An internet of things card data flow monitoring and early warning system based on data analysis

Abstract

The application provides a data analysis-based Internet of Things card data flow monitoring and early warning system, which comprises: establishing an initial channel adjustment table for a transmission channel connected between an Internet of Things device and an Internet of Things card; establishing a data health coefficient and a signal health coefficient based on log stream records in the corresponding Internet of Things channel and a plurality of signal influence parameters affecting data flow transmission, and generating a channel health coefficient; comparing the channel health coefficient with a channel early warning threshold value, if the channel health coefficient is greater than or equal to the channel early warning threshold value, the corresponding transmission channel is a first target; if the channel health coefficient is less than the channel early warning threshold value, the corresponding transmission channel is a second target; generating a sorting assignment based on the channel health coefficient corresponding to the first target; updating the channel adjustment table in real time from large to small according to the sorting assignment; and generating a monitoring and early warning instruction based on the channel health coefficient corresponding to the second target.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and more specifically, to an IoT card data traffic monitoring and early warning system based on data analysis. Background Technology

[0002] An IoT SIM card is a type of SIM card specifically designed for IoT devices; it is primarily used for IoT devices to send and receive data with the internet or other devices. This data can be collected, analyzed, monitored, and controlled via the IoT SIM card, thereby improving productivity, reducing costs, increasing security, and providing a better user experience.

[0003] For example, patent publication number CN103686663A, titled "Network Traffic Monitoring Method and Apparatus," discloses a method that compares and analyzes network traffic within a preset monitoring time window against a preset monitoring and early warning threshold. By performing statistical and trend analysis on the data traffic, the system can detect abnormal situations or situations exceeding the preset threshold. This is currently the main method for data traffic analysis. Once the system detects an abnormal situation or a situation exceeding the preset threshold, it will trigger an early warning notification. Early warning notifications can be sent in different ways, such as sending alarm information to operators, pushing notifications to relevant personnel, or generating reports for analysis.

[0004] However, this analytical method does not consider the specific application of IoT device data traffic. IoT devices transmit data through specific transmission channels. It is unclear whether there are a large number of transmission errors or packet loss during the transmission process, and what impact this phenomenon has on data traffic analysis. In addition, the factors affecting data transmission through the transmission channel also include the parameters affecting the current transmission channel. If the data of the influencing parameters is too negative, resulting in very weak signal strength, it is clear that the transmission channel itself is malfunctioning, which will also trigger an IoT card data traffic monitoring warning.

[0005] In view of this, the present invention provides an IoT card data traffic monitoring and early warning system based on data analysis. Summary of the Invention

[0006] This invention addresses the robustness and security issues of existing technologies by proposing an IoT card data traffic monitoring and early warning system based on data analysis.

[0007] According to one aspect of the present invention, an IoT SIM card data traffic monitoring and early warning system based on data analysis is provided, comprising:

[0008] It includes an initialization module, a monitoring module, an analysis module, a comprehensive analysis module, a sorting module, an early warning module, and a control module;

[0009] The initialization module marks the transmission channel connecting the IoT device and the IoT card as K. i Let i = 1, 2, ..., I, where I is a positive integer; mark the channel adjustment table of the transmission channels connecting the current I IoT devices and IoT cards as {K1, K2, ..., K...} i ,…,K I}, use the currently marked channel adjustment table as the initial channel adjustment table;

[0010] The monitoring module collects log stream records and multiple signal impact parameters that affect data traffic transmission in the corresponding IoT channel in real time.

[0011] The analysis module establishes a data health coefficient based on log stream records through normalization processing;

[0012] A signal health coefficient is established based on multiple signal influence parameters through standardization processing.

[0013] The comprehensive analysis module performs a comprehensive evaluation and analysis of the transmission channel based on the data health coefficient and the signal health coefficient to generate the channel health coefficient.

[0014] The comparison module compares the channel health coefficient with the channel warning threshold. If the channel health coefficient is greater than or equal to the channel warning threshold, the corresponding transmission channel will be the first target; if the channel health coefficient is less than the channel warning threshold, the corresponding transmission channel will be the second target.

[0015] The control module sends the channel health coefficient corresponding to the first target to the sorting module, and sends the channel health coefficient corresponding to the second target to the early warning module.

[0016] The sorting module generates a sorting assignment based on the channel health coefficient corresponding to the first target; it updates the channel adjustment table in real time from large to small according to the sorting assignment; after updating the channel adjustment table, the IoT card transmits data traffic from large to small according to the channel adjustment table.

[0017] The early warning module generates monitoring and early warning instructions based on the channel health coefficient corresponding to the second target, and sends early warning notifications to the information receiving devices based on the monitoring and early warning instructions.

[0018] According to another aspect of the present invention, a data analysis-based method for monitoring and early warning of IoT card data traffic is provided, which is based on the implementation of a data analysis-based IoT card data traffic monitoring and early warning system, comprising:

[0019] The transmission channel connecting the IoT device and the IoT card is labeled K. iLet i = 1, 2, ..., I, where I is a positive integer; mark the channel adjustment table of the transmission channels connecting the current I IoT devices and IoT cards as {K1, K2, ..., K...} i ,…,K I}, use the currently marked channel adjustment table as the initial channel adjustment table;

[0020] Real-time acquisition of log stream records in the corresponding IoT channel and multiple signal impact parameters affecting data traffic transmission;

[0021] A data health coefficient is established based on log stream records through normalization processing;

[0022] A signal health coefficient is established based on multiple signal influence parameters through standardization processing.

