Multidimensional data security transmission method and system based on Internet of Things

Abstract

The present invention discloses a multi-dimensional data security transmission method and system based on the Internet of Things, which relates to the field of data security transmission technology, including collecting multi-dimensional data based on Internet of Things devices for data processing, extracting data features for data classification and storing them in a distributed database; performing data security rating based on a distributed database, formulating a data transmission strategy to transmit the data to a central database; the central database tracks the data status and transmission status in real time, and verifies after receiving the data. The present invention collects multi-dimensional data through the Internet of Things for data processing and classification, and performs data storage and data security level assessment through a distributed database, which effectively improves data processing efficiency and data security, and at the same time formulates a data transmission strategy according to the data security level to encrypt and transmit the data, ensuring the security and reliability of the data during the transmission process.

Description

Technical Field

[0001] The present invention relates to the technical field of data security transmission, and in particular to a multi-dimensional data security transmission method and system based on the Internet of Things. Background Art

[0002] Against the background of rapid development of modern information technology, Internet of Things technology, as an important bridge connecting the physical world and the information world, has become a hot spot for research and application. Internet of Things technology realizes the connection and management of physical devices through information technology means such as sensors, networks, and data processing, supports data collection, transmission, storage and analysis, and thus realizes intelligent control and decision support. With the continuous expansion of Internet of Things application scenarios, such as smart homes, smart cities, industrial Internet, etc., the demand for secure data transmission is growing. The data in the Internet of Things environment is diverse, real-time and massive, which puts higher requirements on data transmission and processing technology. However, the existing Internet of Things data security transmission methods face many deficiencies. The existing technology fails to make full use of data characteristics for effective classification and processing, resulting in low data management efficiency, and lacks transmission measures for different data security implementations, making it difficult to ensure data integrity and reliability. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing multi-dimensional data security transmission method and system based on the Internet of Things, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing technology fails to fully utilize data features for effective classification and processing, resulting in inefficient data management, and lacks transmission measures for different data security implementations, making it difficult to ensure data integrity and reliability.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a multi-dimensional data security transmission method based on the Internet of Things, which includes collecting multi-dimensional data based on Internet of Things devices for data processing, extracting data features for data classification and storing them in a distributed database; performing data security rating based on the distributed database, formulating a data transmission strategy to transmit the data to a central database; the central database tracks the data status and transmission status in real time, and verifies the data after receiving it.

[0006] As a preferred solution of the multi-dimensional data secure transmission method based on the Internet of Things of the present invention, wherein: the data processing based on the collection of multi-dimensional data by the Internet of Things device and the extraction of data features for data classification refer to the collection of original multi-dimensional data by the Internet of Things device for data cleaning, removal of duplicate records, correction of erroneous data and filling of missing values;

[0007] After preprocessing, natural language processing technology is used to extract data features for each type of data. The formula is:

[0008]

[0009] Where T is the extracted data feature value, f t，d is the number of times data t appears in document d, t'' is the data in document d, and f t‘’，d is the number of times data t'' appears in document d, where D is the document set;

[0010] After extracting each type of data feature, assign dimensions to each data feature, and fill the feature dimension according to the data feature value T to generate a data feature vector;

[0011] The data is classified using clustering algorithms based on the data feature vectors.

[0012] As a preferred solution of the multi-dimensional data security transmission method based on the Internet of Things described in the present invention, wherein: the storage in a distributed database means storing the classified data in the distributed database closest to the Internet of Things device, the distributed database performs data protection operations, and generates a data storage directory based on data classification and sends it to the central database for synchronization through the Internet of Things, and the distributed database automatically generates data operation records based on timestamps when storing and transmitting data.

