Communication quality monitoring method and device based on internet of things and storage medium

Abstract

The present application relates to the field of data monitoring, and more particularly to a communication quality monitoring method and device based on the Internet of Things and a storage medium, the present application obtains the communication characteristics of the Internet of Things terminal as a prior sample, performs communication ripple analysis on each Internet of Things terminal, determines the time sequence ripple conduction relationship and the synchronous ripple relationship between different Internet of Things terminals, summarizes the law, and constructs a communication ripple relationship database for the Internet of Things terminal, in the subsequent monitoring process, the ripple starting terminal is captured, the communication ripple relationship database is called, the Internet of Things terminal is selected, and the corresponding constraint law is used for collaborative analysis to analyze the potential abnormal Internet of Things terminal. The present application analyzes the abnormality of the data transmission process from the communication characteristic level, has strong applicability, and uses the space factors of the Internet of Things and the propagation characteristics of the monitored physical quantity to construct a communication ripple relationship database, and then performs collaborative analysis on the Internet of Things terminal, thereby improving the abnormal monitoring efficiency and sensitivity.

Description

Technical Field

[0001] This invention relates to the field of data monitoring, and more particularly to a communication quality monitoring method, device, and storage medium based on the Internet of Things. Background Technology

[0002] The Internet of Things (IoT) enables environmental sensing, data acquisition, and remote monitoring through various sensor terminals, and has been widely applied in fields such as industrial manufacturing, smart cities, and environmental monitoring. In IoT systems, the stable operation of sensor terminals and reliable data transmission are fundamental to ensuring monitoring effectiveness; therefore, monitoring the sensor communication and transmission process is crucial.

[0003] For example, Chinese Patent Publication No. CN117176560A discloses a monitoring equipment supervision system and method based on the Internet of Things (IoT), specifically relating to the field of IoT technology. The system includes a data acquisition module, a data preprocessing module, a data transmission module, an edge device processing module, a cloud platform processing center, an equipment supervision module, and a remote control module. The system trains a machine learning model through the cloud platform processing center, compresses and optimizes the trained model to reduce its size and complexity. The edge device processing module performs model inference locally, avoiding the transmission of large amounts of raw data to the cloud platform processing center. The remote control module remotely controls, configures, and manages the monitoring equipment, improving the efficiency of monitoring equipment management and retaining all remote control, configuration, and management operation logs, which facilitates subsequent auditing, troubleshooting, and maintenance work, ensuring the integrity and traceability of operations.

[0004] However, the following problems still exist in the existing technology.

[0005] In existing technologies, the method of analyzing monitoring values ​​has poor versatility for different models of IoT devices. Furthermore, existing methods mostly focus on independent analysis of a single terminal, ignoring the ripple correlations that objectively exist between multiple terminals at the communication characteristic level due to spatial factors of IoT deployment and the propagation characteristics of the monitored physical quantities. There is a lack of effective means to use ripple correlations for cross-terminal collaborative analysis, resulting in poor analysis efficiency and accuracy. Summary of the Invention

[0006] To address this, the present invention provides a communication quality monitoring method, device, and storage medium based on the Internet of Things (IoT), which overcomes the problems of existing IoT monitoring methods relying on direct parsing of sensor monitoring values, poor versatility for different types of equipment, failure to consider the ripple correlation of multi-terminal communication characteristics caused by deployment space factors and the propagation characteristics of the monitored quantity, and lack of means to utilize this correlation for cross-terminal collaborative analysis, resulting in low efficiency and poor accuracy in anomaly detection.

[0007] To achieve the above objectives, in one aspect, the present invention provides a communication quality monitoring method based on the Internet of Things, which includes:

[0008] The communication characteristics of several IoT terminals during data transmission are obtained as prior samples.

[0009] Based on the communication characteristics, communication ripple analysis is performed for each of the IoT terminals, including verifying the temporal ripple transmission relationship between each IoT terminal based on the temporal changes of the communication characteristics and verifying the synchronous ripple relationship between each IoT terminal based on the synchronous changes of the communication characteristics.

[0010] Based on the prior samples, the constraints of communication characteristics between IoT terminals in terms of temporal transmission and synchronous change direction are summarized. Based on the results of communication ripple analysis, other IoT terminals with communication ripple relationships are identified to construct a communication ripple relationship database corresponding to IoT terminals.

