schematic diagram of the structure of a network remote monitoring device provided by an embodiment of the present application. Figure 8 The network remote monitoring device provided in this embodiment specifically includes: a network status acquisition module 21, an acquisition frequency calculation module 22, and a status data transmission module 23.

[0074] The network status collection module 21 is configured to collect network status data of remote devices using a network probe, wherein the network status data includes network traffic data, network device status information, and operator network parameters; The acquisition frequency calculation module 22 is configured to calculate the network fluctuation coefficient of the remote device according to the network status data, and calculate the optimized acquisition frequency of the network probe based on the preset acquisition frequency of the network probe and the network fluctuation coefficient; The status data transmission module 23 is configured to use the network probe to collect the network status data of the remote device at the optimized collection frequency, and control the network probe to transmit the network status data to the data center for the data center to perform visual display of the network status data.

[0075] Based on the above embodiment, the acquisition frequency calculation module 22 includes: a traffic fluctuation coefficient unit, configured to calculate the packet loss rate, round-trip delay and bandwidth occupancy change rate of the remote device based on the network traffic data, and obtain the traffic fluctuation coefficient of the remote device based on the packet loss rate, the round-trip delay and the bandwidth occupancy change rate; a device status coefficient unit, configured to extract the CPU occupancy rate, device temperature and memory usage of the remote device from the network device status information, and obtain the device status coefficient of the remote device based on the CPU occupancy rate, the device temperature and the memory usage; an operator network coefficient unit, configured to extract the signal strength, signal quality and frequency band switching frequency received by the remote device from the operator network parameters, and obtain the operator network coefficient based on the signal strength, the signal quality and the frequency band switching frequency; a network fluctuation coefficient unit, configured to calculate the network fluctuation coefficient of the remote device based on the traffic fluctuation coefficient, the device status coefficient and the operator network coefficient.

[0076] Based on the above embodiment, the status data transmission module 23 includes: an optimized transmission frequency unit, configured to obtain the preset transmission frequency of the network probe, and calculate the optimized transmission frequency of the network probe based on the preset transmission frequency and the network fluctuation coefficient; a status data transmission unit, configured to control the network probe to transmit the network status data to the data center at the optimized transmission frequency.

[0077] Based on the above embodiment, the status data transmission module 23 also includes a stable state transmission unit, which is configured to control the network probe to transmit the network status data to the data center when the network fluctuation coefficient is less than the first fluctuation threshold; a moderate fluctuation transmission unit, which is configured to control the network probe to transmit the network traffic data to the data center when the network fluctuation coefficient is greater than or equal to the first fluctuation threshold and less than the second fluctuation threshold; and a severe fluctuation transmission unit, which is configured to control the network probe to transmit the network fluctuation coefficient to the data center when the network fluctuation coefficient is greater than or equal to the second fluctuation threshold.

[0078] Based on the above embodiment, the stable state transmission unit includes: a complete data transmission subunit, configured to control the network probe to transmit the network state data to the data center when the network fluctuation coefficient is less than the third fluctuation threshold; a preprocessing data transmission subunit, configured to calculate the state data average and state data variance of the network state data and extract the state data anomaly of the network state data when the network fluctuation coefficient is greater than or equal to the third fluctuation threshold and less than the first fluctuation threshold; and transmit the state data average, the state data variance and the state data anomaly to the data center.

[0079] Based on the above embodiment, the network status acquisition module 21 includes: a historical network fluctuation unit, configured to obtain the historical network status data of the remote device, and calculate the historical network fluctuation coefficient of the remote device based on the historical network status data; an initial acquisition frequency unit, configured to calculate the initial acquisition frequency of the network probe according to the preset acquisition frequency of the network probe and the historical network fluctuation coefficient; and an initial data acquisition unit, configured to use the network probe to collect the network status data of the remote device at the initial acquisition frequency.

[0080] Based on the above embodiment, the network remote monitoring device also includes a network probe positioning module, which is configured to obtain the geographical area corresponding to the network probe on the map interface; a stable state rendering module, which is configured to render the geographical area on the map interface using a set first color when the network fluctuation coefficient is less than a first fluctuation threshold; a moderate fluctuation rendering module, which is configured to render the geographical area on the map interface using a set second color when the network fluctuation coefficient is greater than or equal to the first fluctuation threshold and less than a second fluctuation threshold; a heavy fluctuation rendering module, which is configured to render the geographical area on the map interface using a set third color when the network fluctuation coefficient is greater than or equal to the second fluctuation threshold, wherein the network fluctuation levels corresponding to the first color, the second color and the third color increase in sequence.