[0013] As a preferred solution of the multi-dimensional data security transmission method based on the Internet of Things described in the present invention, wherein: the data security rating based on the distributed database refers to adding a label to each type of stored data according to data classification, setting a sensitivity weight for each label, and counting the number of occurrences of each type of data;

[0014] The data sensitivity is scored using the formula:

[0015]

[0016] Where P is the data sensitivity score, ω i is the sensitivity weight of the i-th type of data, C i is the number of occurrences of the i-th type of data, α is the adjustment parameter, and n is the total number of data types;

[0017] The data sensitivity score is normalized and smoothed using the integral form of the sigmoid function;

[0018] Randomly select any classified data from the distributed database, count the number of historical security events related to the classified data, and define the data security level scoring threshold as:

[0019]

[0020] Where B is the data security level scoring threshold, m is the total number of selected data categories, and H i is the number of historical security events related to the i-th type of data, γ is the parameter for adjusting the impact of historical events, λ is the normalization parameter, and μ is the bias term;

[0021] After obtaining the data sensitivity score P and the data security level score threshold B, the data security level is judged. If P>B, it means that the data is at a high security level, otherwise it is at a low security level.

[0022] As a preferred solution of the multi-dimensional data security transmission method based on the Internet of Things described in the present invention, wherein: the formulation of a data transmission strategy to transmit data to a central database refers to formulating a data transmission strategy according to the data security level. If the data is of a high security level, a high-strength AES-256 encryption algorithm is used to encrypt the data. A high-level authority for data security access is set, and an encrypted tunnel is selected for data transmission. If the data is of a low security level, a common AES-128 encryption algorithm is used for data encryption, and the Internet of Things is used for data transmission.

[0023] As a preferred solution of the multi-dimensional data security transmission method based on the Internet of Things described in the present invention, the central database tracks the data status and transmission status in real time, which means that the distributed database encrypts and transmits the data according to the formulated data transmission strategy, and the central database receives the data and monitors the data transmission status in real time. If any abnormal status is detected, the data transmission task is immediately suspended and the staff is notified to conduct abnormal investigation.

[0024] As a preferred solution of the multi-dimensional data security transmission method based on the Internet of Things described in the present invention, wherein: the verification after receiving the data refers to the distributed database saving an encrypted data snapshot before encrypting the data and synchronously sending it to the central database, the central database decrypts the data after receiving the data, and saves the decrypted data snapshot for comparison with the encrypted data snapshot, and at the same time compares the data classification with the data classification directory generated by the distributed database, if the two are the same, the central database stores the transmitted data according to the data classification, if there is a difference between any of the two, the central database transfers the decrypted data to the security sandbox to notify the staff to perform data analysis, and receives the data again through the distributed database.

[0025] Another object of the present invention is to provide a multi-dimensional data security transmission system based on the Internet of Things, which includes a data collection module for collecting multi-dimensional data through Internet of Things devices and performing pre-processing and feature extraction and classification on the data;

[0026] A data storage module, used for storing the collected data;

[0027] Data analysis module, used to perform security scoring on the collected data and assess the data security level to formulate data transmission strategies;

[0028] A data encryption module is used to encrypt and transmit data according to a data transmission strategy;

[0029] The data verification module is used to compare and verify the data before and after transmission to determine the data changes.

[0030] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of the above-mentioned multi-dimensional data security transmission method based on the Internet of Things when executing the computer program.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned multi-dimensional data secure transmission method based on the Internet of Things.

[0032] The beneficial effects of the present invention are as follows: the present invention collects multi-dimensional data through the Internet of Things for data processing and classification, and performs data storage and data security level assessment through a distributed database, thereby effectively improving data processing efficiency and data security. At the same time, a data transmission strategy is formulated according to the data security level to encrypt and transmit the data, thereby ensuring the security and reliability of the data during the transmission process. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0034] Figure 1 The figure is a flowchart of a multi-dimensional data security transmission method based on the Internet of Things.

[0035] Figure 2 Schematic diagram of the structure for collecting and storing multidimensional data.

[0036] Figure 3 This is a structural diagram of a multi-dimensional data security transmission system based on the Internet of Things. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0040] Example 1

[0041] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and this embodiment provides a multi-dimensional data security transmission method based on the Internet of Things. The multi-dimensional data security transmission method based on the Internet of Things includes the following contents:

[0042] S1, collect multi-dimensional data based on IoT devices for data processing, extract data features for data classification and store them in a distributed database;

[0043] Specifically, data processing based on the collection of multidimensional data by IoT devices and data classification by extracting data features refer to data cleaning, removing duplicate records, correcting erroneous data, and filling missing values ​​by collecting original multidimensional data through IoT devices;

[0044] After preprocessing, natural language processing technology is used to extract data features for each type of data. The formula is:

[0045]

[0046] Where T is the extracted data feature value, f t，d is the number of times data t appears in document d, t'' is the data in document d, and f t‘’，d is the number of times data t'' appears in document d, where D is the document set;

[0047] After extracting each type of data feature, assign dimensions to each data feature, and fill the feature dimension according to the data feature value T to generate a data feature vector;

[0048] The data is classified using clustering algorithms based on the data feature vectors.

