A network connection management system and method based on internet of things devices

Abstract

The application discloses a network connection management system and method based on Internet of Things equipment, and belongs to the technical field of Internet of Things security; the system realizes intelligent classified management of files by constructing a three-level database architecture, combining a multistage composite retrieval mechanism and a dynamic matching algorithm; the method comprises the following steps: composite condition retrieval based on a multi-field joint index strategy, matching verification of sender information and historical data, a dual verification mechanism of file content relevance and similarity, and a dynamic benchmark value determination algorithm; through TF-IDF weight calculation, name-content relevance matching and similarity hierarchical storage strategies, the accuracy of malicious file identification is significantly improved, and the false judgment and missed detection rates are reduced; the application solves the problems of low accuracy of single matching rules, insufficient reuse of historical data and missing storage classification logic in the prior art, and improves the security and management efficiency of file transmission of Internet of Things equipment.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) security technology, and specifically discloses a network connection management system and method based on IoT devices. Background Technology

[0002] Existing file rejection systems typically employ a single matching rule, resulting in a simple system architecture, low deployment and maintenance costs, and a static rule base-based matching mechanism that eliminates the need for complex calculations. This allows for rapid basic feature comparison without requiring multi-level databases or historical data analysis frameworks, thus lowering storage and computing resource requirements and making them suitable for lightweight applications. However, they suffer from the following problems:

[0003] (1) The accuracy of rejection judgment is low, and it is easy to make false judgments or omissions due to file name disguise or content modification:

[0004] The current system relies primarily on basic feature matching for file identification, such as fixed file extensions and simple content keywords. This makes it difficult to counter filename obfuscation techniques, such as using double ".txt.exe" extensions, Unicode special characters for spoofing, and deceptive naming methods like space padding, as well as content-level evasion techniques, such as nested encrypted compressed packages, text encoding conversion, and segmentation to hide malicious code. The detection mechanism lacks deep analytical capabilities; for example, it is insufficient in verifying the binary features of the file's true type, allowing some malicious files to bypass detection through format spoofing. At the same time, the judgment logic based on a static rule base cannot effectively identify variant content that has undergone partial code obfuscation or semantic substitution, causing normal files to be blocked due to accidental rule triggering, or high-risk files to be missed due to lagging feature base updates.

[0005] (2) Historical data is not effectively reused, making it impossible to dynamically optimize matching rules through similarity analysis:

[0006] The system lacks a framework for storing file features in vectorized form and calculating similarity, resulting in massive historical samples being archived in isolation. This makes it impossible to extract common features, such as similar malicious code fragments and high-frequency risk structural patterns, through cluster analysis. Rule iteration relies on manual experience and lacks an automated association learning mechanism, making it difficult to identify the implicit correlation between new attack methods and historical data, such as the code reuse characteristics of similar payloads in different attack events. Furthermore, the lack of a time-series-based threat evolution model makes it impossible to predict and intercept variant files generated by attackers through small-scale, incremental modifications, causing defense rules to lag behind the evolution of attack techniques in the long term.

[0007] (3) The file storage lacks classification logic, resulting in low efficiency in subsequent processing:

[0008] After files are entered into the database, a single storage strategy is used, such as storing them only by timestamp or upload order, without establishing multi-dimensional classification indexes based on file attributes, content characteristics, or processing status. This results in subsequent operations, such as batch retrieval of high-risk documents or correlation analysis of files with the same source of attack, requiring traversal of the entire dataset, and the response speed drops sharply as the data volume increases. At the same time, there is no collaborative mechanism with the business flow, such as automatically routing suspected sensitive files to a dedicated sandbox for detection, which exacerbates the workload of manual review and makes it difficult to support classification-based priority scheduling, such as prioritizing the handling of high-risk files.