[0011] The system captures the communication characteristics of each transmission link in real time, determines the ripple initiation terminal based on the changes in communication characteristics, calls the communication ripple relationship database corresponding to the ripple initiation terminal to select the IoT terminal to be observed, and calls the corresponding constraint rules.

[0012] Obtain the communication characteristics corresponding to each IoT terminal, verify the matching relationship of the constraint rules corresponding to the communication characteristics, and determine the potential abnormal IoT terminals based on the verification results.

[0013] Furthermore, the process of verifying the temporal ripple propagation relationship between various IoT terminals based on the temporal changes in communication characteristics includes,

[0014] Capture and respond to changes in the communication characteristics of different IoT terminals;

[0015] Determine the response events of other IoT terminals within the delayed time domain segment after a single IoT terminal's response event, in order to construct a group of terminals of interest, and record the response time interval of the corresponding response events;

[0016] The response time intervals and the probability of occurrence of the same target terminal group are statistically analyzed to filter out the effective target terminal groups.

[0017] It was determined that there is a temporal ripple propagation relationship among the IoT terminals corresponding to the effective attention terminal groups;

[0018] The terminal group under observation includes the serial numbers of two IoT terminals. Terminal groups with a response time interval variance less than a predetermined variance threshold and an occurrence probability greater than a predetermined occurrence probability are selected as valid terminal groups under observation.

[0019] Furthermore, the process of identifying response events includes,

[0020] Typical fluctuation range of statistical communication characteristics;

[0021] If a communication feature deviates from its typical floating range and the rate of change of the communication feature at the time of deviation is greater than the rate of change threshold, then a response event is determined to have occurred.

[0022] Furthermore, the process of verifying the synchronization ripple relationship between various IoT terminals based on the synchronous changes in communication characteristics includes,

[0023] Determine the statistical probability of synchronous response events occurring between IoT terminals;

[0024] If the statistical probability is greater than a predetermined statistical probability threshold, then a synchronous ripple relationship is determined to exist.

[0025] Furthermore, the process of constructing a communication ripple relationship database corresponding to IoT terminals includes,

[0026] Determine the serial numbers of other IoT terminals with communication ripple relationships and their corresponding constraint rules;

[0027] The sequence number and constraint rules are formed into a binary data set and stored in the corresponding communication ripple relationship database of the Internet of Things terminal.

[0028] Among them, if there is a temporal ripple transmission relationship or a synchronous ripple relationship, it is considered that there is a communication ripple relationship, and the IoT terminal corresponds one-to-one with the communication ripple relationship database.

[0029] Furthermore, the process of summarizing the constraints of communication characteristics between various IoT terminals in terms of temporal transmission and synchronous change direction based on the prior samples, and forming a communication ripple relationship database, includes the following:

[0030] For IoT terminals with temporal ripple propagation relationships, determine the corresponding response time interval fluctuation range;

[0031] Determine the range of variation in the corresponding communication characteristics when a response event occurs;

[0032] The fluctuation range of the response time interval and the fluctuation range of the difference are used as constraints.

[0033] For IoT terminals with synchronous ripple relationships, the communication characteristic fluctuation range when the IoT terminal responds to an event is determined, and the communication characteristic fluctuation range is used as a constraint law.

[0034] Furthermore, the process of selecting the IoT terminals to be observed and invoking constraint rules to identify potentially abnormal IoT terminals includes,

[0035] All IoT terminals in the communication ripple relation database are identified as observation objects to determine the occurrence of response events;

[0036] Determine the actual response time interval between IoT terminals, the actual difference of the corresponding communication characteristics of IoT terminals when a response event occurs, and the actual value of the corresponding communication characteristics when a response event occurs;

[0037] For IoT terminals with temporal ripple propagation relationships, verify the matching relationship between the actual response time interval and the corresponding response time interval fluctuation range, and verify the matching relationship between the actual difference and the corresponding difference fluctuation range.

[0038] For IoT terminals with synchronous ripple relationships, verify the matching relationship between actual values ​​and the fluctuation range of communication characteristics;

[0039] IoT terminals with invalid matching verification are identified as potentially abnormal IoT terminals.

[0040] Furthermore, the extracted communication features include one of the following: information entropy of message payload distribution, message transmission frequency within the time window, and uplink / downlink byte ratio.

