A Dongle Connection Status Monitoring Method and System Based on 5G RedCap

Abstract

This invention relates to the field of firmware status parameter monitoring technology, specifically a method and system for monitoring Dongle connection status based on 5G RedCap. The method includes: collecting connection status parameters of the Dongle firmware based on status monitoring commands and a 5G RedCap module to obtain a current connection status vector; predicting a normal connection status from the current connection status vector to obtain a predicted connection status vector; obtaining a predicted status deviation vector from the predicted and current connection status vectors; obtaining a connection status discrimination result based on the controllable deviation vector and the predicted deviation vector; identifying abnormal data based on the discrimination result to obtain target connection abnormal data; compressing the target connection abnormal data to obtain target connection compressed data; and transmitting the target connection compressed data to a device control center to obtain target control data. This invention can improve the automation level of Dongle firmware monitoring and reduce manpower consumption.

Description

Technical Field

[0001] This invention relates to the field of firmware status parameter monitoring technology, and in particular to a method and system for monitoring Dongle connection status based on 5G RedCap. Background Technology

[0002] In industrial applications such as high-temperature testing, real-time monitoring of device connection status is crucial for ensuring the accuracy of test data and the reliability of the system. With the popularization of 5G technology, RedCap modules provide a new solution for low-power, high-bandwidth IoT connectivity, making intelligent status monitoring an important support for improving overall testing efficiency.

[0003] Traditional technologies typically use manual periodic inspections or fixed threshold alarms to monitor connection status. This approach has drawbacks such as long monitoring intervals, inability to capture instantaneous anomalies, and reliance on subjective experience, which can easily lead to missed or false alarms. It is difficult to adapt to dynamic network environments. Summary of the Invention

[0004] This invention provides a 5G RedCap-based Dongle connection status monitoring method and a computer-readable storage medium. Its main purpose is to improve the automation level of Dongle firmware monitoring and reduce human resource consumption.

[0005] To achieve the above objectives, the present invention provides a Dongle connection status monitoring method based on 5G RedCap, comprising:

[0006] Identify the Dongle firmware, which includes the 5G RedCap module;

[0007] Receive status monitoring instructions, and collect connection status parameters of the Dongle firmware based on the status monitoring instructions and the 5G RedCap module to obtain the current connection status vector;

[0008] Normal connection state prediction is performed on the current connection state vector to obtain the predicted connection state vector. The deviation between the predicted connection state vector and the current connection state vector is calculated to obtain the predicted state deviation vector.

[0009] The connection status is determined based on the preset controllable state deviation vector and the predicted state deviation vector, and the connection status determination result is obtained, which is: connection normal or connection abnormal.

[0010] Based on the connection status judgment result, abnormal data is identified to obtain the target connection abnormal data, which is either residual connection abnormal data or current connection abnormal data.

[0011] The target connection anomaly data is compressed to obtain target connection compressed data. The target connection compressed data is then transmitted to the pre-built device control center to obtain the target control center, thus completing the Dongle connection status monitoring based on 5G RedCap.

[0012] Optionally, the step of predicting the normal connection state from the current connection state vector to obtain the predicted connection state vector includes:

[0013] The connection status was tested on the Dongle firmware, and multiple test connection status vector sequences were obtained.

[0014] Determine the current monitoring time corresponding to the current connection state vector;

[0015] Based on the current monitoring time, query the historical connection status vector sequence, where the historical connection status vector sequence includes multiple historical connection status vectors, and each historical connection status vector corresponds to a status timestamp;

[0016] The current state is predicted by using multiple test connection state vector sequences and historical connection state vector sequences, resulting in a predicted connection state vector.

[0017] Optionally, the step of using multiple test connection state vector sequences and historical connection state vector sequences to predict the current state and obtain a predicted connection state vector includes:

[0018] For each of the multiple test connection state vector sequences, perform the following operation:

[0019] The test connection period is obtained by acquiring the test connection state vector sequence;

[0020] If the Dongle firmware does not experience any connection abnormalities during the test connection period, the test connection state vector sequence will be recorded as the normal connection state vector sequence.

[0021] By summing the normal connection state vector sequences, multiple normal connection state vector sequences are obtained;

[0022] The predicted connection state vector is obtained by predicting the historical connection state vector sequence using multiple normal connection state vector sequences.

[0023] Optionally, the step of predicting the historical connection state vector sequence using multiple normal connection state vector sequences to obtain the predicted connection state vector includes:

[0024] A matrix is ​​constructed based on the sequence of historical connection state vectors to obtain the historical connection state time series matrix, where each row of the historical connection state time series matrix represents a historical connection state vector.

[0025] Matrix column features are extracted from the historical connection state time series matrix to obtain a set of historical column feature vectors. The set of historical column feature vectors includes multiple historical column feature vectors, and each historical column feature vector corresponds to a matrix column in the historical connection state time series matrix.

[0026] Identify and match connection state vector sequences among multiple normal connection state vector sequences based on historical column feature vector sets;

[0027] By matching the sequence of connection state vectors with the sequence of historical connection state vectors, the predicted connection state vectors are obtained.

[0028] Optionally, the step of identifying matching connection state vector sequences among multiple normal connection state vector sequences based on historical column feature vector sets includes:

[0029] For each of the multiple normal connection state vector sequences, perform the following operation:

[0030] Construct a set of normal column feature vectors based on the sequence of normal connection state vectors;

[0031] The similarity of connection states is calculated based on the feature vector sets of normal columns and historical columns, where the similarity of connection states is expressed as:

[0032] ;

[0033] in, Indicates the similarity of connection states. This indicates the number of historical column feature vectors in the historical column feature vector set or the number of normal column feature vectors in the normal column feature vector set. Represents the first in the historical column feature vector set. Historical column feature vectors, Represents the first in the set of eigenvectors of normal columns A normal column feature vector, The modulus symbol for a vector. Represents the dot product of vectors;

[0034] Summarize the connection state similarity corresponding to each normal connection state vector sequence to obtain multiple connection state similarities;

[0035] Identify the maximum state similarity among multiple connection state similarities, and denote the normal connection state vector sequence corresponding to the maximum state similarity as the matching connection state vector sequence.

[0036] Optionally, the connection state discrimination based on the preset controllable state deviation vector and the predicted state deviation vector to obtain the connection state discrimination result includes:

[0037] Identify multiple controllable state deviations in the controllable state deviation vector;

[0038] Controllable state deviations are extracted sequentially from multiple controllable state deviations, and predicted state deviations are identified in the predicted state deviation vector based on the extracted controllable state deviations.

[0039] If the predicted state deviation is not less than the extracted controllable state deviation, then an anomaly label is generated.

[0040] Summarize the abnormal tags to obtain the abnormal tag set;

[0041] If the abnormal label set is not empty, then the connection abnormality is recorded as the connection status judgment result; otherwise, the connection normality is recorded as the connection status judgment result.

[0042] Optionally, the step of identifying abnormal data based on the connection status determination result to obtain target connection abnormal data includes:

[0043] If the connection status determination result is that the connection is normal, the predicted state deviation vector is added to the preset historical state deviation vector sequence to obtain the current state deviation vector sequence. The historical state deviation vector sequence includes multiple historical state deviation vectors, or the historical state deviation vector sequence is an empty set.