[0009] Therefore, it is essential to invent a network connection management system and method based on Internet of Things (IoT) devices to solve the above problems. Summary of the Invention

[0010] To overcome the aforementioned deficiencies in the prior art, this invention provides a network connection management system and method based on Internet of Things (IoT) devices. It constructs a three-tiered database architecture: a rejected file database, a threat file database, and a pending review database. A multi-level composite retrieval mechanism is employed to achieve intelligent file classification management. The core processes include: rejected file retrieval based on a multi-field joint index, historical matching verification of sender information, and a dual verification mechanism for file content relevance / similarity. It also innovatively configures a 72-hour automatic cleanup strategy for the threat file database. Technical features cover multi-dimensional retrieval fields such as file attributes, terminal identifiers, and IP addresses. A notification mechanism combining data fingerprint feature codes and verification QR codes is used, along with a dynamic benchmark value judgment algorithm to achieve risk-level classification and handling. This effectively solves the problems mentioned in the background technology.

[0011] To achieve the above objectives, the present invention provides the following technical solution: a network connection management system and method based on Internet of Things (IoT) devices, specifically including the following steps:

[0012] S1. The receiving end receives a data text set reception request and sends the data text set to the cloud management platform; the cloud management platform searches the data text set and performs a composite condition search on the data text set in the first database based on a multi-field composite index strategy; the first database is a rejected file database;

[0013] S2. If historical data with similar data characteristics is found, a rejection instruction is executed, and the data text set is stored in the first database; if historical data with similar data characteristics is not found, a matching is performed, and the file name is matched with the sender information in the third database; the third database is the database of files to be reviewed.

[0014] S3. Perform a second match based on the first match result; if the first match has historical data with the same data characteristics, then perform name-content relevance matching, scan the data text set content, match the relevance with the content of historical rejected files, and calculate the relevance value; if the first match does not have the same data characteristics, then perform similarity matching, scan the file content, match the content similarity of the file content in the first database, and calculate the similarity value.

[0015] S4. If the relevance reaches the benchmark value, the data text set is stored in the third database. If the benchmark value is not reached, the data text set is then matched for similarity and the similarity value is calculated.

[0016] S5. Classify the data text sets that are matched for similarity according to the size of the similarity value, and store the data text sets in the corresponding databases; the databases also include a second database, which is a threat file database, and is configured with a data isolation storage area and an automatic cleanup strategy;

[0017] S6, the cloud management platform sends notification information to the receiving end based on the storage location of the data text set.

[0018] Preferably, the multiple fields include file attributes, sending terminal identifier, IP address, and digital certificate information; the file attributes include file name, file size, and data text set keywords extracted after scanning the data text set content.

[0019] Preferably, the classification method based on the similarity value is as follows: when the similarity value S∈[0,0.3), the data text set is stored in the third database; when S∈[0.3,0.7), the data text set is stored in the second database; and when S∈[0.7,1], the data text set is stored in the first database.

[0020] Preferably, the relevance calculation formula is: In the formula, R represents the relevance value, w represents the keyword, F represents the set of keywords in the filename, C represents the set of words in the file content, and TF-IDF(w,T) represents the TF-IDF weight of word w in text T. TF-IDF(w,T) = TF(w,T) × IDF(w,D). In the formula, D w This represents the number of documents in the database that contain the keyword 'w', where N represents the total number of documents in the database. In the formula, T w This represents the number of times the keyword w appears in text T, where T is the number of times the keyword w appears in text T. 总 This represents the total number of words in text T.

[0021] Preferably, the content similarity calculation method is as follows: In the formula, S represents the similarity value, and D... iLet represent the i-th historical file in the database, DB1 represent the rejected file database, V represent the entire vocabulary, and C represent the content of the file to be analyzed.

[0022] Preferably, the threshold setting formula is R 基准 =R 初始 +α•ΔR 误判 +β•ΔR 漏检 +γ•F 人工 In the formula, α, β, and γ are weighting coefficients, determined through Bayesian optimization, with typical values ​​of α=0.3, β=0.5, γ=0.2, and ΔR. 误判 This represents the false positive rate adjustment term, calculated using the following formula: In the formula, A represents the false positive rate, ε represents the smoothing factor (e.g., 0.001, to avoid division by zero errors), and ΔR 漏检 This represents the false positive rate adjustment term, calculated using the following formula: In the formula, B represents the false negative rate, and F... 人工 This represents the manual feedback correction item, calculated using the following formula: In the formula, Q represents the number of files confirmed as malicious in the manual review results, P represents the number of files mistakenly labeled as normal, M represents the total number of reviews, and R... 初始 Based on statistics from a historical malicious file sample database, the calculation formula is as follows: In the formula, μ 恶意 σ represents the mean of the relevance between the known malicious file name and its content. 恶意 represents the standard deviation; k represents the confidence factor.