[0041] On the other hand, a storage medium is also provided, which stores a computer program that, when executed by a processor, can be used to perform the aforementioned Internet of Things-based communication quality monitoring method.

[0042] On the other hand, an apparatus is also provided, comprising:

[0043] One or more processors;

[0044] Memory;

[0045] and one or more programs,

[0046] The one or more programs are configured to be executed by one or more processors, and the memory includes the storage medium.

[0047] Compared with existing technologies, this invention obtains the communication characteristics of IoT terminals during data transmission as prior samples, performs communication ripple analysis on each IoT terminal, determines the temporal ripple propagation relationship and synchronous ripple relationship between different IoT terminals, summarizes the changing patterns, and constructs a communication ripple relationship database for IoT terminals. In subsequent monitoring, the ripple initiation terminal is captured, the corresponding communication ripple relationship database is invoked, IoT terminals are selected, and collaborative analysis is performed using corresponding constraint rules to analyze potential abnormal IoT terminals. This invention analyzes anomalies in the data transmission process from the perspective of communication characteristics, has strong applicability, and utilizes the spatial factors of IoT deployment and the propagation characteristics of the monitored physical quantities to construct a communication ripple relationship database, subsequently performing collaborative analysis on IoT terminals, thereby improving the efficiency and sensitivity of anomaly monitoring.

[0048] In particular, this invention captures communication characteristics during data transmission. When an IoT terminal completes data collection and transmission, the collected data tends to be stable under stable operating conditions, and the communication characteristics also show a stable trend. When the collected data fluctuates or changes, the relevant communication characteristics during transmission will also passively respond and change regularly due to factors such as the complexity and volume of the data content. Based on this, this invention does not read the data collected by the IoT terminal, but uses communication characteristics to reflect changes in the data from the side, avoiding the differences in data formats and protocols between different models of terminals, realizing collaborative monitoring of multiple models of IoT terminals, rapid monitoring and analysis, and improving the universality of monitoring.

[0049] In particular, this invention performs communication ripple analysis on IoT terminals. Due to the spatial layout of sensors and the physical quantities being measured in IoT terminals, some physical quantities collected by IoT terminals will exhibit communication ripple relationships. For example, for IoT photography devices at different angles in the same space, the entry of a dynamic object into the space will cause fluctuations in the images of multiple IoT photography devices, leading to coordinated fluctuations in monitoring data, and the communication characteristics will also show the same trend. Another example is in linear scenarios such as industrial pipe corridors or transmission corridors, where vibration or temperature sensors are sequentially deployed along the flow direction of the medium. When a leak occurs in the medium or there are abnormal temperature fluctuations, the abnormal physical quantities propagate along the path, and the communication characteristics exhibit a temporal transmission pattern. Based on this, this invention analyzes the temporal ripple transmission relationship and synchronous ripple relationship of IoT terminals through communication ripple analysis, providing support for the subsequent creation of a communication ripple relationship database. This database is then used for collaborative monitoring of multiple IoT terminals, extracting only communication characteristics for analysis, thus improving the efficiency of anomaly monitoring.

[0050] In particular, this invention constructs a communication ripple relationship database, which stores the communication ripple relationships and constraint rules of different IoT terminals. When a ripple initiation terminal is detected, the communication ripple relationship database is invoked. Then, the IoT terminals to be observed are selected for collaborative monitoring. Using constraint rules as guidance, it is observed whether multiple IoT terminals satisfy the communication ripple relationship. Collaborative monitoring of multiple IoT terminals avoids the lack of reference and the inability to detect fluctuations in noise when monitoring a single terminal, resulting in better sensitivity. Collaborative monitoring allows the communication characteristic fluctuations of a single terminal to be effectively amplified through cross-terminal pattern matching and cross-validation, making it easier to detect anomalies and improving monitoring sensitivity. Attached Figure Description

[0051] Figure 1 This is a schematic diagram illustrating the steps of an IoT-based communication quality monitoring method according to an embodiment of the invention.