[0044] The cumulative state deviation vector is obtained by performing residual accumulation calculation based on the current state deviation vector sequence.

[0045] Based on the cumulative state deviation vector and the controllable state deviation vector, residual accumulation anomaly is determined, and residual determination result is obtained, where the residual determination result is: residual accumulation anomaly or residual accumulation normal.

[0046] If the residual discrimination result is that the residual accumulation is normal, then the current state deviation vector sequence is used as the historical state deviation vector sequence, and the step of receiving the state monitoring instruction is returned until the residual discrimination result is that the residual accumulation is abnormal or the connection state discrimination result is that the connection is abnormal.

[0047] If the residual discrimination result is residual accumulation anomaly, then residual connection anomaly data is generated based on the historical connection state vector sequence and the current connection state vector;

[0048] If the connection status determination result is a connection anomaly, then the current connection anomaly data is generated based on the historical connection status vector sequence and the current connection status vector;

[0049] Record residual connection anomaly data or current connection anomaly data as target connection anomaly data.

[0050] Optionally, compressing the abnormal data of the target connection to obtain compressed data of the target connection includes:

[0051] The matching sliding window is set based on the preset minimum number of matching bytes, where the length of the matching sliding window is equal to the minimum number of matching bytes;

[0052] Determine the initial byte position in the target connection error data;

[0053] The target connection anomaly data is scanned based on the initial byte position and the matching sliding window to obtain scanned byte data, where the number of bytes in the scanned byte data is equal to the minimum number of matching bytes;

[0054] Perform a hash calculation on the scanned byte data to obtain the scan hash value;

[0055] The scanned byte data is compressed based on the scan hash value, the initial byte position, and the preset initial dictionary to obtain compressed byte data and an updated dictionary;

[0056] The initial byte position is shifted to obtain the updated byte position;

[0057] The updated dictionary and the updated byte position are used as the initial dictionary and the initial byte position, respectively, and the step of scanning the target connection anomaly data based on the initial byte position and the matching sliding window is returned until the target connection anomaly data is completely scanned.

[0058] Summarize the compressed byte data to obtain the target connection compressed data.

[0059] Optionally, the step of compressing the scanned byte data according to the scan hash value, the initial byte position, and a preset initial dictionary to obtain compressed byte data and an updated dictionary includes:

[0060] The scan hash values ​​are matched based on the initial dictionary to obtain the matching results, where the matching result is either a match exists or no match exists;

[0061] If the matching result is no match, the scan hash value, scan byte data and initial byte position are stored in the initial dictionary to obtain the updated dictionary, and compressed byte data is generated based on the scan byte data;

[0062] If the matching result indicates that a match exists, then obtain the data matching length and data offset of the scanned byte data;

[0063] Token bytes are generated based on scanned byte data. Data compression is performed based on the token bytes, data offset, and data matching length to obtain compressed byte data. The initial dictionary is then recorded as the updated dictionary.

[0064] To achieve the above objectives, the present invention also provides a 5G RedCap-based Dongle connectivity status monitoring system, comprising:

[0065] The connection parameter acquisition module is used to determine the Dongle firmware, which includes a 5G RedCap module. It receives status monitoring instructions and acquires connection status parameters of the Dongle firmware based on the status monitoring instructions and the 5G RedCap module to obtain the current connection status vector.

[0066] The state vector prediction module is used to predict the normal connection state of the current connection state vector to obtain the predicted connection state vector, and to calculate the deviation between the predicted connection state vector and the current connection state vector to obtain the predicted state deviation vector.

[0067] The connection status discrimination module is used to discriminate the connection status based on the preset controllable state deviation vector and the predicted state deviation vector, and obtain the connection status discrimination result, which is: connection normal or connection abnormal.

[0068] The abnormal data transmission module is used to identify abnormal data based on the connection status judgment result, and obtain the target connection abnormal data. The target connection abnormal data is either residual connection abnormal data or current connection abnormal data. The target connection abnormal data is compressed to obtain target connection compressed data, and the target connection compressed data is transmitted to the pre-built device control center to obtain the target control center.

[0069] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0070] Memory, storing at least one instruction;

[0071] The processor executes the instructions stored in the memory to implement the Dongle connection status monitoring method based on 5G RedCap described above.

[0072] To address the aforementioned issues, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the Dongle connection status monitoring method based on 5G RedCap described above.

[0073] To address the problems described in the background, this invention first collects connection status parameters of the Dongle firmware based on status monitoring commands and a 5G RedCap module, obtaining the current connection status vector. This step achieves automated real-time data collection. Compared to traditional methods relying on periodic manual checks or simple threshold alarms, this method efficiently acquires multi-dimensional parameters through the built-in interface of the 5G RedCap module, improving the continuity and accuracy of monitoring. Next, a normal connection status prediction is performed on the current connection status vector to obtain a predicted connection status vector. The deviation between the predicted and current connection status vectors is calculated to obtain a predicted status deviation vector. This step utilizes historical data and test sequences for status prediction, dynamically reflecting connection trends and reducing misjudgments caused by network fluctuations. Compared to existing static monitoring methods, this improves the adaptability of monitoring. Furthermore, connection status is determined based on the controllable status deviation vector and the predicted status deviation vector, yielding a connection status determination result. This step intelligently compares the deviation with a controllable threshold to automatically determine whether the connection is normal or abnormal, avoiding the subjectivity and delays of traditional methods that rely on human experience. This invention achieves rapid and accurate anomaly detection, improving the automation level of monitoring. Then, based on the connection status judgment results, it identifies anomaly data to obtain target connection anomaly data. This step can distinguish between residual connection anomaly data and current connection anomaly data, capturing cumulative and transient anomalies. Compared to the single anomaly handling method in existing technologies, it enhances the comprehensiveness and precision of monitoring, helping to discover potential problems early. Finally, the target connection anomaly data is compressed to obtain target connection compressed data, which is then transmitted to the device control center. This step uses sliding window and dictionary compression techniques, effectively reducing data transmission volume and network bandwidth consumption. Compared to uncompressed transmission, it improves resource utilization efficiency and is suitable for bandwidth-constrained IoT environments, completing the monitoring closed loop. Therefore, this invention can improve the automation level of Dongle firmware monitoring and reduce human resource consumption. Attached Figure Description

[0074] Figure 1 This is a flowchart illustrating a 5G RedCap-based Dongle connection status monitoring method according to an embodiment of the present invention.

[0075] Figure 2 This is a functional block diagram of a 5G RedCap-based Dongle connection status monitoring system provided in an embodiment of the present invention;

[0076] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the Dongle connection status monitoring method based on 5G RedCap, according to an embodiment of the present invention.

[0077] Explanation of reference numerals in the attached figures:

[0078] 1. Electronic device; 10. Processor; 11. Memory; 12. Bus; 100. Dongle connection status monitoring system based on 5G RedCap; 101. Connection parameter acquisition module; 102. State vector prediction module; 103. Connection status discrimination module; 104. Abnormal data transmission module.