[0081] As mentioned above, the network remote monitoring device provided by the embodiment of the present application improves the adaptability and flexible deployment capability of the network monitoring system in a changeable network environment, and can dynamically adjust the collection and transmission strategies according to the differences in network environments corresponding to different geographical areas, including channel quality, terminal distribution density, operator configuration strategies, etc., thereby significantly improving the system's stable operation capability and data collection efficiency in complex network environments. At the same time, by introducing a hierarchical fluctuation threshold determination mechanism and a multi-dimensional parameter fusion calculation model, accurate quantification and dynamic identification of network fluctuation states are achieved, the sensitivity and identification capability of potential network anomalies are enhanced, and the timeliness and accuracy of fault warnings are significantly improved. It can actively initiate reporting and visual presentation at the early stage of network fluctuations, thereby gaining more response and handling time for network operation and maintenance personnel, effectively reducing the impact of network failures on key business links, end-user experience, and operating costs.

[0082] The network remote monitoring device provided in the embodiment of the present application can be used to execute the network remote monitoring method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0083] Figure 9 This is a schematic diagram of the structure of a network remote monitoring device provided by an embodiment of the present application, with reference to Figure 9 The network remote monitoring device includes: a processor 31, a memory 32, a communication device 33, an input device 34, and an output device 35. The number of processors 31 in the network remote monitoring device can be one or more, and the number of memories 32 in the network remote monitoring device can be one or more. The processor 31, memory 32, communication device 33, input device 34, and output device 35 of the network remote monitoring device can be connected via a bus or other means.

[0084] Memory 32, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the network remote monitoring method of any embodiment of the present application (e.g., the network status acquisition module 21, acquisition frequency calculation module 22, and status data transmission module 23 in the network remote monitoring device). Memory 32 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on device usage. Furthermore, memory 32 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some embodiments, the memory may further include memory located remotely from the processor, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0085] The communication device 33 is used for data transmission.

[0086] The processor 31 executes the software programs, instructions and modules stored in the memory 32 to perform various functional applications and data processing of the device, that is, to implement the above-mentioned network remote monitoring method.

[0087] The input device 34 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 35 may include a display device such as a display screen.

[0088] The network remote monitoring device provided above can be used to execute the network remote monitoring method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0089] An embodiment of the present application also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute a network remote monitoring method, the network remote monitoring method comprising: using a network probe to collect network status data of a remote device, the network status data comprising network traffic data, network device status information and operator network parameters; calculating the network fluctuation coefficient of the remote device based on the network status data, and calculating the optimized collection frequency of the network probe based on the preset collection frequency of the network probe and the network fluctuation coefficient; using the network probe to collect the network status data of the remote device at the optimized collection frequency, and controlling the network probe to transmit the network status data to a data center for the data center to perform visual display of the network status data.

[0090] Storage medium—any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROMs, floppy disks, or tape drives; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, and Rambus RAM; non-volatile memory, such as flash memory, magnetic media (e.g., hard disks or optical storage); registers or other similar types of memory elements; and the like. Storage media may also include other types of memory or a combination thereof. Furthermore, a storage medium may be located in a first computer system where a program is executed, or in a different second computer system connected to the first computer system via a network (such as the Internet). The second computer system may provide program instructions to the first computer system for execution. The term "storage medium" may include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). A storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.

[0091] Of course, the storage medium containing computer-executable instructions provided in the embodiment of the present application, whose computer-executable instructions are not limited to the above-mentioned network remote monitoring method, can also execute related operations in the network remote monitoring method provided in any embodiment of the present application.

[0092] The network remote monitoring device, storage medium and network remote monitoring equipment provided in the above embodiments can execute the network remote monitoring method provided in any embodiment of the present application. For technical details not described in detail in the above embodiments, please refer to the network remote monitoring method provided in any embodiment of the present application.

[0093] The above are only preferred embodiments of the present application and the technical principles employed. The present application is not limited to the specific embodiments described herein, and any obvious changes, readjustments, and substitutions that are apparent to those skilled in the art will not depart from the scope of protection of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present application. The scope of the present application is determined by the scope of the claims.

Claims

1. A network remote monitoring method, characterized in that: include: Using a network probe to collect network status data of a remote device, the network status data includes network traffic data, network device status information, and operator network parameters; Calculating a network fluctuation coefficient of the remote device according to the network status data, and calculating an optimized collection frequency of the network probe based on a preset collection frequency of the network probe and the network fluctuation coefficient; The network status data of the remote device is collected using the network probe at the optimized collection frequency, and the network probe is controlled to transmit the network status data to a data center for the data center to perform visual display of the network status data.

