Internet of Things data collection and processing method and host based on quantum encryption secure communication

Abstract

The present invention discloses an Internet of Things data collection and processing method and host based on quantum encryption secure communication, including the following steps: deploying multiple sensors in the Internet of Things device to collect environmental data in real time; using a quantum random number generator to generate a quantum key, and distributing it to the Internet of Things device and the data processing host through a quantum key distribution system, and using a quantum state super key negotiation mechanism to dynamically adjust the key distribution frequency and method to meet different needs. The Internet of Things device uses a dynamic quantum key to encrypt the data at multiple levels and transmit it to the data processing host through wireless communication technology. The security of the communication link is monitored by a link monitoring sensor to detect and respond to eavesdropping in a timely manner. After receiving the encrypted data, the data processing host uses the negotiated quantum key to decrypt layer by layer, restore the original data, and repair the errors in the transmission in combination with the quantum state error correction technology.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things communication technology, and in particular to an Internet of Things data collection and processing method and a host based on quantum encryption secure communication. Background Art

[0002] With the rapid development of IoT technology, more and more devices and sensors are connected to the network to collect, transmit and process data. These IoT devices play an important role in various application scenarios, such as smart homes, industrial automation, environmental monitoring and smart cities. However, with the increase in the number of IoT devices, the security of data transmission has become increasingly prominent. Although existing encryption technologies, such as symmetric encryption and asymmetric encryption, can protect data security to a certain extent, there are still some defects that cannot be ignored.

[0003] Traditional encryption technology mainly relies on complex mathematical problems, such as prime factorization and discrete logarithm problems. These problems are very difficult for classical computers, and encryption algorithms based on these problems have been considered secure in the past few decades. However, with the development of quantum computing technology, quantum computers can efficiently solve prime factorization and discrete logarithm problems in polynomial time. The currently widely used RSA and elliptic curve encryption algorithms will no longer be secure in the face of quantum computers.

[0004] Existing key distribution methods mainly rely on public key infrastructure, but the PKI system also has its vulnerabilities, such as security threats such as man-in-the-middle attacks and certificate forgery. In the IoT environment, key management becomes more complicated due to the large number of devices. Once the key is stolen, the attacker can decrypt the data communication, resulting in information leakage and tampering. Traditional encryption technology mainly focuses on the protection of the data itself, while the real-time monitoring and security protection of the communication link are relatively weak. Most of the existing link monitoring technologies rely on detecting abnormal traffic and patterns, but in the face of complex and advanced attack methods, these monitoring methods may not be able to detect and respond to eavesdropping and tampering in a timely manner.

[0005] In existing IoT systems, the security and reliability of data transmission are often not fully guaranteed. Especially in wireless communication environments, data is susceptible to interference and attacks, resulting in data loss, tampering, and theft. The openness of wireless communications allows attackers to attack without touching physical devices, which poses a huge security risk to data transmission. Existing IoT systems may produce various errors during data transmission, but traditional error correction technology cannot effectively deal with quantum state errors. Quantum state errors are caused by quantum measurements and quantum state changes. Traditional error correction technology cannot solve such problems, resulting in the accuracy and reliability of data transmission being affected.

[0006] Therefore, how to provide an IoT data collection and processing method and host based on quantum encryption secure communication is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0007] One purpose of the present invention is to propose an Internet of Things data collection and processing method and host based on quantum encryption secure communication. The present invention makes full use of quantum key distribution, quantum state super key negotiation, chaos theory multi-layer encryption, link security monitoring and quantum state error correction technology, and describes in detail a method for achieving high-security Internet of Things data transmission. It has the advantages of modular design, strong adaptability, and the ability to flexibly configure different modules to meet specific needs.

[0008] The Internet of Things data collection and processing method and host based on quantum encryption secure communication according to an embodiment of the present invention include the following steps:

[0009] S1. Deploy various sensors including temperature sensors, humidity sensors, pressure sensors and light intensity sensors in IoT devices to collect environmental data and related parameters in real time;

[0010] S2. Generate quantum keys using a quantum random number generator, distribute quantum keys to IoT devices and data processing hosts through a quantum key distribution system, and use a quantum state super key negotiation mechanism to dynamically adjust the frequency and method of quantum key distribution to meet the needs of different environments and sensors.