[0079] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0080] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0081] This application provides a 5G RedCap-based Dongle connection status monitoring method. The executing entity of the 5G RedCap-based Dongle connection status monitoring method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the 5G RedCap-based Dongle connection status monitoring method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0082] Reference Figure 1 The diagram shown is a flowchart illustrating a 5G RedCap-based Dongle connection status monitoring method according to an embodiment of the present invention. In this embodiment, the 5G RedCap-based Dongle connection status monitoring method includes:

[0083] S1. Determine the Dongle firmware, which includes the 5G RedCap module.

[0084] Understandably, the Dongle firmware refers to the firmware embedded in the Dongle device, used to control hardware and implement connectivity functions, such as the firmware of a 5G USB Dongle. The 5G RedCap module refers to a communication module that supports 5G ReducedCapability technology. This 5G RedCap module is used to collect connection status parameters, such as signal strength, bandwidth, and connection status.

[0085] S2. Receive status monitoring instructions, and collect connection status parameters of the Dongle firmware based on the status monitoring instructions and the 5G RedCap module to obtain the current connection status vector.

[0086] It is clear that the aforementioned status monitoring command refers to a manually initiated command to collect connection status parameters of the Dongle firmware or a collection command automatically polled by the device control center. The current connection status vector refers to a set of parameters representing the real-time network connection quality of the Dongle firmware, including but not limited to: signal strength, bit error rate, latency, RSRP, RSRQ, and other connection status parameters. The aforementioned collection of connection status parameters of the Dongle firmware based on status monitoring commands and the 5G RedCap module means that when the status monitoring command is triggered, the various connection status parameters of the Dongle firmware at the current moment are read through the API interface built into the 5G RedCap module, and these connection status parameters are combined into a multi-dimensional vector, which is the current connection status vector.

[0087] S3. Perform normal connection state prediction on the current connection state vector to obtain the predicted connection state vector. Calculate the deviation between the predicted connection state vector and the current connection state vector to obtain the predicted state deviation vector.

[0088] It should be explained that the predicted connection state vector refers to the connection state vector that the Dongle firmware should possess when it is in a normal connection state at the current moment, obtained after prediction. This predicted connection state vector can be compared with the current connection state vector to determine whether the Dongle firmware is in a normal connection state at the current moment. A normal connection state refers to the network connection status when the Dongle firmware does not experience connection interruptions, data packet loss, or other similar issues. The predicted state deviation vector refers to a vector obtained after deviation calculation, representing the degree of deviation between the predicted connection state vector and the current connection state vector. The predicted state deviation vector is calculated as follows: if the predicted connection state vector and the current connection state vector are respectively... and The predicted state deviation vector is calculated as follows: ,in, This represents the predicted state deviation vector.

[0089] Specifically, the step of predicting the normal connection state from the current connection state vector to obtain the predicted connection state vector includes:

[0090] The connection status was tested on the Dongle firmware, and multiple test connection status vector sequences were obtained.

[0091] Determine the current monitoring time corresponding to the current connection state vector;

[0092] Based on the current monitoring time, query the historical connection status vector sequence, where the historical connection status vector sequence includes multiple historical connection status vectors, and each historical connection status vector corresponds to a status timestamp;

[0093] The current state is predicted by using multiple test connection state vector sequences and historical connection state vector sequences, resulting in a predicted connection state vector.

[0094] It should be explained that the test connection state vector sequence refers to the time sequence of multiple test connection state vectors of the Dongle firmware recorded during the connection state test of the Dongle firmware within a certain period of time. The test connection state vector refers to the connection state vector of the Dongle firmware collected by the 5G RedCap module at a certain moment during the connection state test. The composition of the test connection state vector is the same as that of the current connection state vector mentioned above. The aforementioned connection status test of the Dongle firmware refers to: a specific network data transmission task being loaded onto the Dongle firmware by a human, such as continuous large file FTP download or high-bitrate video streaming. During this task loading process, the connection status of the Dongle firmware is collected using a 5G RedCap module, resulting in a test connection status vector sequence. Simultaneously, the status of the Dongle firmware during this task loading process is observed by a human. This observation includes monitoring whether the network connection of the Dongle firmware is stable, whether the data transmission rate is normal, and whether there are obvious connection interruptions. When abnormal connection states are observed, such as connection drops, a sharp increase in data packet loss rate, or abnormally increased network latency, these abnormal connection states are recorded for subsequent evaluation of the test connection status vector sequence. This process is repeated to obtain multiple test connection status vector sequences. The current monitoring time refers to the moment when the status monitoring command is received. The historical connection status vector sequence refers to the time sequence set of multiple historical connection status vectors obtained before the current monitoring time, where the historical connection status vector refers to the current connection status vector obtained before the current monitoring time.

[0095] In detail, the step of using multiple test connection state vector sequences and historical connection state vector sequences to predict the current state and obtain a predicted connection state vector includes:

[0096] For each of the multiple test connection state vector sequences, perform the following operation:

[0097] The test connection period is obtained by acquiring the test connection state vector sequence;

[0098] If the Dongle firmware does not experience any connection abnormalities during the test connection period, the test connection state vector sequence will be recorded as the normal connection state vector sequence.

[0099] By summing the normal connection state vector sequences, multiple normal connection state vector sequences are obtained;

[0100] The predicted connection state vector is obtained by predicting the historical connection state vector sequence using multiple normal connection state vector sequences.

[0101] It is clear that the test connection period refers to the time period during which the task loading is executed when the test connection state vector sequence is obtained. The statement that the Dongle firmware did not experience connection anomalies during the aforementioned test connection period means that the Dongle firmware experienced abnormal connection states such as connection loss, a sharp increase in data packet loss rate, or abnormally increased network latency during the test connection period.

[0102] Specifically, the step of predicting a predicted connection state vector by using multiple normal connection state vector sequences to predict a historical connection state vector sequence includes:

[0103] A matrix is ​​constructed based on the sequence of historical connection state vectors to obtain the historical connection state time series matrix, where each row of the historical connection state time series matrix represents a historical connection state vector.

[0104] Matrix column features are extracted from the historical connection state time series matrix to obtain a set of historical column feature vectors. The set of historical column feature vectors includes multiple historical column feature vectors, and each historical column feature vector corresponds to a matrix column in the historical connection state time series matrix.

[0105] Identify and match connection state vector sequences among multiple normal connection state vector sequences based on historical column feature vector sets;

[0106] By matching the sequence of connection state vectors with the sequence of historical connection state vectors, the predicted connection state vectors are obtained.