[0052] Figure 2 A logic block diagram for selecting effective terminal groups in the embodiments of the invention;

[0053] Figure 3 This is a logic block diagram for determining a response event according to an embodiment of the invention;

[0054] Figure 4 A logic block diagram for identifying potential anomalies in IoT terminals based on the call constraint rules of an embodiment of the invention. Detailed Implementation

[0055] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0056] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] Please see Figure 1 The diagram illustrates the steps of an IoT-based communication quality monitoring method according to an embodiment of the invention. The IoT-based communication quality monitoring method according to this embodiment includes:

[0058] Step S1: Obtain communication characteristics of several IoT terminals during data transmission as prior samples;

[0059] Step S2, perform communication ripple analysis for each of the IoT terminals based on the communication characteristics, including verifying the temporal ripple transmission relationship between each IoT terminal based on the temporal changes of the communication characteristics and verifying the synchronous ripple relationship between each IoT terminal based on the synchronous changes of the communication characteristics.

[0060] Step S3: Based on the prior samples, summarize the constraint rules of communication characteristics between IoT terminals in terms of temporal transmission and synchronous change direction, and determine other IoT terminals with communication ripple relationships based on the results of communication ripple analysis, so as to construct a communication ripple relationship database corresponding to IoT terminals.

[0061] Step S4: Capture the communication characteristics of each transmission link in real time, determine the ripple initiation terminal based on the changes in communication characteristics, call the communication ripple relationship database corresponding to the ripple initiation terminal to select the IoT terminal to be observed, and call the corresponding constraint rules.

[0062] Step S5: Obtain the communication characteristics corresponding to each IoT terminal, verify the matching relationship of the constraint rules corresponding to the communication characteristics, and determine the potential abnormal IoT terminals based on the verification results.

[0063] In practice, there are no restrictions on the specific form of IoT terminals. They can be any device with data acquisition and network transmission capabilities, including but not limited to image sensors, temperature sensors, humidity sensors, vibration sensors, etc., which will not be elaborated further.

[0064] In practice, when acquiring communication characteristics, only the message header needs to be read; the message payload does not need to be read.

[0065] The process of determining the information entropy of the message payload distribution involves extracting the byte stream of the payload portion from a single message or a message sequence composed of multiple consecutive messages, treating the payload bytes as a symbol sequence without actual semantics, counting the frequency of occurrence of each byte value, calculating its probability distribution, and then calculating the information entropy of this distribution as the information entropy of the message payload distribution based on the Shannon information entropy formula.

[0066] Understandably, the Shannon information entropy calculation formula only needs to determine the number of possible values ​​of the random variable, the probability distribution of the random variable, and the base of the logarithm in the formula. In practice, the basic unit of the payload is a byte, and there are 256 possible values ​​for a single byte, so there are 256 random variables. The probability distribution of the random variables is known. Usually, the base of the logarithm is 2, so that the unit of the calculated Shannon information entropy is bits.

[0067] It is understandable that when the monitored physical quantity fluctuates, the complexity of the raw data collected by the sensor increases. Even after compression or encryption, the randomness of the payload bytes will increase accordingly, which is reflected in the increase of the information entropy value. The calculation process of this feature only involves statistical operations on the payload bytes, and does not involve the parsing and semantic restoration of the payload content.

[0068] To determine the transmission frequency of messages within a time window, a time window of unit duration can be set as the statistical unit. The number of messages belonging to the same data stream within this time window is counted to determine the transmission frequency. The unit duration is 1 second. Of course, those skilled in the art can adjust the length of the time window, which will not be elaborated here.

[0069] Understandably, when the amount of data collected in a single instance increases significantly due to abnormal fluctuations in the monitored physical quantity, the network protocol stack may passively trigger IP fragmentation or TCP segmentation, splitting a single application layer data session into multiple packets for continuous transmission. This results in fluctuations in the packet transmission frequency observed by the network layer within a time window. Therefore, this characteristic reflects the change in passive transmission behavior of the network layer caused by the increase in data volume.

[0070] For the ratio of uplink to downlink bytes, the time window of unit duration is used as the statistical unit. The ratio is obtained by calculating the number of uplink bytes and downlink bytes.

[0071] It is understandable that IoT terminals mainly report data, and the uplink / downlink byte ratio remains in a relatively stable range. When the monitored physical quantity is abnormal, the uplink data volume may surge or the server may issue a large number of control signals such as confirmation, retransmission requests, and parameter adjustment instructions due to abnormal alarms, resulting in a significant deviation in the uplink / downlink byte ratio.

[0072] In practice, the information entropy of the message payload distribution can be prioritized as a communication characteristic.