[0023] A channel health coefficient is generated by comprehensively evaluating and analyzing the transmission channel based on the data health coefficient and the signal health coefficient.

[0024] Based on the comparison between the channel health coefficient and the channel warning threshold, if the channel health coefficient is greater than or equal to the channel warning threshold, the corresponding transmission channel will be the first target; if the channel health coefficient is less than the channel warning threshold, the corresponding transmission channel will be the second target.

[0025] The sorting assignment is generated based on the channel health coefficient corresponding to the first target; the channel adjustment table is updated in real time from large to small according to the sorting assignment; after the channel adjustment table is updated, the IoT card transmits data traffic from large to small according to the channel adjustment table.

[0026] Based on the channel health coefficient corresponding to the second objective, a monitoring and early warning instruction is generated, and an early warning notification is sent to the information receiving device based on the monitoring and early warning instruction.

[0027] According to another aspect of the present invention, an electronic device is provided, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0028] The processor executes the above-described IoT card data traffic monitoring and early warning method based on data analysis by calling the computer program stored in the memory.

[0029] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores instructions that, when executed on a computer, cause the computer to perform the above-described data analysis-based IoT card data traffic monitoring and early warning method.

[0030] The beneficial effects of this invention are as follows:

[0031] This invention analyzes data traffic applications of specific IoT devices to provide more comprehensive, accurate, and real-time monitoring and early warning, helping to ensure the security and stability of data transmission in IoT devices. First, it comprehensively evaluates and analyzes transmission channels using data health coefficients and signal health coefficients, generating a channel health coefficient. This coefficient assesses, optimizes, and monitors the health of transmission channels, solving technical problems in data traffic detection systems within IoT cards and improving system stability and reliability. The channel health coefficient is compared with a channel early warning threshold to designate a channel as a first or second target. This helps to detect problems early and take corresponding measures to avoid data transmission interruptions or other potential issues. For the first target, the system can sort and assign values ​​and update the channel adjustment table in real time to prioritize healthy channels for data traffic transmission. For the second target, a monitoring and early warning command is generated to notify the information receiving device. By monitoring and analyzing the data traffic of IoT cards, problematic transmission channels or influencing parameters can be identified, maximizing the use of reliable transmission channels and improving the reliability and efficiency of data transmission. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope of patent protection. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of the IoT card data traffic monitoring and early warning system based on data analysis according to the present invention;

[0034] Figure 2 This is a flowchart of the IoT card data traffic monitoring and early warning method based on data analysis according to the present invention;

[0035] Figure 3 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0037] The following explanation of the technology involved in this application is provided for ease of understanding:

[0038] MQTT (Message Queuing Telemetry Transport): The MQTT protocol is a lightweight publish / subscribe protocol suitable for low-bandwidth and unstable network environments. It is one of the most commonly used protocols in the Internet of Things (IoT) for transmitting small data packets, such as sensor data.

[0039] HTTP (Hypertext Transfer Protocol): HTTP is a widely used protocol on the Internet for transferring data between clients and servers. In the Internet of Things (IoT), HTTP is typically used to communicate with cloud platforms and upload and download device data.

[0040] CoAP (Constrained Application Protocol): CoAP is an application layer protocol designed specifically for constrained environments, such as sensor nodes. It is based on HTTP but is more lightweight and energy-efficient. CoAP supports communication between low-power devices and has the ability to discover and manage resources.

[0041] AMQP (Advanced Message Queuing Protocol): AMQP is a message-oriented protocol used for reliable message passing between heterogeneous systems. It supports advanced message routing and queue management features and is widely used in the Internet of Things (IoT) for large-scale message passing and communication.

[0042] DDS (Data Distribution Service): The DDS protocol is a data communication protocol for real-time systems. It supports high-throughput, low-latency data transmission and is suitable for IoT applications that require real-time performance and reliability, such as industrial automation and intelligent transportation systems.

[0043] This system can filter and determine the target traffic collection model from a pre-set set of candidate collection models based on traffic collection type information. Then, it uses the target traffic collection model to process historical collection information and pre-set collection time intervals to obtain the target collection time information set. This allows for the determination of target traffic collection information used to analyze the activity of target IoT cards. This helps avoid duplicate and invalid traffic data collection, focusing collection opportunities on key IoT cards requiring attention, thereby enabling real-time monitoring of their activity and usage, and improving the efficiency of IoT card management. Detailed explanations follow.

[0044] Example 1

[0045] To solve the above problems, such as Figure 1As shown, this invention proposes an IoT card data traffic monitoring and early warning system based on data analysis, aiming to solve the problem of abnormal data traffic in IoT devices during application in existing technologies, such as... Figure 1 As shown, it includes an initialization module, a monitoring module, an analysis module, a comprehensive analysis module, a comparison module, a sorting module, an early warning module, and a control module. The modules are connected by wired and / or wireless connections.