[0107] It should be explained that the historical connection state time series matrix refers to a matrix composed of a sequence of historical connection state vectors. Constructing a matrix based on the sequence of historical connection state vectors means treating each historical connection state vector in the sequence as a matrix row. A matrix row refers to a row vector in the historical connection state time series matrix, and multiple such matrix rows constitute the historical connection state time series matrix. The historical column feature vector set refers to the set of statistical features corresponding to each matrix column in the historical connection state time series matrix. The specific extraction method for this historical column feature vector set is as follows: Multiple matrix columns in the historical connection state time series matrix are identified, where each matrix column represents a time series set of a certain connection state parameter. A matrix column refers to a column vector in the historical connection state time series matrix. For example, matrix column A1 represents a set of signal strengths, matrix column A2 represents a set of bit error rates, matrix column A3 represents a set of delays, etc. Matrix columns are extracted sequentially from these multiple matrix columns, and statistical features such as mean, standard deviation, variance, and median are calculated for the extracted matrix columns. The numerical vectors composed of these statistical features are recorded as historical column feature vectors. The historical column feature vector set is obtained by summing the historical column feature vectors corresponding to each matrix column. The matching connection state vector sequence refers to the normal connection state vector sequence that has the most similar features to the historical column feature vector set.

[0108] Furthermore, the above-mentioned parameter fitting of the historical connection state vector sequence using the matched connection state vector sequence refers to: averaging the values ​​at the same position in each matched connection state vector in the matched connection state vector sequence to obtain the average connection state vector. This average averaging involves calculating the average of the values ​​at the same position in each matched connection state vector, resulting in the average connection state vector. A connection state vector variable is then set up, which is used for subsequent solving to obtain the predicted connection state vector. The composition of this connection state vector variable is the same as that of the historical connection state vectors in the historical connection state vector sequence. Then, the connection state vector variable... The quantity is supplemented to the historical connection state vector sequence to obtain the supplementary connection state vector sequence. The connection state vector variable is arranged at the last position in the supplementary connection state vector sequence. The supplementary connection state vector sequence is averaged to obtain the average supplementary state vector. An equation is constructed based on the average supplementary state vector and the average connection state vector. The equation states that each vector element in the average supplementary state vector is numerically equal to the corresponding vector element in the average connection state vector. Then, the connection state vector variable in the equation is solved to make the equation true. The specific numerical vector of the connection state vector variable obtained after solving is the predicted connection state vector.

[0109] Specifically, the step of identifying and matching connection state vector sequences among multiple normal connection state vector sequences based on historical column feature vector sets includes:

[0110] For each of the multiple normal connection state vector sequences, perform the following operation:

[0111] Construct a set of normal column feature vectors based on the sequence of normal connection state vectors;

[0112] The similarity of connection states is calculated based on the feature vector sets of normal columns and historical columns, where the similarity of connection states is expressed as:

[0113] ;

[0114] in, Indicates the similarity of connection states. This indicates the number of historical column feature vectors in the historical column feature vector set or the number of normal column feature vectors in the normal column feature vector set. Represents the first in the historical column feature vector set. Historical column feature vectors, Represents the first in the set of eigenvectors of normal columns A normal column feature vector, The modulus symbol for a vector. Represents the dot product of vectors;

[0115] Summarize the connection state similarity corresponding to each normal connection state vector sequence to obtain multiple connection state similarities;

[0116] Identify the maximum state similarity among multiple connection state similarities, and denote the normal connection state vector sequence corresponding to the maximum state similarity as the matching connection state vector sequence.

[0117] It should be explained that the normal column feature vector set refers to a collection of multiple normal column feature vectors extracted from the normal connection state vector sequence. The method for constructing the normal column feature vector set based on the normal connection state vector sequence is the same as the method for constructing the historical column feature vector set based on the historical connection state vector sequence, and will not be repeated here. The connection state similarity refers to a numerical value that quantifies the degree of similarity between the normal column feature vector set and the historical column feature vector set. The formula for calculating the connection state similarity includes... The term represents the first The eigenvector of the th normal column and the th The vector cosine value between the feature vectors of the nth historical column is used to quantize the nth historical column feature vector. The eigenvector of the th normal column and the th The similarity of the feature vectors of each historical column. The maximum state similarity refers to the connection state similarity with the largest numerical value among multiple connection state similarities.

[0118] S4. Based on the preset controllable state deviation vector and the predicted state deviation vector, the connection state is determined to obtain the connection state determination result, which is either a normal connection or an abnormal connection.

[0119] Understandably, the controllable state deviation vector refers to a numerical vector composed of multiple controllable state deviations that are artificially set to determine whether the connection status of the Dongle firmware is abnormal. The controllable state deviation vector is composed of multiple controllable state deviations, and each controllable state deviation corresponds to the predicted state deviation at the same position in the predicted state deviation vector (that is, the two represent the deviation of the same connection status parameter).

[0120] Optionally, the controllable state deviation vector is constructed as follows: Obtain the connection task currently being performed by the Dongle firmware, such as high-definition video streaming or low-latency online gaming. Based on this connection task, query multiple connection state vector sequences with the same task from multiple normal connection state vector sequences. Here, a connection state vector sequence with the same task refers to a normal connection state vector sequence corresponding to the connection task, and "corresponding to the connection task" means that the task executed during task loading is the same as the aforementioned connection task. Then, according to the type of connection state parameter, group the values ​​of the same connection state parameters in multiple connection state vector sequences with the same task into the same time series set, thereby obtaining multiple state parameter time series sets corresponding to multiple connection state parameters. Extract the state parameter time series sets sequentially from the multiple state parameter time series sets, and calculate the maximum and minimum values ​​of the extracted state parameter time series sets. Construct a controllable state deviation based on these maximum and minimum values, where the controllable state deviation is represented as: ,in, This indicates a deviation from a controllable state. and The maximum and minimum values ​​of the extracted state parameter time series set are represented respectively. The controllable state deviations corresponding to each state parameter time series set are summarized to obtain multiple controllable state deviations. These multiple controllable state deviations are then converted into a numerical vector, which is the controllable state deviation vector. The connection state discrimination result refers to the connection state of the Dongle firmware obtained after connection state discrimination. "Normal connection" means the Dongle firmware is currently in a normal connection state, and "abnormal connection" means the Dongle firmware is currently in an abnormal connection state.

[0121] In detail, the connection state discrimination based on the preset controllable state deviation vector and the predicted state deviation vector, to obtain the connection state discrimination result, includes:

[0122] Identify multiple controllable state deviations in the controllable state deviation vector;

[0123] Controllable state deviations are extracted sequentially from multiple controllable state deviations, and predicted state deviations are identified in the predicted state deviation vector based on the extracted controllable state deviations.

[0124] If the predicted state deviation is not less than the extracted controllable state deviation, then an anomaly label is generated.

[0125] Summarize the abnormal tags to obtain the abnormal tag set;

[0126] If the abnormal label set is not empty, then the connection abnormality is recorded as the connection status judgment result; otherwise, the connection normality is recorded as the connection status judgment result.

[0127] It should be explained that the predicted state deviation refers to the state deviation in the predicted state deviation vector corresponding to the same connection state parameter as the controllable state deviation. If the predicted state deviation is not less than the extracted controllable state deviation, it indicates that the predicted state deviation has exceeded the maximum acceptable deviation threshold for the connection state parameter within its normal fluctuation range, meaning that the Dongle firmware exhibits an abnormal condition in the corresponding connection state parameter. The anomaly label refers to an identifier used to mark an anomaly in the connection state parameter. If the anomaly label set is not empty, it indicates that an abnormal connection state parameter exists among the multiple connection state parameters of the Dongle firmware, meaning that the Dongle firmware can be considered to have experienced a connection anomaly, and therefore the connection anomaly is recorded as the connection state discrimination result.