[0023] Preferably, the similar data features are a combination of file attribute hash values, sending terminal device fingerprints, and network behavior feature vectors.

[0024] Preferably, the notification information includes a data fingerprint feature code, a handling suggestion, a storage partition identifier, and a verification QR code.

[0025] Preferably, the network connection management system based on IoT devices includes a cloud management platform, a receiver, and a database.

[0026] The technical effects and advantages of this invention are as follows:

[0027] 1. By constructing a three-level database architecture and a multi-level composite retrieval mechanism, refined classification and management of files are achieved; based on a multi-field composite index strategy, retrieval efficiency is significantly improved, high-risk files are quickly identified and isolated, and the cost of manual intervention is reduced;

[0028] 2. By employing a dual verification mechanism of file content relevance matching and similarity matching, combined with a dynamic threshold judgment algorithm, it effectively distinguishes between malicious files and false positives, reducing the probability of missed detections and false positives; the dual verification covers historical data features and real-time content analysis, enhancing adaptability to new threats.

[0029] 3. Configure a threat file database with a 72-hour automatic cleanup policy, combined with a data isolation storage area, to avoid redundant data occupying resources for a long time, while reducing the impact of old threats on the system, and significantly optimizing storage efficiency and system response speed. Attached Figure Description

[0030] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0031] Figure 1 This is a step diagram of the present invention.

[0032] Figure 2 This is the overall flowchart of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] This invention provides a network connection management system and method based on Internet of Things (IoT) devices, characterized in that it specifically includes: a cloud management platform, a receiving end, and a database;

[0035] In a more specific application of this invention, the cloud management platform is used to perform multi-dimensional analysis on the received data text set, specifically including: file attributes, sender information, and content features. Through composite condition retrieval and dynamic matching algorithms, it realizes automated risk assessment. Based on the matching results, such as similarity and relevance, it triggers a hierarchical storage strategy to distinguish between malicious files, potential threats, and files pending review.

[0036] The database adopts a three-tier database architecture. The first database is used to permanently store malicious data text sets. The second database is a data isolation storage area used to store data text sets with unknown sources or threats. The third database is used to store data text sets that are pending manual review.

[0037] like Figure 1 As shown, the specific implementation of the present invention includes the following steps:

[0038] S1. The receiving end receives the data text set reception request and sends the data text set to the cloud management platform; the cloud management platform retrieves the data text set and performs a composite condition search on the data text set in the first database based on a multi-field composite index strategy.

[0039] In step S1 above, the multiple fields include file attributes, sending terminal identifier, IP address, and digital certificate information; the file attributes include file name, file size, and keywords extracted from the scanned data text set content.

[0040] Upon receiving a data text set transmission request from an external IoT device, the receiving end immediately uploads the complete data packet, including metadata and content, to the cloud management platform. Transmission utilizes a TLS encrypted channel to ensure data integrity verification and source authentication. The cloud management platform extracts multi-dimensional features from the data text set, including: filename, file size, MIME type, keyword set, sender IP address, device fingerprint, and digital certificate serial number. Based on these extracted multi-dimensional features, an index is constructed, and composite condition searches are performed.

[0041] It should be further explained that the specific implementation method of the composite condition retrieval is as follows:

[0042] In the first database, combined Boolean queries from an Elasticsearch cluster are used to perform joint queries on the extracted multi-dimensional feature fields, including fuzzy matching of filenames, Levenshtein distance ≤ 2, Jaccard similarity of keyword sets ≥ 0.6, location matching of IP address ranges, CIDR block comparison, precise matching of device fingerprints, and real-time query of certificate blacklists.

[0043] S2. If historical data with similar data characteristics exists, execute the rejection instruction and store the data text set in the first database; if historical data with similar data characteristics does not exist, perform a match and match the file name with the sender information in the third database.