[0049] By extracting data features through advanced natural language processing technology and converting these features into data feature vectors, the efficiency of data processing is greatly improved. Especially in the Internet of Things environment, the data volume is huge and the types are diverse. Traditional data processing methods are often difficult to cope with. The present invention uses automated feature extraction and vectorization processing to enable data to be quickly classified and applied. The data feature vectors are classified using a clustering algorithm, which can automatically identify and summarize the inherent patterns and categories of the data. Compared with manual classification, this method is not only more efficient, but also more accurate. Especially when processing complex multidimensional data, the clustering algorithm can aggregate data according to the similarity of data features, thereby discovering potential connections between data, which is crucial for further analysis and application of the data.

[0050] Furthermore, storing in a distributed database means storing the classified data in a distributed database closest to the IoT device. The distributed database performs data protection operations and generates a data storage directory based on data classification and sends it to the central database for synchronization through the IoT. The distributed database automatically generates data operation records based on timestamps when storing and transmitting data.

[0051] By storing data in a distributed database closest to the IoT device, the efficiency of data storage and access is significantly improved. By utilizing the characteristics of a distributed database, data can be quickly written and retrieved, especially in large-scale IoT systems. This method effectively reduces data transmission delays and improves the response speed of the overall system. Data protection operations such as data encryption and backup are performed to ensure the security of data during storage and transmission. This step is crucial to preventing data leakage, tampering and loss. Especially when processing sensitive information, the present invention achieves effective management and real-time synchronization of data by generating a data storage directory and using a timestamp to generate data operation records. The data storage directory facilitates the organization and indexing of data, while the timestamp supports data version control and conflict resolution, ensuring data consistency.

[0052] S2, perform data security rating based on distributed databases and formulate data transmission strategies to transfer data to the central database;

[0053] Specifically, data security rating based on a distributed database means adding labels to each type of stored data according to data classification, setting sensitivity weights for each label, and counting the number of occurrences of each type of data;

[0054] The data sensitivity is scored using the formula:

[0055]

[0056] Where P is the data sensitivity score, ω iis the sensitivity weight of the i-th type of data, C i is the number of occurrences of the i-th type of data, α is the adjustment parameter, and n is the total number of data types;

[0057] The data sensitivity score is normalized and smoothed using the integral form of the sigmoid function;

[0058] Randomly select any classified data from the distributed database, count the number of historical security events related to the classified data, and define the data security level scoring threshold as:

[0059]

[0060] Where B is the data security level scoring threshold, m is the total number of selected data categories, and H i is the number of historical security events related to the i-th type of data, γ is the parameter for adjusting the impact of historical events, λ is the normalization parameter, and μ is the bias term;

[0061] After obtaining the data sensitivity score P and the data security level score threshold B, the data security level is judged. If P>B, it means that the data is at a high security level, otherwise it is at a low security level.

[0062] By adding labels to each type of data and setting sensitivity weights, combined with the number of data occurrences, this method can carefully evaluate the sensitivity of the data, and use the sigmoid function to normalize and smooth the scores to make the scoring results more stable and avoid the influence of extreme values. By counting the number of historical security events related to the classified data and defining the data security level scoring threshold, a method for dynamically assessing the data security level is provided. This method not only considers the current status of the data, but also takes into account the historical security performance of the data, providing a more comprehensive perspective for data security management. By comparing the data sensitivity score with the security level scoring threshold, a clear judgment logic is established to distinguish the high and low security levels of the data. This judgment logic simplifies the security level division process and makes data security management more efficient and intuitive.