[0046] The initialization module marks the transmission channel connecting the IoT device and the IoT card as K. i Let i = 1, 2, ..., I, where I is a positive integer; mark the channel adjustment table of the transmission channels connecting the current I IoT devices and IoT cards as {K1, K2, ..., K...} i ,…,K I}, use the currently marked channel adjustment table as the initial channel adjustment table;

[0047] It's important to note here that the transmission channels between IoT devices and IoT cards are labeled with positive integers to uniquely identify each channel. Simultaneously, an initial channel tuning table is used to record and manage relevant channel information.

[0048] The monitoring module collects log stream records and multiple signal impact parameters that affect data traffic transmission in the corresponding IoT channel in real time.

[0049] It's important to note that data throughput and signal impact parameters are crucial indicators for evaluating transmission channels. Generally, higher data throughput indicates greater data transmission activity on the corresponding channel. However, different transmission channels may face varying levels of signal interference and attenuation. Parameters such as bandwidth, signal-to-noise ratio, network latency, signal strength, previous signal transmission rate, and bit synchronization signal phase jitter rate can be considered to assess the signal quality of a transmission channel. Better signal quality indicates a more reliable state and performance of the corresponding transmission channel.

[0050] The analysis module establishes a data health coefficient based on log stream records through normalization processing;

[0051] A signal health coefficient is established based on multiple signal influence parameters through standardization processing.

[0052] It should be noted that the data health coefficient is used to assess the data transmission status of the transmission channel, such as determining whether there are problems such as overload or low bandwidth utilization.

[0053] The specific analysis is as follows: The logic for obtaining the data health coefficient is as follows:

[0054] The log stream records include one or more of the following data: inbound data packets, outbound data packets, error data packets, and dropped data packets;

[0055] The inbound data packet D will be sent to the inbound data packet D. in Outbound data packet D out Error data packet D error and dropping data packets D drop Perform normalization processing to obtain the normalized inbound data packet D. in,norm Outbound data packet D out,norm Error data packet D error,norm and dropping data packets D drop,norm Among them, the normalized inbound data packet D in,norm Outbound data packet D out,norm Error data packet D error,norm and dropping data packets D drop,norm The values ​​all range from [0, 1].

[0056] It should be noted here that the normalization method is to use minimum-maximum normalization, that is, to subtract the minimum value of the data packet within a preset time period from the data packet, and then divide by the difference between the maximum value and the minimum value of the data packet within the preset time period.

[0057] The specific formula is as follows:

[0058] Normalized inbound data packets

[0059] Normalized outbound data packets

[0060] Normalized error data packets,

[0061] Normalized dropped data packets

[0062] In the formula, D in For real-time detection of inbound data packets, D in,min D is the minimum number of inbound data packets within a preset time period. in,max The maximum value of inbound data packets within a preset time period, after normalization, is D. in,norm Inbound data packets that have been normalized within a preset time period;

[0063] Similarly: D out For real-time detection of outbound data packets, D out,min D is the minimum number of outbound data packets within a preset time period. out,max The maximum value of outbound data packets within a preset time period, after normalization, is D. out,normOutbound data packets that have been normalized within a preset time period;

[0064] D error For real-time detection of error data packets, D error,min D is the minimum number of erroneous data packets within a preset time period. error,max The maximum value of erroneous data packets within a preset time period, after normalization, is D. error,norm Error data packets that have been normalized within a preset time period;

[0065] D drop For real-time detection of dropped data packets, D drop,min D is the minimum number of data packets to be dropped within a preset time period. drop,max The maximum number of data packets dropped within a preset time period, after normalization, is D. drop,norm Discarded data packets that have been normalized within a preset time period;

[0066] And the normalized inbound data packet D in,norm Outbound data packet D out,norm Error data packet D error,norm and dropping data packets D drop,norm Assign appropriate weighting factors to reflect the importance of the corresponding data packets to the transmission channel;

[0067] Inbound data packet D in,norm Outbound data packet D out,norm Error data packet D error,norm and dropping data packets D drop,norm Multiply each value by its corresponding weighting factor, and then calculate the average of the weighted values ​​to obtain the data health coefficient X. sj The formula is as follows:

[0068]

[0069] In the formula, ε1, ε2, ε3 and ε4 are the inbound data packets D in,norm Outbound data packet D out,norm Error data packet D error,norm and dropping data packets D drop,norm The weighting factors are: 0≤ε1≤1, 0≤ε2≤1, 0≤ε3≤1 and 0≤ε4≤1.

[0070] It should be noted here that: Inbound data packet D in,norm Outbound data packet D out,norm Error data packet D error,norm and dropping data packets D drop,norm The weighted values ​​reflect the degree of impact of the corresponding data packet on the transmission channel. These weighted values ​​can be summed and averaged to obtain the data health coefficient X. sj .

[0071] It should be noted that the specific calculation method for the data health coefficient and the selection of the weighting coefficient need to be determined based on the specific circumstances.