[0073] This invention captures communication characteristics during data transmission. When an IoT terminal completes data collection and transmission, under stable operating conditions, the collected data tends to be stable, and the communication characteristics also exhibit a stable trend. However, when the collected data fluctuates or changes, the relevant communication characteristics during transmission will passively respond and undergo regular changes due to factors such as the complexity and volume of the data content. Therefore, this invention does not read the data collected by the IoT terminal but uses communication characteristics to reflect data changes indirectly, avoiding differences in data formats and protocols between different terminal models. This enables collaborative monitoring of multiple IoT terminal models, rapid monitoring and analysis, and improved monitoring universality.

[0074] Specifically, please refer to Figure 2 As shown, it is a logical block diagram for selecting effective target terminal groups according to an embodiment of the invention. The process of verifying the temporal ripple transmission relationship between each IoT terminal based on the temporal changes of communication characteristics includes,

[0075] Capture and respond to changes in the communication characteristics of different IoT terminals;

[0076] Determine the response events of other IoT terminals within the delayed time domain segment after a single IoT terminal's response event, in order to construct a group of terminals of interest, and record the response time interval of the corresponding response events;

[0077] The response time intervals and the probability of occurrence of the same target terminal group are statistically analyzed to filter out the effective target terminal groups.

[0078] It was determined that there is a temporal ripple propagation relationship among the IoT terminals corresponding to the effective attention terminal groups;

[0079] The terminal group under observation includes the serial numbers of two IoT terminals. Terminal groups with a response time interval variance less than a predetermined variance threshold and an occurrence probability greater than a predetermined occurrence probability are selected as valid terminal groups under observation.

[0080] In practice, the first sequence number of the terminal group is the sequence number of the IoT terminal that first experiences a response event.

[0081] The delayed time domain segment is selected within the interval [2min, 5min]. The lower limit of this interval is set to provide sufficient capture time for the physical quantity anomaly to propagate along the spatial path to the adjacent terminal, ensuring that the response event chain with temporal correlation can be completely captured. The upper limit of this interval is set to avoid the introduction of noise response unrelated to the target physical event due to excessive time, and to prevent the true temporal ripple propagation relationship from being obscured.

[0082] In practice, the probability of a terminal group being monitored is the probability that after the IoT terminal corresponding to the first serial number in the monitored terminal group has a response event, the IoT terminal corresponding to the second serial number in the monitored terminal group will also have a response event.

[0083] In practice, the predetermined variance threshold is set in advance to measure typical events where the response time interval is stable and the timing pattern is stable. In practice, the mean floating variance of the corresponding communication characteristics of the IoT terminal under stable operating conditions over a certain period of time can be statistically analyzed to reflect the level of floating variance under stable conditions. For the predetermined variance threshold, considering that the response time interval is jointly determined by the timing of the response events of the communication characteristics of the two terminals, its fluctuation is affected by the superposition of the fluctuations of the communication characteristics at both ends, and the uncertainty is higher than the variance level of a single communication characteristic, the mean floating variance is slightly increased to reflect the stable response time interval. In practice, it is set to between 1.05 and 1.1 times the mean floating variance, preferably 1.05.

[0084] In practice, it is recommended to set the predetermined occurrence probability to a relatively high value in order to filter out IoT terminal groups with obvious temporal ripple transmission relationships. Preferably, the occurrence probability is set between 70% and 90%, with 70% being the most preferred. This probability is sufficient to reflect that the sequential responses of the two response times have obvious regularity and are not random, and avoids the possibility of failing to capture effective terminal groups of interest due to an excessively high probability.

[0085] Understandably, the criteria for identifying the terminal group are relatively stringent, with the aim of filtering out specific typical working conditions with obvious regularities in industrial scenarios, and then using multi-terminal collaborative monitoring to improve accuracy in the backend.

[0086] Specifically, please refer to Figure 3 As shown, it is a logic block diagram for determining a response event according to an embodiment of the invention. The process of determining the response event includes,

[0087] Typical fluctuation range of statistical communication characteristics;

[0088] If a communication feature deviates from its typical floating range and the rate of change of the communication feature at the time of deviation is greater than the rate of change threshold, then a response event is determined to have occurred.