[0128] S5. Based on the connection status judgment result, identify abnormal data to obtain target connection abnormal data, where the target connection abnormal data is residual connection abnormal data or current connection abnormal data.

[0129] It is clear that the target connection anomaly data refers to the data recorded when the Dongle firmware experiences a connection anomaly. The specific methods for obtaining the residual connection anomaly data and the current connection anomaly data will be given in subsequent embodiments.

[0130] Specifically, the step of identifying abnormal data based on the connection status determination result to obtain target connection abnormal data includes:

[0131] If the connection status determination result is that the connection is normal, the predicted state deviation vector is added to the preset historical state deviation vector sequence to obtain the current state deviation vector sequence. The historical state deviation vector sequence includes multiple historical state deviation vectors, or the historical state deviation vector sequence is an empty set.

[0132] The cumulative state deviation vector is obtained by performing residual accumulation calculation based on the current state deviation vector sequence.

[0133] Based on the cumulative state deviation vector and the controllable state deviation vector, residual accumulation anomaly is determined, and residual determination result is obtained, where the residual determination result is: residual accumulation anomaly or residual accumulation normal.

[0134] If the residual discrimination result is that the residual accumulation is normal, then the current state deviation vector sequence is used as the historical state deviation vector sequence, and the step of receiving the state monitoring instruction is returned until the residual discrimination result is that the residual accumulation is abnormal or the connection state discrimination result is that the connection is abnormal.

[0135] If the residual discrimination result is residual accumulation anomaly, then residual connection anomaly data is generated based on the historical connection state vector sequence and the current connection state vector;

[0136] If the connection status determination result is a connection anomaly, then the current connection anomaly data is generated based on the historical connection status vector sequence and the current connection status vector;

[0137] Record residual connection anomaly data or current connection anomaly data as target connection anomaly data.

[0138] It should be explained that the historical state deviation vector sequence refers to the time-series set of multiple state deviation vectors stored by the Dongle firmware before the current moment. This historical state deviation vector sequence represents the current state deviation vector sequence collected at the previous moment. If the Dongle firmware has not yet undergone connection status determination, this historical state deviation vector sequence is an empty set. If the connection status determination result of the previous Dongle firmware was a connection anomaly or the residual determination result was a residual accumulation anomaly, it indicates that the most recently stored historical state deviation vector sequence has an anomaly. In this case, the historical state deviation vector sequence will be packaged into target connection anomaly data, and the historical state deviation vector sequence needs to be initialized (i.e., cleared of the vector elements in the historical state deviation vector sequence). Therefore, in this situation, the historical state deviation vector sequence is also an empty set. The current state deviation vector sequence refers to the historical state deviation vector sequence after supplementing the predicted state deviation vector. Supplementing the predicted state deviation vector to the preset historical state deviation vector sequence means adding the predicted state deviation vector to the last position in the historical state deviation vector sequence.

[0139] Furthermore, the cumulative state deviation vector refers to the vector obtained after residual accumulation calculation. This cumulative state deviation vector represents the cumulative effect of the deviations of various connection state parameters of the Dongle firmware over a continuous monitoring period. The above-mentioned residual accumulation calculation based on the current state deviation vector sequence refers to summing the vector elements with the same position in each current state deviation vector in the current state deviation vector sequence to obtain the cumulative state deviation vector. For example, a current state deviation vector sequence is: { }, then the cumulative state deviation vector obtained by performing residual accumulation calculation on the current state deviation vector sequence is: The residual discrimination result refers to the result obtained after residual accumulation anomaly discrimination. The step of residual accumulation anomaly discrimination based on the cumulative state deviation vector and the controllable state deviation vector is the same as the step of connection status discrimination based on the preset controllable state deviation vector and the predicted state deviation vector. The residual accumulation anomaly or residual accumulation normal here corresponds to the connection normal or connection anomaly mentioned above, respectively. The residual accumulation anomaly means that multiple small deviations of the Dongle firmware that were judged as connection normal have accumulated continuously over a period of time and eventually exceeded the acceptable range. The residual accumulation normal means that although the Dongle firmware has small deviations at individual moments during a connection monitoring period, the cumulative effect of these deviations is still within the controllable range.

[0140] Understandably, when the residual discrimination result is that the residual accumulation is normal, in order to ensure communication efficiency and reduce unnecessary network transmission, it is not necessary to transmit data with the device control center. Instead, the connection status of the Dongle firmware is monitored locally, i.e., the step of receiving the status monitoring command is re-executed. When the residual discrimination result is that the residual accumulation is abnormal, it means that although the Dongle firmware did not experience a sudden connection failure at a certain moment (i.e., the connection status discrimination result is normal), the connection quality of the Dongle firmware has a potential and continuous deterioration trend. Therefore, it is necessary to return the historical connection status vector sequence and the current connection status vector to the device control center and initialize the historical connection status vector sequence set at this time. The residual connection anomaly data refers to the data combination of the historical connection status vector sequence and the current connection status vector when the residual discrimination result is that the residual accumulation is abnormal. The above-mentioned generation of residual connection anomaly data based on the historical connection status vector sequence and the current connection status vector means: putting the historical connection status vector sequence and the current connection status vector into the same data combination, and the resulting data combination is the residual connection anomaly data.

[0141] It is clear that when the connection status determination result is a connection anomaly, it means that the Dongle firmware has experienced a sudden connection anomaly. Therefore, it is necessary to promptly send the historical connection status vector sequence and the current connection status vector to the device control center. The current connection anomaly data refers to the data combination of the historical connection status vector sequence and the current connection status vector when the connection status determination result is a connection anomaly.

[0142] S6. Compress the target connection anomaly data to obtain target connection compressed data, transmit the target connection compressed data to the pre-built device control center to obtain the target control center, and complete the Dongle connection status monitoring based on 5G RedCap.

[0143] It is clear that the target connection compressed data refers to the compressed target connection anomaly data. This compression step further reduces the amount of data that needs to be transmitted, thereby saving 5G network bandwidth and reducing transmission latency and power consumption. The device control center refers to a remote server or cloud platform used to receive, store, analyze, and display Dongle connection status data, such as an IoT device management platform deployed in the cloud. The target control center refers to the device control center that receives the target connection compressed data, allowing relevant operators to manually inspect, repair, or replace the Dongle firmware based on the received target connection compressed data.

[0144] Specifically, the compression of the abnormal data of the target connection to obtain compressed data of the target connection includes:

[0145] The matching sliding window is set based on the preset minimum number of matching bytes, where the length of the matching sliding window is equal to the minimum number of matching bytes;

[0146] Determine the initial byte position in the target connection error data;

[0147] The target connection anomaly data is scanned based on the initial byte position and the matching sliding window to obtain scanned byte data, where the number of bytes in the scanned byte data is equal to the minimum number of matching bytes;

[0148] Perform a hash calculation on the scanned byte data to obtain the scan hash value;

[0149] The scanned byte data is compressed based on the scan hash value, the initial byte position, and the preset initial dictionary to obtain compressed byte data and an updated dictionary;

[0150] The initial byte position is shifted to obtain the updated byte position;

[0151] The updated dictionary and the updated byte position are used as the initial dictionary and the initial byte position, respectively, and the step of scanning the target connection anomaly data based on the initial byte position and the matching sliding window is returned until the target connection anomaly data is completely scanned.