[0072] By counting inbound and outbound data packets, we can understand the traffic load on the network interface, enabling us to assess network bandwidth usage and performance bottlenecks. Error and dropped data packets can help identify network faults or security issues. By examining the characteristics or error codes of these packets, we can determine the cause of the problem, such as network connectivity issues, configuration errors, or network attacks.

[0073] By using the quantitative analysis methods described above for data traffic, cloud service providers or users can gain insights into the network interface performance, traffic load, abnormal behavior, and security events of the transmission channel connecting IoT devices and physical network cards, in order to optimize network performance, improve security, and respond quickly to any issues.

[0074] In summary, the data health coefficient X sj The larger the value of the X coefficient, the more stable the data transmission of the current transmission channel; conversely, a smaller value indicates a lower data health coefficient. sj The smaller the value, the more unstable the data transmission in the current transmission channel.

[0075] The signal health factor is used to evaluate the signal quality and stability of a transmission channel.

[0076] The specific analysis is as follows: The logic for obtaining the signal health coefficient includes:

[0077] Signal influencing parameters include one or more of the following: bandwidth, signal-to-noise ratio, network delay, signal strength, previous signal transmission rate, and bit synchronization signal phase jitter rate;

[0078] The bandwidth B is respectively pd Signal-to-noise ratio B xz Network latency B yt Signal strength B xq The signal transmission rate B at the previous moment xv Phase jitter rate B of the synchronization signal dv After removing the units, dimensionless processing is performed to establish the signal health coefficient X. xh The specific formula is as follows:

[0079]

[0080] In the formula, 0≤X xh≤1, 0≤α1≤1, 0≤α2≤1, 0≤α3≤1, 0≤α4≤1, 0≤α5≤1, 0≤α6≤1, where α1, α2, α3, α4, α5, and α6 are the bandwidths B, respectively. pd Signal-to-noise ratio B xz Network latency B yt Signal strength B xq The signal transmission rate B at the previous moment xv Phase jitter rate B of the synchronization signal dv The corresponding weight, B pdmax B represents the maximum bandwidth of the corresponding transmission channel. pdmin B represents the minimum bandwidth of the corresponding transmission channel. xqs The signal strength of the transmission channel at the previous moment;

[0081] It should be noted here that in this embodiment, the bandwidth B... pd Real-time acquisition via a spectrum analyzer; refers to the frequency range available in wireless communication. A wider bandwidth can provide greater data transmission capacity, but it is also more susceptible to interference and attenuation. Limited bandwidth may lead to reduced signal transmission rates or data loss; therefore, for bandwidth B... pd The corresponding frequency range for the transmission channel of the IoT device is in [B]. pdmin B pdmax Substituting this into the formula, that is, when the bandwidth B pd In [B] pdmin B pdmax Within this range, the proportion of the current frequency band can be obtained through the corresponding ratio. If it is not within [B]... pdmin B pdmax Within this range, the formula produces negative values, severely impacting the signal health coefficient X. xh The value of ; therefore, when the bandwidth B pd The closer to B pdmax -B pdmin When the bandwidth is less affected by interference and attenuation, the impact on the signal health coefficient is positive.

[0082] Signal-to-noise ratio and network latency B yt Monitoring using an oscilloscope involves connecting the oscilloscope to both the signal input and output terminals of the IoT device. The oscilloscope is then used to test the ratio of the variables at the output terminals of each transmission channel under varying input and output conditions, as well as the time delay of data transmission from the sending end to the receiving end; the signal-to-noise ratio (SNR) B... xzThe signal-to-noise ratio (SNR) is the ratio of signal strength to background noise. A higher SNR indicates a stronger signal and less noise, which is beneficial for accurate signal transmission and interpretation, thus having a positive impact on the signal health factor. Conversely, a low SNR allows noise to interfere with the signal, leading to transmission errors or signal loss, thus having a negative impact on the signal health factor.

[0083] Network latency B yt Network latency refers to the time delay that data experiences from the sender to the receiver. Higher network latency increases signal transmission delay, potentially impacting real-time applications such as real-time voice or video communication, thus negatively affecting signal health. Conversely, lower network latency... yt The shorter the time, the better it meets the real-time requirements of signal transmission for IoT devices, and thus has a positive impact on the signal health coefficient.

[0084] Signal strength B xq And the signal transmission rate B at the previous moment xv Signal strength B is collected and calculated by the system processor. xq And can detect the signal strength of the transmission channel in real time, and the signal transmission rate B at the previous moment. xv It can detect the transmission rate of the previous moment in the transmission channel. The greater the change in signal strength in the transmission channel, the stronger the signal strength B. xq The signal strength B corresponding to the transmission channel at the previous moment xqs The larger the difference between them, the higher the signal transmission rate B at the previous moment. xv The greater the relative change, the worse the stability of the transmission channel, leading to transmission anomalies. Conversely, the smaller the amplitude of signal strength change in the transmission channel, the stronger the signal strength B. xq The signal strength B corresponding to the transmission channel at the previous moment xqs The smaller the difference between them, the higher the signal transmission rate B at the previous moment. xv The smaller the relative change, the better the stability of the transmission channel. Therefore, using the signal transmission rate of the transmission channel at the previous moment as the acquisition parameter can effectively capture changes in the transmission channel. Transmission channels with excessively large changes in signal strength generally affect the value of the signal health coefficient, resulting in a low channel health coefficient and triggering an early warning command. Conversely, signals with small changes in signal strength within a preset time period indicate a relatively stable transmission channel. If the signal transmission rate at the previous moment is not abnormal, it is unlikely to affect the value of the signal health coefficient, and real-time acquisition can be performed according to the actual situation.