[0063] Furthermore, formulating a data transmission strategy to transfer data to a central database means formulating a data transmission strategy according to the data security level. If the data is of a high security level, the high-strength AES-256 encryption algorithm is used for data encryption. And set a high-level permission for data security access, select an encrypted tunnel for data transmission, and if the data is of a low security level, use a common AES-128 encryption algorithm for data encryption, and use the Internet of Things for data transmission.

[0064] The present invention assesses the security level of data and adopts encryption algorithms of corresponding strength, which not only improves the security of data during transmission, but also takes into account the computing resources and time costs in the encryption process. High-security-level data adopts AES-256 encryption algorithm to maximize the security of sensitive data; low-security-level data adopts AES-128 encryption to ensure a balance between transmission efficiency and security. For high-security-level data, encrypted tunnels are selected for transmission, which greatly enhances the security of data transmission and effectively prevents data from being intercepted or tampered with during transmission. For low-security-level data, ordinary transmission means of the Internet of Things are used to ensure the efficiency of data transmission and meet basic security requirements. High-level access rights are set for high-security-level data to further enhance data security and ensure that only authorized users or systems can access the data. This measure, combined with the selection of encryption algorithms and the use of encrypted tunnels, constitutes a multi-level data security protection system.

[0065] S3. The central database tracks data status and transmission in real time and verifies the data after receiving it.

[0066] Specifically, the central database tracks data status and transmission status in real time, which means that the distributed database encrypts and transmits data according to the established data transmission strategy. The central database receives data and monitors the data transmission status in real time. If any abnormal status is detected, the data transmission task is immediately suspended and the staff is notified to investigate the abnormality.

[0067] By encrypting data during transmission at the distributed database level, the security of data during transmission is greatly enhanced, effectively preventing data from being intercepted or tampered with during transmission. The central database monitors the data transmission status in real time, and can immediately detect any anomalies in the transmission process and respond in a timely manner, thereby avoiding data loss or damage, ensuring high reliability of data transmission. When an abnormal state is detected, the data transmission task is immediately suspended and the staff is notified. This mechanism greatly reduces the risk of data loss due to anomalies and shortens the time to solve the problem. Especially for large-scale data processing and transmission systems, such as cloud computing services, big data analysis platforms, etc., it can handle anomalies in a timely manner to ensure the stable operation of the system and the security of data.

[0068] Furthermore, verification after receiving data means that the distributed database saves an encrypted data snapshot before encrypting the data and synchronously sends it to the central database. The central database decrypts the data after receiving the data, saves the decrypted data snapshot and compares it with the encrypted data snapshot, and compares the data classification with the data classification directory generated by the distributed database. If the two are the same, the central database stores the transmitted data according to the data classification. If there is a difference between any of the two, the central database transfers the decrypted data to the security sandbox to notify the staff to perform data analysis, and receives the data again through the distributed database.

[0069] By saving data snapshots before encryption and synchronizing them with the central database, the security and integrity of data during transmission are effectively ensured. On the central database side, by comparing data snapshots before and after encryption, it can be accurately verified that the data has not been tampered with or damaged during transmission. The decrypted data is compared with the encrypted data snapshot on the central database side, and the data classification is compared with the data classification directory generated by the distributed database, which improves the efficiency of data consistency verification. This method makes data synchronization between nodes more reliable and reduces problems caused by data inconsistency. When there are differences in data snapshot comparison or data classification directory comparison, the central database does not directly store the data, but transfers it to a secure sandbox and notifies staff to conduct further data analysis. This mechanism effectively isolates potential unsafe data to prevent it from having a negative impact on the central database and subsequent data processing processes.

[0070] Example 2

[0071] Reference Figure 3 , which is the second embodiment of the present invention, which is different from the previous embodiment and provides a multi-dimensional data security transmission system based on the Internet of Things, which includes:

[0072] The data collection module is used to collect multi-dimensional data through IoT devices and perform pre-processing and feature extraction and classification on the data;

[0073] A data storage module, used for storing the collected data;

[0074] Data analysis module, used to perform security scoring on the collected data and assess the data security level to formulate data transmission strategies;

[0075] A data encryption module is used to encrypt and transmit data according to a data transmission strategy;

[0076] The data verification module is used to compare and verify the data before and after transmission to determine the data changes.