[0152] Summarize the compressed byte data to obtain the target connection compressed data.

[0153] It should be explained that the minimum number of matching bytes refers to the minimum number of consecutive bytes required for matching in the compression algorithm. Optionally, this minimum number of matching bytes can be set according to the desired compression efficiency, for example, set to 4 sub-bytes. The matching sliding window refers to a fixed-length byte window that slides across the target connection anomaly data, used to find duplicate data patterns during compression. The initial byte position refers to the initial position of the matching sliding window when sliding over the target connection anomaly data. If this is the first time the matching sliding window has slid over the target connection anomaly data, then the initial byte position is the 0th byte in the target connection anomaly data. The scan hash value refers to the hash value of the scanned byte data. Hash calculation of the scanned byte data means using a hash function (such as CRC32) to map the scanned byte data to a fixed-length integer value. The initial dictionary refers to a data structure used to store the processed scanned byte data and the corresponding hash value and position. This initial dictionary is used to quickly query and match whether the current scanned byte data has appeared before. This initial dictionary is an empty set at the beginning of compression. The updated dictionary position refers to the initial byte position after shifting. Shifting the initial byte position means moving it one position to the right. For example, if the initial byte position is 0 (the starting position of the first byte), then the updated byte position is 1 (the starting position of the second byte). The completed scan means that the matching sliding window has reached the end of the target connection exception data, and all bytes of data in the target connection exception data have been processed.

[0154] In detail, the step of compressing the scanned byte data based on the scan hash value, the initial byte position, and a preset initial dictionary to obtain compressed byte data and an updated dictionary includes:

[0155] The scan hash values ​​are matched based on the initial dictionary to obtain the matching results, where the matching result is either a match exists or no match exists;

[0156] If the matching result is no match, the scan hash value, scan byte data and initial byte position are stored in the initial dictionary to obtain the updated dictionary, and compressed byte data is generated based on the scan byte data;

[0157] If the matching result indicates that a match exists, then obtain the data matching length and data offset of the scanned byte data;

[0158] Token bytes are generated based on scanned byte data. Data compression is performed based on the token bytes, data offset, and data matching length to obtain compressed byte data. The initial dictionary is then recorded as the updated dictionary.

[0159] It is clear that the matching result refers to the result obtained after matching. A match means that an entry with the same scan hash value was found in the initial dictionary, indicating that the content represented by the scanned byte data has appeared before. No match means that no entry with the same scan hash value was found in the initial dictionary. The above matching of scan hash values ​​based on the initial dictionary means: using the scan hash value as the key, searching the initial dictionary for a matching key; if a matching key exists, verifying whether the scanned byte data corresponding to that matching key is completely consistent with the current scanned byte data (to prevent hash collisions).

[0160] It should be explained that if the matching result is no match, it means that the currently scanned byte data is new and non-repeating. This scanned byte data, its corresponding scan hash value, and its initial byte position need to be stored in the initial dictionary. The storage method is as follows: create a new dictionary entry, using the scan hash value as the index, and store the corresponding scanned byte data and its initial byte position. For example, the updated dictionary structure is: {scan hash value 1: (scanned byte data 1, initial byte position 1), scan hash value 2: (scanned byte data 2, initial byte position 2), ...}. Simultaneously, the complete scanned byte data needs to be used as compressed byte data. Generating compressed byte data based on scanned byte data means using the token byte and the complete scanned byte data as compressed byte data.

[0161] Furthermore, if the matching result indicates a match exists, it means that the current scanned byte data is a repetition of previously encountered scanned byte data, and compression encoding can be performed. The data matching length refers to the maximum number of consecutive bytes that match the scanned byte data in the initial dictionary, starting from the current scan position. The data offset refers to the byte distance between the current scan position and the starting position of the matched scanned byte data in the initial dictionary. For example, if the index of the current scan position is 15 and the index of the starting position of the matched scanned byte data is 10, then the data offset is... The token byte refers to an 8-bit byte used to identify whether the subsequently compressed data (i.e., compressed byte data) is the source data (i.e., the complete data of the scanned byte data) or the data offset and data match length. The high 4 bits of this 8-bit byte represent the data length of the uncompressed scanned byte data, and the low 4 bits represent the length of the data matched in the initial dictionary plus the length of the matching sliding window. This token byte is used to distinguish data types and control flow during decompression. The data compression based on the token byte, data offset, and data match length refers to encoding the token byte, data offset, and data match length into a sequence. When the matching result is no match, the format of this sequence is: {token byte, complete data of the scanned byte data}; when the matching result is a match, the format of this sequence is: {token byte, data offset, data match length}.

[0162] To address the problems described in the background, this invention first collects connection status parameters of the Dongle firmware based on status monitoring commands and a 5G RedCap module, obtaining the current connection status vector. This step achieves automated real-time data collection. Compared to traditional methods relying on periodic manual checks or simple threshold alarms, this method efficiently acquires multi-dimensional parameters through the built-in interface of the 5G RedCap module, improving the continuity and accuracy of monitoring. Next, a normal connection status prediction is performed on the current connection status vector to obtain a predicted connection status vector. The deviation between the predicted and current connection status vectors is calculated to obtain a predicted status deviation vector. This step utilizes historical data and test sequences for status prediction, dynamically reflecting connection trends and reducing misjudgments caused by network fluctuations. Compared to existing static monitoring methods, this improves the adaptability of monitoring. Furthermore, connection status is determined based on the controllable status deviation vector and the predicted status deviation vector, yielding a connection status determination result. This step intelligently compares the deviation with a controllable threshold to automatically determine whether the connection is normal or abnormal, avoiding the subjectivity and delays of traditional methods that rely on human experience. This invention achieves rapid and accurate anomaly detection, improving the automation level of monitoring. Then, based on the connection status judgment results, it identifies anomaly data to obtain target connection anomaly data. This step can distinguish between residual connection anomaly data and current connection anomaly data, capturing cumulative and transient anomalies. Compared to the single anomaly handling method in existing technologies, it enhances the comprehensiveness and precision of monitoring, helping to discover potential problems early. Finally, the target connection anomaly data is compressed to obtain target connection compressed data, which is then transmitted to the device control center. This step uses sliding window and dictionary compression techniques, effectively reducing data transmission volume and network bandwidth consumption. Compared to uncompressed transmission, it improves resource utilization efficiency and is suitable for bandwidth-constrained IoT environments, completing the monitoring closed loop. Therefore, this invention can improve the automation level of Dongle firmware monitoring and reduce human resource consumption.

[0163] like Figure 2 The diagram shown is a functional block diagram of a 5G RedCap-based Dongle connection status monitoring system provided in an embodiment of the present invention.