[0085] Signal strength B xqThis indicates the electromagnetic energy or power level of a signal. A stronger signal strength contributes to improved reliability and stability of signal transmission, thus having a positive impact on the signal health coefficient. Conversely, a weaker signal strength may lead to signal attenuation, increased interference, or limited transmission distance, thereby affecting signal stability and negatively impacting the signal health coefficient.

[0086] The signal transmission rate B at the previous moment xv This refers to the amount of data successfully transmitted within the previous time period. If the signal strength changes significantly, the amplitude of the difference between the signal transmission rate at the previous moment and the current signal transmission rate will be large. In this case, the current signal transmission rate will be unstable, meaning there will be large fluctuations or continuous low-speed transmission, which may affect the overall signal stability and data transmission efficiency.

[0087] The phase jitter rate of the bit synchronization signal is monitored in real time using a filtering method. The phase jitter rate B of the bit synchronization signal... dv In digital communication, phase jitter refers to the degree of timing fluctuation in a bit synchronization signal. A large phase jitter rate can lead to bit synchronization errors, thereby affecting the accuracy of signal interpretation and data transmission.

[0088] In summary, the signal health coefficient X xh The larger the value of X, the better the signal quality and stability of the current transmission channel; conversely, the smaller the value of X, the better the signal health coefficient. xh The smaller the value, the worse the signal quality and stability of the current transmission channel.

[0089] The comprehensive analysis module performs a comprehensive evaluation and analysis of the transmission channel based on the data health coefficient and the signal health coefficient to generate the channel health coefficient.

[0090] It should be noted that the channel health coefficient integrates the evaluation results of both data traffic and signal quality, and can more comprehensively measure the channel's performance and health status.

[0091] Specifically, the logic for obtaining the channel health coefficient includes:

[0092] Its specific analytical formula is: X td =β1×X sj +β2×X xh , where β1 and β2 represent the weighting factors corresponding to the set data health coefficient and signal health coefficient, respectively.

[0093] The comparison module compares the channel health coefficient with the channel warning threshold. If the channel health coefficient is greater than or equal to the channel warning threshold, the corresponding transmission channel will be the first target; if the channel health coefficient is less than the channel warning threshold, the corresponding transmission channel will be the second target.

[0094] It should be noted that: if the channel health coefficient is greater than or equal to the channel warning threshold, it indicates that the data traffic and signal quality of the current transmission channel are within the normal operating range, and therefore the channel health coefficient is marked as the first target. If the channel health coefficient is less than the channel warning threshold, it indicates that the data traffic and signal quality of the current transmission channel are within the abnormal operating range, and therefore the channel health coefficient is marked as the second target.

[0095] The control module sends the channel health coefficient corresponding to the first target to the sorting module, and sends the channel health coefficient corresponding to the second target to the early warning module, and sends early warning notifications to the information receiving device based on the monitoring and early warning instructions.

[0096] The sorting module generates a sorting assignment based on the channel health coefficient corresponding to the first target; it updates the channel adjustment table in real time from largest to smallest according to the sorting assignment; after updating the channel adjustment table, the IoT card transmits data traffic from largest to smallest according to the channel adjustment table.

[0097] It's important to note here that the health coefficients of normally functioning transmission channels are assigned and sorted in descending order. Then, the channel adjustment table is updated in real-time based on these sorted values, ensuring that subsequent data traffic transmission prioritizes channels with higher health coefficients. Based on the sorting results, you can obtain a reasonable transmission channel order for subsequent data transmission and management operations.

[0098] The specific logic for generating sorted assignment values ​​is as follows:

[0099] The initial channel adjustment table corresponding to the first target is obtained as {K1,K2,…,K}. j ,…,K J}, and obtain the channel health coefficient table corresponding to the initial channel adjustment table. Let J be the channel health coefficient corresponding to the j-th transmission channel, where j = 1, 2, ..., J, and J is a positive integer.

[0100] Numerical analysis is performed on the channel health coefficients corresponding to J transmission channels. All channel health coefficients are scaled up proportionally, converted into positive integers, and the obtained positive integers are assigned as the sorting values ​​for the current channel health coefficients.

[0101] If there are identical sorting assignments, then sorting will be performed according to the corresponding transmission channel in the initial channel adjustment table;

[0102] Sort the channels according to their assigned values ​​and update the channel adjustment table in real time to obtain the updated channel adjustment table.