[0089] The process of determining typical floating ranges during implementation includes,

[0090] The data collected by IoT terminals within a period is statistically analyzed. Communication characteristics within the corresponding time period under typical operating conditions with stable data are selected, and the fluctuation range of the communication characteristics is statistically analyzed as the typical fluctuation range. For numerical sensors, the typical operating condition is when the values ​​are stable and the maximum fluctuation is within 5%. For image sensors, the typical operating condition can be the case where there are no moving objects in the image. To maintain the data representativeness of the typical fluctuation range, the period can be set to one week, that is, the typical fluctuation range is updated weekly to be applicable to industrial application scenarios.

[0091] In practice, considering the impact of individual values ​​on the fluctuation range, when calculating the fluctuation range, the normal distribution of the corresponding values ​​is first obtained, and the 95% confidence interval is used as the fluctuation range.

[0092] In practice, the role of the rate of change threshold is to determine the situation where the rate of change is higher than the rate of change corresponding to the stable operating condition. When determining the rate of change threshold, the statistical distribution of the rate of change of the communication characteristics of the IoT terminal within the statistical period is pre-statistically calculated, and the rate of change corresponding to the 75th percentile is used as the rate of change threshold.

[0093] Specifically, the process of verifying the synchronization ripple relationship between various IoT terminals based on the synchronous changes in communication characteristics includes,

[0094] Determine the statistical probability of synchronous response events occurring between IoT terminals;

[0095] If the statistical probability is greater than a predetermined statistical probability threshold, then a synchronous ripple relationship is determined to exist.

[0096] In practice, the statistical probability threshold can be set with reference to the probability of occurrence. Essentially, it quantifies that the synchronous response of two response times has a regularity and is not accidental. It can be set with the same value as the probability of occurrence.

[0097] Given that the conditions for complete synchronization are quite stringent, in practice, response time intervals within 1 second are considered to be synchronous.

[0098] This invention performs communication ripple analysis on IoT terminals. Due to the spatial layout of sensors and the measured physical quantities in IoT terminals, some physical quantities collected by IoT terminals exhibit communication ripple relationships. For example, for IoT photography devices at different angles in the same space, the entry of a dynamic object into the space will cause fluctuations in the images of multiple IoT photography devices, leading to coordinated fluctuations in monitoring data, and the communication characteristics will also show the same trend. Another example is in linear scenarios such as industrial pipe corridors or transmission corridors, where vibration or temperature sensors are sequentially deployed along the flow direction of the medium. When a leak occurs in the medium or there are abnormal temperature fluctuations, the abnormal physical quantities propagate along the path, and the communication characteristics exhibit a temporal transmission pattern. Based on this, this invention analyzes the temporal ripple transmission relationship and synchronous ripple relationship of IoT terminals through communication ripple analysis, providing support for the subsequent creation of a communication ripple relationship database. This database is then used for collaborative monitoring of multiple IoT terminals, extracting only communication characteristics for analysis, thus improving the efficiency of anomaly monitoring.

[0099] Specifically, the process of constructing a communication ripple relationship database corresponding to IoT terminals includes,

[0100] Determine the serial numbers of other IoT terminals with communication ripple relationships and their corresponding constraint rules;

[0101] The sequence number and constraint rules are formed into a binary data set and stored in the corresponding communication ripple relationship database of the Internet of Things terminal.

[0102] Among them, if there is a temporal ripple transmission relationship or a synchronous ripple relationship, it is considered that there is a communication ripple relationship, and the IoT terminal corresponds one-to-one with the communication ripple relationship database.

[0103] It is understandable that the constraint rules apply to two IoT terminals. Therefore, constructing a binary array with the serial number and the constraint rules to form a correspondence facilitates backend retrieval.

[0104] Specifically, the process of summarizing the constraints of communication characteristics between various IoT terminals in terms of temporal transmission and synchronization direction based on the prior samples, and forming a communication ripple relationship database, includes the following:

[0105] For IoT terminals with temporal ripple propagation relationships, determine the corresponding response time interval fluctuation range;

[0106] Determine the range of variation in the corresponding communication characteristics when a response event occurs;

[0107] The fluctuation range of the response time interval and the fluctuation range of the difference are used as constraints.

[0108] For IoT terminals with synchronous ripple relationships, the communication characteristic fluctuation range when the IoT terminal responds to an event is determined, and the communication characteristic fluctuation range is used as a constraint law.