[0164] The 5G RedCap-based Dongle connection status monitoring system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the 5G RedCap-based Dongle connection status monitoring system 100 may include a connection parameter acquisition module 101, a state vector prediction module 102, a connection status discrimination module 103, and an abnormal data transmission module 104. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0165] The connection parameter acquisition module 101 is used to determine the Dongle firmware, wherein the Dongle firmware includes a 5G RedCap module, receives a status monitoring command, and acquires connection status parameters of the Dongle firmware based on the status monitoring command and the 5G RedCap module to obtain the current connection status vector.

[0166] The state vector prediction module 102 is used to predict the normal connection state of the current connection state vector to obtain the predicted connection state vector, and to calculate the deviation between the predicted connection state vector and the current connection state vector to obtain the predicted state deviation vector.

[0167] The connection status discrimination module 103 is used to perform connection status discrimination based on a preset controllable state deviation vector and a predicted state deviation vector to obtain a connection status discrimination result, wherein the connection status discrimination result is: connection normal or connection abnormal.

[0168] The abnormal data transmission module 104 is used to identify abnormal data based on the connection status judgment result, obtain target connection abnormal data, wherein the target connection abnormal data is residual connection abnormal data or current connection abnormal data, compress the target connection abnormal data to obtain target connection compressed data, and transmit the target connection compressed data to the pre-built device control center to obtain the target control center.

[0169] In detail, the modules in the 5G RedCap-based Dongle connectivity status monitoring system 100 described in this embodiment of the invention employ the same methods as described above. Figure 1 The method used is the same as the Dongle connection status monitoring method based on 5G RedCap described above, and can produce the same technical effect, so it will not be repeated here.

[0170] like Figure 3 The diagram shown is a structural schematic of an electronic device that implements a 5G RedCap-based Dongle connection status monitoring method according to an embodiment of the present invention.

[0171] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a Dongle connection status monitoring method program based on 5G RedCap.

[0172] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of the Dongle connection status monitoring method program based on 5G RedCap, but also to temporarily store data that has been output or will be output.

[0173] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a Dongle connection status monitoring method program based on 5G RedCap) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0174] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0175] Figure 3 Only electronic devices with components are shown; those skilled in the art will understand that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0176] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0177] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0178] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0179] The 5G RedCap-based Dongle connectivity status monitoring method program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0180] Identify the Dongle firmware, which includes the 5G RedCap module;

[0181] Receive status monitoring instructions, and collect connection status parameters of the Dongle firmware based on the status monitoring instructions and the 5G RedCap module to obtain the current connection status vector;

[0182] Normal connection state prediction is performed on the current connection state vector to obtain the predicted connection state vector. The deviation between the predicted connection state vector and the current connection state vector is calculated to obtain the predicted state deviation vector.

[0183] The connection status is determined based on the preset controllable state deviation vector and the predicted state deviation vector, and the connection status determination result is obtained, which is: connection normal or connection abnormal.

[0184] Based on the connection status judgment result, abnormal data is identified to obtain the target connection abnormal data, which is either residual connection abnormal data or current connection abnormal data.

[0185] The target connection anomaly data is compressed to obtain target connection compressed data. The target connection compressed data is then transmitted to the pre-built device control center to obtain the target control center, thus completing the Dongle connection status monitoring based on 5G RedCap.

[0186] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0187] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0188] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0189] Identify the Dongle firmware, which includes the 5G RedCap module;

[0190] Receive status monitoring instructions, and collect connection status parameters of the Dongle firmware based on the status monitoring instructions and the 5G RedCap module to obtain the current connection status vector;

[0191] Normal connection state prediction is performed on the current connection state vector to obtain the predicted connection state vector. The deviation between the predicted connection state vector and the current connection state vector is calculated to obtain the predicted state deviation vector.

[0192] The connection status is determined based on the preset controllable state deviation vector and the predicted state deviation vector, and the connection status determination result is obtained, which is: connection normal or connection abnormal.

[0193] Based on the connection status judgment result, abnormal data is identified to obtain the target connection abnormal data, which is either residual connection abnormal data or current connection abnormal data.

[0194] The target connection anomaly data is compressed to obtain target connection compressed data. The target connection compressed data is then transmitted to the pre-built device control center to obtain the target control center, thus completing the Dongle connection status monitoring based on 5G RedCap.

[0195] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0196] The modules described as separate components may or may not be physically separate. The components shown as modules 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 modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0197] Furthermore, the functional modules 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. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0198] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0199] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for monitoring Dongle connectivity status based on 5G RedCap, characterized in that, The method includes: Identify the Dongle firmware, which includes the 5G RedCap module; Receive status monitoring instructions, and collect connection status parameters of the Dongle firmware based on the status monitoring instructions and the 5G RedCap module to obtain the current connection status vector; Normal connection state prediction is performed on the current connection state vector to obtain the predicted connection state vector. The deviation between the predicted connection state vector and the current connection state vector is calculated to obtain the predicted state deviation vector. The connection status is determined based on the preset controllable state deviation vector and the predicted state deviation vector, and the connection status determination result is obtained, which is: connection normal or connection abnormal. The connection state discrimination based on the preset controllable state deviation vector and the predicted state deviation vector, to obtain the connection state discrimination result, includes: Identify multiple controllable state deviations in the controllable state deviation vector; Controllable state deviations are extracted sequentially from multiple controllable state deviations, and predicted state deviations are identified in the predicted state deviation vector based on the extracted controllable state deviations. If the predicted state deviation is not less than the extracted controllable state deviation, then an anomaly label is generated. Summarize the abnormal tags to obtain the abnormal tag set; If the abnormal tag set is not empty, then the connection abnormality is recorded as the connection status judgment result; otherwise, the connection normality is recorded as the connection status judgment result. Based on the connection status judgment result, abnormal data is identified to obtain the target connection abnormal data, which is either residual connection abnormal data or current connection abnormal data. The target connection anomaly data is compressed to obtain target connection compressed data. The target connection compressed data is then transmitted to the pre-built device control center to obtain the target control center, thus completing the Dongle connection status monitoring based on 5G RedCap.

2. The Dongle connection status monitoring method based on 5G RedCap as described in claim 1, characterized in that, The step of predicting the normal connection state from the current connection state vector to obtain the predicted connection state vector includes: The connection status was tested on the Dongle firmware, and multiple test connection status vector sequences were obtained. Determine the current monitoring time corresponding to the current connection state vector; Based on the current monitoring time, query the historical connection status vector sequence, where the historical connection status vector sequence includes multiple historical connection status vectors, and each historical connection status vector corresponds to a status timestamp; The current state is predicted by using multiple test connection state vector sequences and historical connection state vector sequences, resulting in a predicted connection state vector.

3. The Dongle connection status monitoring method based on 5G RedCap as described in claim 2, characterized in that, The step of using multiple test connection state vector sequences and historical connection state vector sequences to predict the current state and obtain the predicted connection state vector includes: For each of the multiple test connection state vector sequences, perform the following operation: The test connection period is obtained by acquiring the test connection state vector sequence; If the Dongle firmware does not experience any connection abnormalities during the test connection period, the test connection state vector sequence will be recorded as the normal connection state vector sequence. By summing the normal connection state vector sequences, multiple normal connection state vector sequences are obtained; The predicted connection state vector is obtained by predicting the historical connection state vector sequence using multiple normal connection state vector sequences.