[0103] In this embodiment, for example, an IoT card connects to 6 IoT devices, corresponding to a total of 6 transmission channels. These 6 transmission channels are labeled as {K1, K2, K3, K4, K5, K6}. The transmission channels {K1, K2, K3, K4, K5, K6} are assigned a sorting value based on a channel health coefficient. Assuming the sorting values ​​are 5, 10, 16, 3, 2, and 12 respectively, the channel adjustment table is updated in real-time from largest to smallest using the sorting values. The updated channel adjustment table is then obtained, with the sorting method being {K3, K6, K2, K1, K4, K5}. The control module and the IoT card transmit data traffic according to the channel adjustment table from largest to smallest.

[0104] In the next preset time period, the transmission channels {K3,K6,K2,K1,K4,K5} are used as initialization channels. The channel health coefficients generated in the next preset time period are assigned sorting values ​​of 10, 5, 7, 10, 17, and 12. After updating the channel adjustment table in real time from large to small by sorting values, the sorting method of the transmission channels is {K4,K5,K3,K1,K2,K6}. The control module and IoT card transmit data traffic according to the channel adjustment table from large to small.

[0105] The preset time here is set manually. The preset time is a fixed time value. The previous moment and the next moment are based on the preset time provided by the user when using it. When there are the same sorting assignments in the sorting assignments generated by the transmission channel according to the channel health coefficient, the channel adjustment table is updated in real time from large to small. The transmission channels with the same sorting assignments are sorted according to the channel adjustment table of the previous moment.

[0106] The channel adjustment table at the previous moment here is also the initial channel adjustment table mentioned in this embodiment. Each updated channel adjustment table is converted into the initial channel adjustment table to facilitate subsequent updates of the channel adjustment table.

[0107] When updating the channel adjustment table in real time, the system dynamically adjusts the priority of data traffic transmission based on the sorting of the first target channel. By prioritizing channels with good health for data transmission, channel load balancing can be achieved, avoiding transmission delays or failures caused by overloading a single channel.

[0108] The early warning module generates monitoring and early warning instructions based on the channel health coefficient corresponding to the second target;

[0109] It should be noted here that: monitoring and early warning instructions are generated for the channel health coefficient corresponding to the transmission channel in abnormal use, which are used to issue early warning notifications to relevant personnel or equipment.

[0110] Here's a detailed explanation: Warning notifications may include disabling applications that consume network traffic for IoT devices, readjusting the network protocol for IoT devices to access their IoT SIM cards, or replacing the IoT devices.

[0111] The network protocol includes one or more of the following network protocols: MQTT protocol, HTTP protocol, CoAP protocol, AMQP protocol, and DDS protocol.

[0112] It should be noted that this invention can monitor, evaluate, and optimize the transmission channel connecting IoT devices and IoT cards, thereby improving the stability and performance of the IoT system. This embodiment primarily assesses the health of the transmission channel connecting IoT devices and IoT cards by collecting log stream records and multiple signal influence parameters. By calculating data health coefficients and signal health coefficients, the status of the transmission channel can be understood, identifying any anomalies or faults. The channel health coefficient is compared with a channel warning threshold, setting the transmission channel as either a first or second target. For the first target, sorting and assigning values ​​and updating the channel adjustment table in real time prioritize healthy channels for data transmission. For the second target, where the channel health coefficient is below the warning threshold, a monitoring and warning command is generated. This allows for timely monitoring of the channel's health status and provides warning notifications to information receiving devices, enabling appropriate measures to repair or adjust the transmission channel.

[0113] Example 2

[0114] Please see Figure 2 As shown in this embodiment, the IoT SIM card data traffic monitoring and early warning method based on data analysis is implemented based on an IoT SIM card data traffic monitoring and early warning system based on data analysis, including:

[0115] The transmission channel connecting the IoT device and the IoT card is labeled K. i Let i = 1, 2, ..., I, where I is a positive integer; mark the channel adjustment table of the transmission channels connecting the current I IoT devices and IoT cards as {K1, K2, ..., K...} i ,…,K I}, use the currently marked channel adjustment table as the initial channel adjustment table;

[0116] Real-time acquisition of log stream records in the corresponding IoT channel and multiple signal impact parameters affecting data traffic transmission;

[0117] A data health coefficient is established based on log stream records through normalization processing;

[0118] A signal health coefficient is established based on multiple signal influence parameters through standardization processing.

[0119] A channel health coefficient is generated by comprehensively evaluating and analyzing the transmission channel based on the data health coefficient and the signal health coefficient.

[0120] Based on the comparison between the channel health coefficient and the channel warning threshold, if the channel health coefficient is greater than or equal to the channel warning threshold, the corresponding transmission channel will be the first target; if the channel health coefficient is less than the channel warning threshold, the corresponding transmission channel will be the second target.

[0121] The sorting assignment is generated based on the channel health coefficient corresponding to the first target; the channel adjustment table is updated in real time from large to small according to the sorting assignment; after the channel adjustment table is updated, the IoT card transmits data traffic from large to small according to the channel adjustment table.

[0122] Based on the channel health coefficient corresponding to the second objective, a monitoring and early warning instruction is generated, and an early warning notification is sent to the information receiving device based on the monitoring and early warning instruction.