[0109] Understandably, when there are temporal relationships, the laws of temporal transmission are fully utilized, thus the fluctuation range of the response time interval and the fluctuation range of the difference are statistically analyzed.

[0110] This invention constructs a communication ripple relationship database, which stores the communication ripple relationships and constraint rules of different IoT terminals. When a ripple initiation terminal is detected, the communication ripple relationship database is invoked. Then, the IoT terminals to be observed are selected for collaborative monitoring. Using constraint rules as guidance, it is observed whether multiple IoT terminals satisfy the communication ripple relationship. Collaborative monitoring of multiple IoT terminals avoids the lack of reference and the inability to detect fluctuations in noise when monitoring a single terminal. This results in better sensitivity. Collaborative monitoring allows the communication characteristic fluctuations of a single terminal to be effectively amplified through cross-terminal pattern matching and cross-validation, making it easier to detect anomalies and improving monitoring sensitivity.

[0111] Specifically, please refer to Figure 4 The diagram shown is a logical block diagram of an embodiment of the invention for identifying potentially abnormal IoT terminals by invoking constraint rules. The process of selecting the IoT terminal to be observed and invoking constraint rules to identify potentially abnormal IoT terminals includes:

[0112] All IoT terminals in the communication ripple relation database are identified as observation objects to determine the occurrence of response events;

[0113] Determine the actual response time interval between IoT terminals, the actual difference of the corresponding communication characteristics of IoT terminals when a response event occurs, and the actual value of the corresponding communication characteristics when a response event occurs;

[0114] For IoT terminals with temporal ripple propagation relationships, verify the matching relationship between the actual response time interval and the corresponding response time interval fluctuation range, and verify the matching relationship between the actual difference and the corresponding difference fluctuation range.

[0115] For IoT terminals with synchronous ripple relationships, verify the matching relationship between actual values ​​and the fluctuation range of communication characteristics;

[0116] IoT terminals with invalid matching verification are identified as potentially abnormal IoT terminals.

[0117] During implementation, when matching, the actual response time interval is compared with the response time interval fluctuation range. If the actual response time interval falls within the response time interval fluctuation range, the matching relationship is considered valid.

[0118] The actual difference is compared with the corresponding difference fluctuation range. If the actual difference falls within the difference fluctuation range, the matching relationship is considered valid.

[0119] The actual value of the communication feature is compared with the floating range of the communication feature. If the actual value falls within the floating range of the communication feature, the matching relationship is considered valid.

[0120] Specifically, the extracted communication features include one of the following: information entropy of message payload distribution, message transmission frequency within a time window, and uplink / downlink byte ratio.

[0121] Once a potentially abnormal IoT terminal is identified, maintenance personnel can be notified to carry out maintenance or other follow-up work, which will not be elaborated further.

[0122] The embodiment also provides a storage medium storing a computer program, which, when executed by a processor, can be used to perform the IoT-based communication quality monitoring method.

[0123] The embodiments also provide an apparatus comprising:

[0124] One or more processors;

[0125] Memory;

[0126] and one or more programs,

[0127] The one or more programs are configured to be executed by one or more processors, and the memory includes the storage medium.

[0128] The technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0129] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A communication quality monitoring method based on the Internet of Things, characterized in that, include: The communication characteristics of several IoT terminals during data transmission are obtained as prior samples. Based on the communication characteristics, communication ripple analysis is performed for each of the IoT terminals, including verifying the temporal ripple transmission relationship between each IoT terminal based on the temporal changes of the communication characteristics and verifying the synchronous ripple relationship between each IoT terminal based on the synchronous changes of the communication characteristics. Based on the prior samples, the constraints of communication characteristics between IoT terminals in terms of temporal transmission and synchronous change direction are summarized. Based on the results of communication ripple analysis, other IoT terminals with communication ripple relationships are identified to construct a communication ripple relationship database corresponding to IoT terminals. The system captures the communication characteristics of each transmission link in real time, determines the ripple initiation terminal based on the changes in communication characteristics, calls the communication ripple relationship database corresponding to the ripple initiation terminal to select the IoT terminal to be observed, and calls the corresponding constraint rules. Obtain the communication characteristics corresponding to each IoT terminal, verify the matching relationship of the constraint rules corresponding to the communication characteristics, and determine the potential abnormal IoT terminals based on the verification results.