[0044] It should be further explained that if historical data with similar characteristics exists, the multi-dimensional features of the data text set are extracted and the data text set is stored in the corresponding database fields.

[0045] S3. Perform a second match based on the first match result; if the first match has historical data with the same data characteristics, then perform name-content relevance matching, scan the data text set content, match the relevance with the content of historical rejected files, and calculate the relevance value; if the first match does not have the same data characteristics, then perform similarity matching, scan the file content, match the content similarity of the file content in the first database, and calculate the similarity value.

[0046] In step S3 above, the formula for calculating the relevance matching value is as follows: In the formula, R represents the relevance value, w represents the keyword, F represents the set of keywords in the filename, C represents the set of words in the file content, and TF-IDF(w,T) represents the TF-IDF weight of word w in text T. TF-IDF(w,T) = TF(w,T) × IDF(w,D). In the formula, D w This represents the number of documents in the database that contain the keyword 'w', where N represents the total number of documents in the database. In the formula, T w This represents the number of times the keyword w appears in text T, where T is the number of times the keyword w appears in text T. 总 This represents the total number of words in text T.

[0047] The formula for calculating the similarity value is as follows: In the formula, S represents the similarity value, and D... i Let represent the i-th historical file in the database, DB1 represent the rejected file database, V represent the entire vocabulary, and C represent the content of the file to be analyzed.

[0048] Relevance value calculation example: Scenario:

[0049] File name: Equipment Failure Report;

[0050] Extract keywords F = {equipment, malfunction, report}

[0051] Content C: Total words: 200; device appeared 2 times; malfunction appeared 5 times; report appeared 0 times.

[0052] Database D: Total number of documents N=1000, of which: devices appear in 300 documents, faults appear in 50 documents, and reports appear in 200 documents;

[0053] Calculation process:

[0054] TF(device,C) = 2 / 200 = 0.01

[0055] TF(fault, C) = 5 / 200 = 0.025

[0056] TF(report,C)=0 / 200=0

[0057] IDF(device, D) = log(1000 / (300+1)) ≈ log(3.32) ≈ 1.20

[0058] IDF(fault, D) = log(1000 / (50+1)) ≈ log(19.61) ≈ 2.97

[0059] IDF(report,D) = log(1000 / (200+1)) ≈ log(4.98) ≈ 1.61

[0060] TF-IDF(device,C) = 0.01 × 1.20 = 0.012

[0061] TF-IDF(fault, C) = 0.025 × 2.97 = 0.074

[0062] TF-IDF(report,C)=0×1.61=0,

[0063] =0.012 + 0.074 = 0.086,

[0064] Assuming all other words are common words, totaling 193, each composed of 20 different words, and assuming each other word appears in an average of 800 documents, and each word appears on average in content C...

[0065] The average IDF for each word is approximately ln(1000 / 801) ≈ ln(1.2484) ≈ 0.2217.

[0066] The average TF-IDF for each word is approximately 0.04825 × 0.2217 ≈ 0.01070.

[0067] TF-IDF 2 (Equipment, C) = (0.01 × 1.20) 2 =0.000144,

[0068] TF-IDF 2 (Fault, C) = (0.025 × 2.97) 2 =0.005535,

[0069] TF-IDF 2 (Report, C) = (0 × 1.61) 2 =0,

[0070] The total value of the keyword portion = 0.000144 + 0.005535 = 0.005679.

[0071] For the 20 other words, the TF-IDF² of each word is approximately 0.01070. 2=0.0001145,

[0072] The sum of the other words = 20 × 0.0001145 = 0.002290.

[0073] = (0.005679 + 0.002290) 1 / 2 ≈0.08927,

[0074] ≈1.1960,

[0075] R=0.086 / (1.1960×0.08927)≈0.8092

[0076] In conclusion, filenames are highly correlated with file content, so storing the data text set in a third-party database is recommended.