4. The Dongle connection status monitoring method based on 5G RedCap as described in claim 3, characterized in that, The step of predicting the historical connection state vector sequence using multiple normal connection state vector sequences to obtain the predicted connection state vector includes: A matrix is ​​constructed based on the sequence of historical connection state vectors to obtain the historical connection state time series matrix, where each row of the historical connection state time series matrix represents a historical connection state vector. Matrix column features are extracted from the historical connection state time series matrix to obtain a set of historical column feature vectors. The set of historical column feature vectors includes multiple historical column feature vectors, and each historical column feature vector corresponds to a matrix column in the historical connection state time series matrix. Identify and match connection state vector sequences among multiple normal connection state vector sequences based on historical column feature vector sets; By matching the sequence of connection state vectors with the sequence of historical connection state vectors, the predicted connection state vectors are obtained.

5. The Dongle connection status monitoring method based on 5G RedCap as described in claim 4, characterized in that, The method of identifying and matching connection state vector sequences among multiple normal connection state vector sequences based on historical column feature vector sets includes: For each of the multiple normal connection state vector sequences, perform the following operation: Construct a set of normal column feature vectors based on the sequence of normal connection state vectors; The similarity of connection states is calculated based on the feature vector sets of normal columns and historical columns, where the similarity of connection states is expressed as: ； in, Indicates the similarity of connection states. This indicates the number of historical column feature vectors in the historical column feature vector set or the number of normal column feature vectors in the normal column feature vector set. Represents the first in the historical column feature vector set. Historical column feature vectors, Represents the first in the set of eigenvectors of normal columns A normal column feature vector, The modulus symbol for a vector. Represents the dot product of vectors; Summarize the connection state similarity corresponding to each normal connection state vector sequence to obtain multiple connection state similarities; Identify the maximum state similarity among multiple connection state similarities, and denote the normal connection state vector sequence corresponding to the maximum state similarity as the matching connection state vector sequence.

6. The Dongle connection status monitoring method based on 5G RedCap as described in claim 5, characterized in that, The step of identifying abnormal data based on the connection status judgment result to obtain target connection abnormal data includes: If the connection status determination result is that the connection is normal, the predicted state deviation vector is added to the preset historical state deviation vector sequence to obtain the current state deviation vector sequence. The historical state deviation vector sequence includes multiple historical state deviation vectors, or the historical state deviation vector sequence is an empty set. The cumulative state deviation vector is obtained by performing residual accumulation calculation based on the current state deviation vector sequence. Based on the cumulative state deviation vector and the controllable state deviation vector, residual accumulation anomaly is determined, and residual determination result is obtained, where the residual determination result is: residual accumulation anomaly or residual accumulation normal. If the residual discrimination result is that the residual accumulation is normal, then the current state deviation vector sequence is used as the historical state deviation vector sequence, and the step of receiving the state monitoring instruction is returned until the residual discrimination result is that the residual accumulation is abnormal or the connection state discrimination result is that the connection is abnormal. If the residual discrimination result is residual accumulation anomaly, then residual connection anomaly data is generated based on the historical connection state vector sequence and the current connection state vector; If the connection status determination result is a connection anomaly, then the current connection anomaly data is generated based on the historical connection status vector sequence and the current connection status vector; Record residual connection anomaly data or current connection anomaly data as target connection anomaly data.

7. The Dongle connection status monitoring method based on 5G RedCap as described in claim 6, characterized in that, The process of compressing the abnormal data of the target connection to obtain compressed data of the target connection includes: The matching sliding window is set based on the preset minimum number of matching bytes, where the length of the matching sliding window is equal to the minimum number of matching bytes; Determine the initial byte position in the target connection error data; The target connection anomaly data is scanned based on the initial byte position and the matching sliding window to obtain scanned byte data, where the number of bytes in the scanned byte data is equal to the minimum number of matching bytes; Perform a hash calculation on the scanned byte data to obtain the scan hash value; The scanned byte data is compressed based on the scan hash value, the initial byte position, and the preset initial dictionary to obtain compressed byte data and an updated dictionary; The initial byte position is shifted to obtain the updated byte position; The updated dictionary and the updated byte position are used as the initial dictionary and the initial byte position, respectively, and the step of scanning the target connection anomaly data based on the initial byte position and the matching sliding window is returned until the target connection anomaly data is completely scanned. Summarize the compressed byte data to obtain the target connection compressed data.

8. The Dongle connection status monitoring method based on 5G RedCap as described in claim 7, characterized in that, The step of compressing the scanned byte data based on the scan hash value, the initial byte position, and a preset initial dictionary to obtain compressed byte data and an updated dictionary includes: The scan hash values ​​are matched based on the initial dictionary to obtain the matching results, where the matching result is either a match exists or no match exists; If the matching result is no match, the scan hash value, scan byte data and initial byte position are stored in the initial dictionary to obtain the updated dictionary, and compressed byte data is generated based on the scan byte data; If the matching result indicates that a match exists, then obtain the data matching length and data offset of the scanned byte data; Token bytes are generated based on scanned byte data. Data compression is performed based on the token bytes, data offset, and data matching length to obtain compressed byte data. The initial dictionary is then recorded as the updated dictionary.

9. A 5G RedCap-based Dongle connectivity status monitoring system, characterized in that, The system includes: The connection parameter acquisition module is used to determine the Dongle firmware, which includes a 5G RedCap module. It receives status monitoring instructions and acquires connection status parameters of the Dongle firmware based on the status monitoring instructions and the 5G RedCap module to obtain the current connection status vector. The state vector prediction module is used to predict the normal connection state of the current connection state vector to obtain the predicted connection state vector, and to calculate the deviation between the predicted connection state vector and the current connection state vector to obtain the predicted state deviation vector. The connection status discrimination module is used to discriminate the connection status based on the preset controllable state deviation vector and the predicted state deviation vector, and obtain the connection status discrimination result, which is: connection normal or connection abnormal. The connection state discrimination based on the preset controllable state deviation vector and the predicted state deviation vector, to obtain the connection state discrimination result, includes: Identify multiple controllable state deviations in the controllable state deviation vector; Controllable state deviations are extracted sequentially from multiple controllable state deviations, and predicted state deviations are identified in the predicted state deviation vector based on the extracted controllable state deviations. If the predicted state deviation is not less than the extracted controllable state deviation, then an anomaly label is generated. Summarize the abnormal tags to obtain the abnormal tag set; If the abnormal tag set is not empty, then the connection abnormality is recorded as the connection status judgment result; otherwise, the connection normality is recorded as the connection status judgment result. The abnormal data transmission module is used to identify abnormal data based on the connection status judgment result, and obtain the target connection abnormal data. The target connection abnormal data is either residual connection abnormal data or current connection abnormal data. The target connection abnormal data is compressed to obtain target connection compressed data, and the target connection compressed data is transmitted to the pre-built device control center to obtain the target control center.

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