[0011] S3. The IoT device uses the received key to encrypt the collected data. In the data encryption process, a multi-layer encryption algorithm based on chaos theory is introduced to achieve multi-level encryption.

[0012] S4. The encrypted data is transmitted to the data processing host through wireless communication technology. During the transmission process, the link monitoring sensor is used to monitor the security of the communication link to detect and respond to eavesdropping in a timely manner;

[0013] S5. The data processing host receives the encrypted data and decrypts it using the quantum key to restore the original data. It combines quantum state error correction technology to automatically repair the quantum state errors generated during the transmission process.

[0014] S6. The decrypted data is processed in a data processing module, including data analysis, mining and storage. The data processing module is equipped with a quantum computing accelerator;

[0015] S7. In the process of data processing and transmission, a quantum state trust management mechanism is adopted to optimize the credibility of data transmission nodes through quantum state authentication and trust evaluation to prevent attacks from malicious nodes;

[0016] S8. Manage and process data in a distributed environment, and use dynamic load balancing technology based on adaptive learning to dynamically adjust data distribution based on node computing power and real-time data traffic.

[0017] Optionally, the S2 specifically includes:

[0018] S21, using a quantum random number generator to generate a high entropy random number R, and converting the high entropy random number into an initial quantum key K;

[0019] S22. Distribute the quantum key K to the IoT device and the data processing host through a quantum key distribution system. The quantum key distribution system includes a transmitting end and a receiving end, which are respectively deployed on the IoT device and the data processing host;

[0020] S23, adopt the quantum state super key negotiation mechanism, dynamically adjust the frequency and method of quantum key distribution, and the IoT device and data processing host generate their own initial key K 0,device and K 0,host , exchange the initial key through the quantum channel;

[0021] S24. Based on the received initial key, the IoT device and the data processing host respectively generate the negotiated quantum key K neg ;

[0022] S25. After the negotiation, the quantum key K neg Based on this, the quantum key distribution frequency f is dynamically adjusted. dis and distribution method dis , using the environment perception module and sensor data analysis module, update the quantum key distribution parameters in real time according to environmental changes and sensor status, and adjust the distribution strategy through adaptive algorithms to adapt to the needs of different environments and sensors:

[0023]

[0024] Among them, w i and v j are the weight coefficients of environmental parameters and sensor types, env i and sensor_j represent different environmental parameters and sensor types respectively;

[0025] S26, the dynamically adjusted quantum key K dyn Distribute to IoT devices and data processing hosts to maintain an efficient and secure key distribution mechanism under different environmental and sensor conditions.

[0026] Optionally, the S3 specifically includes:

[0027] S31. The IoT device receives the dynamically adjusted quantum key Kdyn After that, the collected data is encrypted;

[0028] S32. Perform preliminary encryption on the data using a multi-layer encryption algorithm based on chaos theory to generate preliminary encrypted data D1:

[0029] D1=C1(data,Kdyn)=sin(a·data+b·Kdyn)×cos(c·data+d·Kdyn);

[0030] Among them, C1 represents the encryption function of chaos theory, data is the collected original data, K dyn is the dynamically adjusted quantum key, a, b, c and d are the parameters of chaotic encryption;

[0031] S33, re-encrypting the initially encrypted data D1 to generate re-encrypted data D2;

[0032] S34, performing multi-level encryption processing on the data D2 after the secondary encryption, and finally generating multi-level encrypted data D final :

[0033] Dfinal = Cn(Dn-1, Kdyn);

[0034] Among them, C n Denotes the nth level encryption function of chaos theory, D n-1 Encrypted data for the n-1th layer, K dyn is the dynamically adjusted quantum key, and n is the number of encryption layers;

[0035] S35, the multi-level encrypted data D final It is stored in the secure storage module of the IoT device and transmitted to the data processing host through a secure communication channel.