[0077] Scene:

[0078] Current file C: Keywords and TF-IDF weights are {Motor: 0.8, Overheat: 1.2, Shutdown: 0.9}. Database DB1: Contains two historical files: D1: {Motor: 1.0, Fault: 0.7, Fire: 1.4}, D2: {Overheat: 0.9, Shutdown: 1.1, Maintenance: 0.5};

[0079] Calculation process:

[0080] Calculate the similarity SD1 between the current file and D1. ,

[0081] Calculate the similarity between the current file and D2: SD2= ,

[0082] S = max(0.25, 0.78) = 0.78,

[0083] Conclusion: The similarity between the current file and the files in the first database is 0.78;

[0084] S4. If the relevance reaches the benchmark value, the data text set is stored in the third database. If it does not reach the benchmark value, the data text set is then subjected to similarity matching. The data text sets are classified according to the size of the similarity value and stored in the database.

[0085] It should be further explained that the formula for setting the reference value is R. 基准 =R 初始 +α•ΔR 误判 +β•ΔR 漏检 +γ•F 人工 In the formula, α, β, and γ are weighting coefficients, determined through Bayesian optimization, with typical values ​​of α=0.3, β=0.5, γ=0.2, and ΔR.误判 This represents the false positive rate adjustment term, calculated using the following formula: In the formula, A represents the false positive rate, ε represents the smoothing factor (e.g., 0.001, to avoid division by zero errors), and ΔR 漏检 This represents the false positive rate adjustment term, calculated using the following formula: In the formula, B represents the false negative rate, and F... 人工 This represents the manual feedback correction item, calculated using the following formula: In the formula, Q represents the number of files confirmed as malicious in the manual review results, P represents the number of files mistakenly labeled as normal, M represents the total number of reviews, and R... 初始 This represents the initial baseline value, derived from statistics from a historical malicious file sample database. The calculation formula is as follows: In the formula, μ 恶意 σ represents the mean of the relevance between the known malicious file name and its content. 恶意 represents the standard deviation; k represents the confidence factor, which is determined based on expert advice and historical experience.

[0086] Example:

[0087] Example of initial baseline value calculation:

[0088] If μ 恶意= 0.8, σ 恶意= 0.1, take k=1,

[0089] Then R 初始 =0.8−1×0.1=0.7;

[0090] Example of calculating manual feedback correction items:

[0091] If 100 documents are reviewed, and 80 are confirmed to be malicious, while 10 are mistakenly labeled as normal, then...

[0092] Therefore, F_labor = (80−10) / 100 = 0.7;

[0093] Example of benchmark value calculation:

[0094] R_initial = 0.7; recent false positive rate = 1%, false negative rate = 1%, F_manual = 0.7;

[0095] α=0.4, β=0.4, γ=0.2;

[0096] R 基准 =0.7+0.4•ln[1 / (0.01+0.001)]+0.4•-ln[1 / (0.01+0.001)]+0.2•0.7=0.84;

[0097] Conclusion: Adjust the baseline value to 0.84;

[0098] S5. Classify the data text sets for similarity matching according to the size of the similarity value and store the data text sets in the database;

[0099] It should be further explained that the classification method based on the similarity value is as follows: when the similarity value S∈[0,0.3), the data text set is stored in the third database; when S∈[0.3,0.7), the data text set is stored in the second database; and when S∈[0.7,1], the data text set is stored in the first database.

[0100] S6, the cloud management platform sends notification information to the receiving end based on the storage location of the data text set.

[0101] The cloud management platform dynamically generates a notification containing the following key information based on the final storage location of the data text set:

[0102] Data fingerprint: A unique identifier generated based on the hash value or feature vector of the file content, used for subsequent tracking and verification.

[0103] Recommended actions: If stored in the first database: the receiving end is advised to immediately terminate the transmission and mark the sender as a high-risk terminal; if stored in the second database: the receiving end is advised to temporarily isolate the file and perform dynamic monitoring before automatic cleanup within 72 hours; if stored in the third database: the receiving end is notified that manual review is required.

[0104] Storage partition identifier: Clearly indicates the database to which a file belongs;

[0105] Verification QR code: Contains an encrypted metadata link. After scanning, the receiving end can obtain file details, processing logs, or submit a review request.