[0123] This invention enables the monitoring, evaluation, and optimization of the transmission channel connecting IoT devices and IoT cards, thereby improving the stability and performance of IoT systems. This embodiment primarily assesses the health of the transmission channel by collecting log stream records and multiple signal influence parameters. By calculating data health coefficients and signal health coefficients, the status of the transmission channel can be determined, identifying any anomalies or faults. The channel health coefficient is compared with a channel warning threshold, setting the transmission channel as either a first or second target. For the first target, a ranking and real-time update of the channel adjustment table prioritizes healthy channels for data transmission. For the second target, where the channel health coefficient is below the warning threshold, a monitoring and warning command is generated. This allows for timely monitoring of the channel's health status and provides warning notifications to information receiving devices, enabling appropriate measures to repair or adjust the transmission channel.

[0124] Example 3

[0125] An electronic device according to an exemplary embodiment includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0126] The processor executes the aforementioned IoT card data traffic monitoring and early warning system by calling the computer program stored in the memory.

[0127] This invention enables the monitoring, evaluation, and optimization of the transmission channel connecting IoT devices and IoT cards, thereby improving the stability and performance of IoT systems. This embodiment primarily assesses the health of the transmission channel by collecting log stream records and multiple signal influence parameters. By calculating data health coefficients and signal health coefficients, the status of the transmission channel can be determined, identifying any anomalies or faults. The channel health coefficient is compared with a channel warning threshold, setting the transmission channel as either a first or second target. For the first target, a ranking and real-time update of the channel adjustment table prioritizes healthy channels for data transmission. For the second target, where the channel health coefficient is below the warning threshold, a monitoring and warning command is generated. This allows for timely monitoring of the channel's health status and provides warning notifications to information receiving devices, enabling appropriate measures to repair or adjust the transmission channel.

[0128] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device can vary considerably depending on its configuration or performance. It can include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the stock algorithm trading method based on deep neural networks provided in the various method embodiments described above. The electronic device can also include other components for implementing device functions; for example, it can have wired or wireless network interfaces and input / output interfaces for input and output. Further details are not elaborated upon in the embodiments of this application.

[0129] Example 4

[0130] A computer-readable storage medium is shown in an exemplary embodiment, on which an erasable and rewritable computer program is stored;

[0131] When the computer program is run on a computer device, the computer device executes the aforementioned IoT card data traffic monitoring and early warning system based on data analysis.

[0132] This invention enables the monitoring, evaluation, and optimization of the transmission channel connecting IoT devices and IoT cards, thereby improving the stability and performance of IoT systems. This embodiment primarily assesses the health of the transmission channel by collecting log stream records and multiple signal influence parameters. By calculating data health coefficients and signal health coefficients, the status of the transmission channel can be determined, identifying any anomalies or faults. The channel health coefficient is compared with a channel warning threshold, setting the transmission channel as either a first or second target. For the first target, a ranking and real-time update of the channel adjustment table prioritizes healthy channels for data transmission. For the second target, where the channel health coefficient is below the warning threshold, a monitoring and warning command is generated. This allows for timely monitoring of the channel's health status and provides warning notifications to information receiving devices, enabling appropriate measures to repair or adjust the transmission channel.

[0133] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including at least one computer program, which is executable by a processor to perform the deep neural network-based stock algorithm trading method described in the above embodiments. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0134] In an exemplary embodiment, a computer program product or computer program is also provided, comprising one or more lines of program code stored in a computer-readable storage medium. One or more processors of an electronic device are capable of reading the one or more lines of program code from the computer-readable storage medium, and the one or more processors execute the one or more lines of program code, enabling the electronic device to execute the aforementioned stock algorithm trading method based on a deep neural network.