[0036] Optionally, the S4 specifically includes:

[0037] S41, the multi-level encrypted data D final Transmit to the data processing host via wireless communication technology;

[0038] S42, during the data transmission process, using a link monitoring sensor to monitor the communication link in real time, the link monitoring sensor including a detection module and an analysis module, and obtaining a communication link status parameter link_status in real time;

[0039] S43, the link monitoring sensor detects and identifies potential eavesdropping and tampering behaviors by analyzing the acquired communication link status parameter link_status, and generates a security alarm S alert ;

[0040] S44, upon detection of a security alarm S alert When eavesdropping is indicated, link monitoring sensors immediately trigger security response mechanisms, including reallocating communication channels and adjusting encryption parameters;

[0041] S45, the data processing host receives the encrypted data D transmitted final After that, first verify the generated security alert S alert , confirm the security of data transmission, and if it is safe, perform subsequent data processing.

[0042] Optionally, the S5 specifically includes:

[0043] S51, the data processing host receives the multi-level encrypted data D transmitted via wireless communication technology final ;

[0044] S52, using the negotiated quantum key K neg Decrypt the received encrypted data layer by layer and restore the decrypted data D n-1 :

[0045]

[0046] in, Denotes the decryption function of the nth layer, D n-1 Decrypted data for layer n-1, K neg is the negotiated quantum key, g is the multi-level encryption parameter, α i is the decryption coefficient;

[0047] S53. Continue to use the quantum key K neg Decrypted data D layer by layer n-1 Decrypt and recover the next layer of data D n-2 , until the initial decrypted data D1 is restored;

[0048] S54, perform final decryption on the initially decrypted data D1 to restore the original data data:

[0049]

[0050] in, represents the decryption function of the initial encryption, D1 is the initial encrypted data, K neg is the negotiated quantum key, a, b, c and d are decryption parameters;

[0051] S55. Combined with quantum state error correction technology, error correction is performed on the recovered original data to repair the quantum state errors generated during the transmission process:

[0052]

[0053] Among them, data is the original data recovered, data corrected is the data after error correction, β i and γ are error correction parameters, error i is the quantum state error;

[0054] S56, the data after error correction corrected Perform verification and verify the data through the verification function V:

[0055]

[0056] Where V represents the verification function, δ i and ω i To verify the parameters.

[0057] Optionally, the S7 specifically includes:

[0058] S71. In the process of data processing and transmission, the quantum state trust management mechanism is adopted to calculate the trust value T of each data transmission node through the quantum state authentication and trust evaluation module. node ,The trust value is calculated comprehensively based on quantum state parameters and historical behavior data to evaluate the reliability of each node;

[0059] S72, update the trust value of each node in real time through the trust evaluation module, and generate a node trust evaluation report R trust , the report combines the communication link status parameters to dynamically reflect the current credibility of the node:

[0060]

[0061] Among them, τ represents the trust evaluation function, link_status is the communication link status parameter, and λ is the evaluation parameter;

[0062] S73. According to the trust assessment report R trust ,Dynamically adjust the data transmission path, select nodes with high trust values ​​for data transmission, and the path selection process is based on the principle of maximizing the trust value;

[0063] S74. During the data transmission process, the trust management mechanism continuously monitors the trust value changes of the nodes, promptly discovers and isolates untrustworthy nodes, and updates the node list L nodes ;

[0064] S75. Perform trust verification on the data transmitted through the trust management mechanism. The trust verification process analyzes the error-corrected data to ensure that it has not been tampered with or interfered with.

[0065] Optionally, the S8 specifically includes:

[0066] S81. Deploy multiple data processing nodes in a distributed environment. Each node has different computing power and processing performance. The node set is defined as N = {n1, n2, ..., n k}, where k is the number of nodes;

[0067] S82. Collect the computing capacity parameter C of each node i and real-time data flow parameter F i , define the comprehensive capability parameter of the node as P i ;

[0068] S83, using adaptive learning algorithm to calculate the comprehensive capability parameter P of each node i Perform dynamic evaluation and update to generate the initial load distribution strategy L initial :

[0069]

[0070] Among them, γ is the allocation coefficient, D is the total data volume, L initial The initial load distribution amount for each node;

[0071] S84, the initial load distribution strategy L initial Applied to data processing nodes, according to the comprehensive capability parameter P of the node i Dynamically distribute data to various data processing nodes;