[0077] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0078] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0079] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0080] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0081] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-dimensional data security transmission method based on the Internet of Things, characterized by: include, Collect multi-dimensional data based on IoT devices for data processing, extract data features for data classification and store them in distributed databases; Conduct data security rating based on distributed databases and formulate data transmission strategies to transfer data to the central database; The central database tracks data status and transmission in real time and verifies data after receiving it; The data processing based on the collection of multi-dimensional data by IoT devices and the extraction of data features for data classification refer to the collection of original multi-dimensional data by IoT devices for data cleaning, removal of duplicate records, correction of erroneous data and filling of missing values; After preprocessing, natural language processing technology is used to extract data features for each type of data. The formula is: Where T is the extracted data feature value, f t，d is the number of times data t appears in document d, t'' is the data in document d, and f t‘’，d is the number of times data t'' appears in document d, where D is the document set; After extracting each type of data feature, assign dimensions to each data feature, and fill the feature dimension according to the data feature value T to generate a data feature vector; Use clustering algorithms to classify data based on data feature vectors; The storing in the distributed database means storing the classified data in the distributed database closest to the IoT device, the distributed database performs data protection operations, and generates a data storage directory based on data classification and sends it to the central database through the IoT for synchronization, and the distributed database automatically generates data operation records based on timestamps when storing and transmitting data; The data security rating based on the distributed database refers to adding a label to each type of stored data according to data classification, setting a sensitivity weight for each label, and counting the number of occurrences of each type of data; The data sensitivity is scored using the formula: Where P is the data sensitivity score, ω i is the sensitivity weight of the i-th type of data, C i is the number of occurrences of the i-th type of data, α is the adjustment parameter, and n is the total number of data types; The data sensitivity score is normalized and smoothed using the integral form of the sigmoid function; Randomly select any classified data from the distributed database, count the number of historical security events related to the classified data, and define the data security level scoring threshold as: Where B is the data security level scoring threshold, m is the total number of selected data categories, and H i is the number of historical security events related to the i-th type of data, γ is the parameter for adjusting the impact of historical events, λ is the normalization parameter, and μ is the bias term; After obtaining the data sensitivity score P and the data security level score threshold B, the data security level is judged. If P>B, it means that the data is at a high security level, otherwise it is at a low security level; The said formulating a data transmission strategy to transmit data to a central database means formulating a data transmission strategy according to the data security level. If the data is of a high security level, the high-strength AES-256 encryption algorithm is used to encrypt the data, and a high-level authority for data security access is set, and an encrypted tunnel is selected for data transmission. If the data is of a low security level, the ordinary AES-128 encryption algorithm is used to encrypt the data, and the Internet of Things is used for data transmission; The central database real-time tracking of data status and transmission status means that the distributed database encrypts and transmits data according to the formulated data transmission strategy, and the central database receives data and monitors the data transmission status in real time. If any abnormal status is detected, the data transmission task is immediately suspended and the staff is notified to conduct abnormality investigation; The verification after receiving the data means that the distributed database saves an encrypted data snapshot before encrypting the data and sends it to the central database synchronously. After receiving the data, the central database decrypts the data, saves the decrypted data snapshot for comparison with the encrypted data snapshot, and compares the data classification with the data classification directory generated by the distributed database. If the two are the same, the central database stores the transmitted data according to the data classification. If there is a difference between any of the two, the central database transfers the decrypted data to the security sandbox to notify the staff to perform data analysis, and receives the data again through the distributed database.

2. A multi-dimensional data secure transmission system based on the Internet of Things, the system being used to implement the multi-dimensional data secure transmission method based on the Internet of Things according to claim 1, characterized in that: include, The data collection module is used to collect multi-dimensional data through IoT devices and perform pre-processing and feature extraction and classification on the data; A data storage module, used for storing the collected data; Data analysis module, used to perform security scoring on the collected data and assess the data security level to formulate data transmission strategies; A data encryption module is used to encrypt and transmit data according to a data transmission strategy; The data verification module is used to compare and verify the data before and after transmission to determine the data changes.

3. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the multi-dimensional data security transmission method based on the Internet of Things described in claim 1 are implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for secure transmission of multi-dimensional data based on the Internet of Things described in claim 1 are implemented.

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