2. The communication quality monitoring method based on the Internet of Things according to claim 1, characterized in that, The process of verifying the temporal ripple propagation relationship between various IoT terminals based on the temporal changes in communication characteristics includes, Capture and respond to changes in the communication characteristics of different IoT terminals; Determine the response events of other IoT terminals within the delayed time domain segment after a single IoT terminal's response event, in order to construct a group of terminals of interest, and record the response time interval of the corresponding response events; The response time intervals and the probability of occurrence of the same target terminal group are statistically analyzed to filter out the effective target terminal groups. It was determined that there is a temporal ripple propagation relationship among the IoT terminals corresponding to the effective attention terminal groups; The terminal group under observation includes the serial numbers of two IoT terminals. Terminal groups with a response time interval variance less than a predetermined variance threshold and an occurrence probability greater than a predetermined occurrence probability are selected as valid terminal groups under observation.

3. The communication quality monitoring method based on the Internet of Things according to claim 2, characterized in that, The process of determining a response event includes, Typical fluctuation range of statistical communication characteristics; If a communication feature deviates from its typical floating range and the rate of change of the communication feature at the time of deviation is greater than the rate of change threshold, then a response event is determined to have occurred.

4. The communication quality monitoring method based on the Internet of Things according to claim 3, characterized in that, The process of verifying the synchronization ripple relationship between IoT terminals based on the synchronous changes in communication characteristics includes: Determine the statistical probability of synchronous response events occurring between IoT terminals; If the statistical probability is greater than a predetermined statistical probability threshold, then a synchronous ripple relationship is determined to exist.

5. The communication quality monitoring method based on the Internet of Things according to claim 4, characterized in that, The process of constructing a communication ripple relational database corresponding to IoT terminals includes, Determine the serial numbers of other IoT terminals with communication ripple relationships and their corresponding constraint rules; The sequence number and constraint rules are formed into a binary data set and stored in the corresponding communication ripple relationship database of the Internet of Things terminal. Among them, if there is a temporal ripple transmission relationship or a synchronous ripple relationship, it is considered that there is a communication ripple relationship, and the IoT terminal corresponds one-to-one with the communication ripple relationship database.

6. The communication quality monitoring method based on the Internet of Things according to claim 5, characterized in that, The process of summarizing the constraints of communication characteristics between various IoT terminals in terms of temporal transmission and synchronous change direction based on the prior samples, and forming a communication ripple relationship database, includes the following: For IoT terminals with temporal ripple propagation relationships, determine the corresponding response time interval fluctuation range; Determine the range of variation in the corresponding communication characteristics when a response event occurs; The fluctuation range of the response time interval and the fluctuation range of the difference are used as constraints. For IoT terminals with synchronous ripple relationships, the communication characteristic fluctuation range when the IoT terminal responds to an event is determined, and the communication characteristic fluctuation range is used as a constraint law.

7. The communication quality monitoring method based on the Internet of Things according to claim 1, characterized in that, The process of selecting the IoT terminals to be observed and applying constraint rules to identify potentially abnormal IoT terminals includes: All IoT terminals in the communication ripple relation database are identified as observation objects to determine the occurrence of response events; Determine the actual response time interval between IoT terminals, the actual difference of the corresponding communication characteristics of IoT terminals when a response event occurs, and the actual value of the corresponding communication characteristics when a response event occurs; For IoT terminals with temporal ripple propagation relationships, verify the matching relationship between the actual response time interval and the corresponding response time interval fluctuation range, and verify the matching relationship between the actual difference and the corresponding difference fluctuation range. For IoT terminals with synchronous ripple relationships, verify the matching relationship between actual values ​​and the fluctuation range of communication characteristics; IoT terminals with invalid matching verification are identified as potentially abnormal IoT terminals.

8. The communication quality monitoring method based on the Internet of Things according to claim 1, characterized in that, The extracted communication features include one of the following: information entropy of message payload distribution, message transmission frequency within a time window, and uplink / downlink byte ratio.

9. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it can be used to perform the communication quality monitoring method based on the Internet of Things as described in any one of claims 1-8.

10. An apparatus, characterized in that, include: One or more processors; Memory; and one or more programs, The one or more programs are configured to be executed by one or more processors, and the memory includes the storage medium as described in claim 9.

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