[0135] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0136] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0137] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0138] The above description is only an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0139] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0140] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0141] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0142] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0143] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data traffic monitoring and early warning system for IoT cards based on data analysis, characterized in that: include: The initialization module marks the transmission channel connecting the IoT device and the IoT card as... , , It is a positive integer; the current The channel adjustment table for the transmission channel connecting an IoT device and an IoT card is marked as follows: Use the currently marked channel adjustment table as the initial channel adjustment table; The monitoring module collects log stream records and multiple signal impact parameters that affect data traffic transmission in the corresponding IoT channel in real time. The analysis module establishes a data health coefficient based on log stream records through normalization processing; A signal health coefficient is established based on multiple signal influence parameters through standardization processing. The comprehensive analysis module performs a comprehensive evaluation and analysis of the transmission channel based on the data health coefficient and the signal health coefficient to generate the channel health coefficient. The comparison module compares the channel health coefficient with the channel warning threshold. If the channel health coefficient is greater than or equal to the channel warning threshold, the corresponding transmission channel will be the first target. If the channel health coefficient is less than the channel warning threshold, the corresponding transmission channel will be the second target; The control module sends the channel health coefficient corresponding to the first target to the sorting module, and sends the channel health coefficient corresponding to the second target to the early warning module. The sorting module generates sorting assignments based on the channel health coefficient corresponding to the first target; The channel adjustment table is updated in real time according to the sorting values ​​from largest to smallest. After the channel adjustment table is updated, the IoT card transmits data traffic according to the channel adjustment table from largest to smallest. The early warning module generates monitoring and early warning commands based on the channel health coefficient corresponding to the second target, and sends early warning notifications to the information receiving devices based on these commands. The logic for obtaining the data health coefficient includes: The log stream records include one or more of the following data: inbound data packets, outbound data packets, error data packets, and dropped data packets; Inbound data packets, outbound data packets, erroneous data packets, and dropped data packets are normalized respectively to obtain normalized inbound data packets, outbound data packets, erroneous data packets, and dropped data packets; and corresponding weight factors are assigned to the normalized inbound data packets, outbound data packets, erroneous data packets, and dropped data packets. The normalized inbound data packets, outbound data packets, erroneous data packets, and dropped data packets are each multiplied by their respective weighting factors, and then the average of the weighted values ​​is calculated to obtain the data health coefficient. The logic for obtaining the signal health coefficient includes: Signal influencing parameters include one or more of the following: bandwidth, signal-to-noise ratio, network delay, signal strength, previous signal transmission rate, and bit synchronization signal phase jitter rate; The bandwidth Signal-to-noise ratio Network latency Signal strength Signal transmission rate at the previous moment Phase jitter rate of bit synchronization signal After removing units, dimensionless processing is performed to establish the signal health coefficient. The specific formula is as follows: ; In the formula, , , , , , , , , , , , and They are respectively the bandwidth Signal-to-noise ratio Network latency Signal strength Signal transmission rate at the previous moment Phase jitter rate of bit synchronization signal The corresponding weights This represents the maximum bandwidth of the corresponding transmission channel. This represents the minimum bandwidth of the corresponding transmission channel; The signal strength corresponding to the transmission channel at the previous moment; Channel health coefficient The acquisition logic includes: Health coefficient of data and signal health coefficient The health coefficient of the channel can be calculated using a formula. The specific analysis formula is as follows: , In the formula, , These are respectively represented as the weighting factors corresponding to the set data health coefficient and signal health coefficient; For the channel health coefficient The analysis method is as follows: Channel health coefficient With the preset channel warning threshold Perform a comparison; like Then the channel health coefficient The higher the value, the stronger the anti-interference capability of the corresponding transmission channel; therefore, the corresponding transmission channel is marked as the first target. like Then the channel health coefficient The lower the value, the worse the anti-interference capability of the corresponding transmission channel; therefore, the corresponding transmission channel is marked as the second target. The logic for generating the sorting assignment is as follows: Obtain the initialization channel adjustment table corresponding to the first target. And obtain the channel health coefficient table corresponding to the initial channel adjustment table. , For the first The channel health coefficient corresponding to each transmission channel. , It is a positive integer; Will Numerical analysis is performed on the channel health coefficients corresponding to each transmission channel. All channel health coefficients are scaled up proportionally, converted into positive integers, and the obtained positive integers are used as the sorting values ​​for the current channel health coefficients. If there are identical sorting assignments, then sorting will be performed according to the corresponding transmission channel in the initial channel adjustment table; Sort the channels according to their assigned values; and update the channel adjustment table in real time to obtain the updated channel adjustment table. The warning notification includes one or more of the following operation instructions: closing applications that consume network traffic on IoT devices, readjusting the network protocol for IoT devices to access IoT cards, and replacing IoT devices; the network protocol includes one or more of the following network protocols: MQTT protocol, HTTP protocol, CoAP protocol, AMQP protocol, and DDS protocol.

2. A data analysis-based IoT SIM card data traffic monitoring and early warning method, which is based on the implementation of the data analysis-based IoT SIM card data traffic monitoring and early warning system described in claim 1, characterized in that: include: The transmission channel connecting the IoT device and the IoT card is marked as... , , It is a positive integer; the current The channel adjustment table for the transmission channel connecting an IoT device and an IoT card is marked as follows: Use the currently marked channel adjustment table as the initial channel adjustment table; Real-time acquisition of log stream records in the corresponding IoT channel and multiple signal impact parameters affecting data traffic transmission; A data health coefficient is established based on log stream records through normalization processing; A signal health coefficient is established based on multiple signal influence parameters through standardization processing. A channel health coefficient is generated by comprehensively evaluating and analyzing the transmission channel based on the data health coefficient and the signal health coefficient. Based on the comparison between the channel health coefficient and the channel warning threshold, if the channel health coefficient is greater than or equal to the channel warning threshold, the corresponding transmission channel is marked as the first target; If the channel health coefficient is less than the channel warning threshold, the corresponding transmission channel will be marked as the second target; The sorting and assignment are based on the channel health coefficient corresponding to the first objective; The channel adjustment table is updated in real time according to the sorting values ​​from largest to smallest. After the channel adjustment table is updated, the IoT card transmits data traffic according to the channel adjustment table from largest to smallest. Based on the channel health coefficient corresponding to the second objective, a monitoring and early warning instruction is generated, and an early warning notification is sent to the information receiving device based on the monitoring and early warning instruction.

3. An electronic device, characterized in that, include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the IoT card data traffic monitoring and early warning method according to claim 2 by calling the computer program stored in the memory.

4. A computer-readable storage medium, characterized in that: The device stores instructions that, when executed on a computer, cause the computer to perform a data analysis-based IoT card data traffic monitoring and early warning method as described in claim 2.

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