[0106] The overall flowchart of the invention is as follows Figure 2 As shown;

[0107] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A network connection management method based on Internet of Things (IoT) devices, characterized in that, Specifically, the following steps are included: S1. The receiving end receives a data text set reception request and sends the data text set to the cloud management platform; the cloud management platform searches the data text set and performs a composite condition search on the data text set in the first database based on a multi-field composite index strategy; the first database is a rejected file database; S2. If historical data with similar data characteristics is found, a rejection instruction is executed, and the data text set is stored in the first database; if historical data with similar data characteristics is not found, a matching is performed, and the file name is matched with the sender information in the third database; the third database is the database of files to be reviewed. S3. Perform a second match based on the first match result; if the first match has historical data with the same data characteristics, then perform name-content relevance matching, scan the data text set content, match the relevance with the content of historical rejected files, and calculate the relevance value; if the first match does not have the same data characteristics, then perform similarity matching, scan the file content, match the content similarity of the file content in the first database, and calculate the similarity value. S4. If the relevance reaches the benchmark value, the data text set is stored in the third database. If the benchmark value is not reached, the data text set is then matched for similarity and the similarity value is calculated. S5. Classify the data text sets that are matched for similarity according to the size of the similarity value, and store the data text sets in the corresponding databases; the databases also include a second database, which is a threat file database, and is configured with a data isolation storage area and an automatic cleanup strategy; S6, the cloud management platform sends notification information to the receiving end based on the storage location of the data text set.

2. The network connection management method based on IoT devices as described in claim 1, characterized in that: The multiple fields include file attributes, sending terminal identifier, IP address, and digital certificate information; the file attributes include file name, file size, and keywords extracted from the scanned data text set content.

3. The network connection management method based on IoT devices as described in claim 2, characterized in that: The classification method based on similarity is as follows: when the similarity value S∈[0,0.3), the data text set is stored in the third database; when S∈[0.3,0.7), the data text set is stored in the second database; and when S∈[0.7,1], the data text set is stored in the first database.

4. The network connection management method based on IoT devices as described in claim 1, characterized in that: The formula for calculating the relevance is: In the formula, R represents the relevance value, w represents the keyword, F represents the set of keywords in the filename, C represents the set of words in the file content, and TF-IDF(w,T) represents the TF-IDF weight of word w in text T. TF-IDF(w,T) = TF(w,T) × IDF(w,D). In the formula, D w This represents the number of documents in the database that contain the keyword 'w', where N represents the total number of documents in the database. In the formula, T w This represents the number of times the keyword w appears in text T, where T is the number of times the keyword w appears in text T. 总 This represents the total number of words in text T.

5. The network connection management method based on IoT devices as described in claim 1, characterized in that: The content similarity calculation method is as follows: In the formula, S represents the similarity value, and D... i Let represent the i-th historical file in the database, DB1 represent the rejected file database, V represent the entire vocabulary, and C represent the content of the file to be analyzed.

6. The network connection management method based on IoT devices as described in claim 1, characterized in that: The formula for setting the benchmark value is R. 基准 =R 初始 +α•ΔR 误判 +β•ΔR 漏检 +γ•F 人工 In the formula, α, β, and γ are weighting coefficients, determined through Bayesian optimization, ΔR 误判 This represents the false positive rate adjustment term, calculated using the following formula: In the formula, A represents the false positive rate, and ε represents the smoothing factor. In the formula, B represents the false negative rate, and F... 人工 This represents the manual feedback correction item, calculated using the following formula: In the formula, Q represents the number of files confirmed as malicious in the manual review results, P represents the number of files mistakenly labeled as normal, M represents the total number of reviews, and R... 初始 Based on statistics from a historical malicious file sample database, the calculation formula is as follows: In the formula, μ 恶意 σ represents the mean of the relevance between the known malicious file name and its content. 恶意 represents the standard deviation; k represents the confidence factor.

7. The network connection management method based on IoT devices as described in claim 1, characterized in that: The data features of the same type are a combination of file attribute hash values, sending terminal device fingerprints, and network behavior feature vectors.

8. The network connection management method based on IoT devices as described in claim 1, characterized in that: The notification information includes a data fingerprint feature code, handling suggestions, storage partition identifier, and verification QR code.

9. A network connection management system based on Internet of Things (IoT) devices, using the method described in any one of claims 1-8, characterized in that: This includes a cloud management platform, a receiving end, and a database.

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