2. The network remote monitoring method according to claim 1, characterized in that: Calculating the network fluctuation coefficient of the remote device according to the network status data includes: Calculating a packet loss rate, a round-trip delay, and a bandwidth occupancy change rate of the remote device based on the network traffic data, and obtaining a traffic fluctuation coefficient of the remote device based on the packet loss rate, the round-trip delay, and the bandwidth occupancy change rate; Extracting a CPU occupancy rate, a device temperature, and a memory usage rate of the remote device from the network device status information, and obtaining a device status coefficient of the remote device based on the CPU occupancy rate, the device temperature, and the memory usage rate; Extracting the signal strength and signal quality received by the remote device and the frequency of frequency switching of the remote device from the operator network parameters, and obtaining an operator network coefficient based on the signal strength, the signal quality and the frequency switching frequency; The network fluctuation coefficient of the remote device is calculated according to the traffic fluctuation coefficient, the device status coefficient and the operator network coefficient.

3. The network remote monitoring method according to claim 1, characterized in that: The controlling the network probe to transmit the network status data to a data center includes: Obtaining a preset transmission frequency of the network probe, and calculating an optimized transmission frequency of the network probe based on the preset transmission frequency and the network fluctuation coefficient; The network probe is controlled to transmit the network status data to a data center at the optimized transmission frequency.

4. The network remote monitoring method according to claim 1, characterized in that: The controlling the network probe to transmit the network status data to a data center includes: When the network fluctuation coefficient is less than a first fluctuation threshold, controlling the network probe to transmit the network status data to a data center; When the network fluctuation coefficient is greater than or equal to a first fluctuation threshold and less than a second fluctuation threshold, controlling the network probe to transmit the network traffic data to a data center; When the network fluctuation coefficient is greater than or equal to a second fluctuation threshold, the network probe is controlled to transmit the network fluctuation coefficient to a data center.

5. The network remote monitoring method according to claim 4, characterized in that: When the network fluctuation coefficient is less than a first fluctuation threshold, controlling the network probe to transmit the network status data to a data center includes: When the network fluctuation coefficient is less than a third fluctuation threshold, controlling the network probe to transmit the network status data to a data center; When the network fluctuation coefficient is greater than or equal to the third fluctuation threshold and less than the first fluctuation threshold, the status data average value and the status data variance of the network status data are calculated, and the status data abnormal value of the network status data is extracted; the status data average value, the status data variance and the status data abnormal value are transmitted to the data center.

6. The network remote monitoring method according to claim 1, characterized in that: The method of collecting network status data of a remote device by using a network probe includes: Acquiring historical network status data of the remote device, and calculating a historical network fluctuation coefficient of the remote device based on the historical network status data; Calculating an initial collection frequency of the network probe according to the preset collection frequency of the network probe and the historical network fluctuation coefficient; The network status data of the remote device is collected using the network probe at the initial collection frequency.

7. The network remote monitoring method according to claim 1, characterized in that: After controlling the network probe to transmit the network status data to the data center, the method further includes: Obtaining the geographical area corresponding to the network probe on the map interface; When the network fluctuation coefficient is less than a first fluctuation threshold, rendering the geographical area on the map interface using a set first color; When the network fluctuation coefficient is greater than or equal to a first fluctuation threshold and less than a second fluctuation threshold, rendering the geographical area on the map interface using a set second color; When the network fluctuation coefficient is greater than or equal to the second fluctuation threshold, the geographical area is rendered on the map interface using a set third color, wherein the network fluctuation levels corresponding to the first color, the second color, and the third color increase in sequence.

8. A network remote monitoring device, characterized in that: include: A network status collection module is used to collect network status data of remote devices using network probes. The network status data includes network traffic data, network device status information, and operator network parameters. an acquisition frequency calculation module, configured to calculate a network fluctuation coefficient of the remote device according to the network status data, and calculate an optimized acquisition frequency of the network probe based on a preset acquisition frequency of the network probe and the network fluctuation coefficient; The status data transmission module is used to use the network probe to collect the network status data of the remote device at the optimized collection frequency, control the network probe to transmit the network status data to the data center, and use the data center to visualize the network status data.

9. A network remote monitoring device, characterized in that: include: one or more processors; The memory stores one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the network remote monitoring method according to any one of claims 1 to 7.

10. A storage medium containing computer-executable instructions, characterized in that: When executed by a computer processor, the computer executable instructions are used to execute the network remote monitoring method according to any one of claims 1 to 7.

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