[0072] S85. During the data processing process, monitor the processing status and data flow changes of each node in real time, and update the comprehensive capability parameter P of the node. i , and dynamically adjust the load distribution strategy L:

[0073]

[0074] Among them, γ ′ is the new distribution coefficient, P i (t) is the comprehensive capacity parameter of the node at time t, D(t) is the total data volume at time t, and L is the load distribution after dynamic adjustment;

[0075] S86, using an adaptive learning algorithm to provide feedback and optimize the adjustment results of the load distribution process through real-time monitoring and feedback adjustment;

[0076] S87. In a distributed environment, the processed data is summarized and integrated to generate the final processing result R, which is then transmitted to the central management system through a secure communication channel for storage and analysis.

[0077] Optionally, the host includes the following components:

[0078] Quantum random number generator: used to generate high-entropy random numbers and convert random numbers into initial quantum keys;

[0079] Quantum key distribution system: including the transmitter and receiver, which are deployed on IoT devices and data processing hosts respectively, and distribute quantum keys to IoT devices and data processing hosts through quantum channels;

[0080] Quantum state super key negotiation mechanism: used to dynamically adjust the frequency and method of quantum key distribution. The IoT device and the data processing host generate initial keys respectively, and exchange the initial keys through the quantum channel to generate the negotiated quantum key.

[0081] Data encryption and decryption module: IoT devices use dynamically adjusted quantum keys to perform multi-level encryption on the collected data. After receiving the encrypted data, the data processing host uses the negotiated quantum keys to decrypt layer by layer and restore the original data.

[0082] Wireless communication module: used to transmit encrypted data through wireless communication technology, monitor the security of the communication link during the transmission process, and promptly detect and respond to eavesdropping;

[0083] Quantum state error correction and verification module: used to perform error correction on the recovered original data, repair the quantum state errors generated during the transmission process, and verify the corrected data;

[0084] Trust management and load balancing module: Calculate the trust value of each data transmission node through quantum state authentication and trust evaluation, generate node trust evaluation reports, dynamically adjust the data transmission path, select nodes with high trust values ​​for data transmission, monitor the changes in node trust values, promptly discover and isolate untrusted nodes, manage and process data, use adaptive learning algorithms to dynamically adjust data distribution according to the computing power of nodes and real-time data traffic, and generate load distribution strategies;

[0085] Data aggregation and integration module: used to aggregate and integrate processed data, generate final processing results, and transmit them to the central management system through a secure communication channel for storage and analysis.

[0086] The beneficial effects of the present invention are:

[0087] The present invention significantly improves the security and reliability of IoT data collection and transmission by introducing quantum encryption secure communication technology. By using quantum key distribution technology, unconditional and secure key distribution is achieved through the non-cloning of quantum states and the unpredictability of quantum measurements, which completely solves the security risks in traditional key distribution methods. The quantum state super key negotiation mechanism can dynamically adjust the frequency and method of quantum key distribution according to the needs of different environments and sensors, ensuring that highly secure key distribution services can be provided in various usage environments. IoT devices use dynamically adjusted quantum keys to perform multi-level encryption based on chaos theory on the collected data, providing multiple protections and greatly enhancing the security of data during transmission.

[0088] During the data transmission process, the link monitoring sensor is used to monitor the security of the communication link in real time, which can promptly detect and respond to eavesdropping and tampering, further ensuring the security of data transmission. The data processing host combines quantum state error correction technology to perform error correction on the decrypted data, automatically repair quantum state errors that may occur during the transmission process, and ensure the accuracy and integrity of the data. The system is designed to be modular, allowing users to add, remove or replace different modules according to their needs, and flexibly build IoT platforms for different industries that meet specific needs, enhancing the applicability and flexibility of the system.

[0089] Through the quantum state authentication and trust assessment module, the data transmission path is calculated and dynamically adjusted, and nodes with high trust values ​​are selected for data transmission to ensure the reliability of the data transmission path. Using the adaptive learning algorithm, data allocation is dynamically adjusted according to the computing power of the node and the real-time data flow to ensure the efficient operation of the system in a distributed environment; the processed data is transmitted to the central management system through a secure communication channel for storage and further analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0091] Figure 1 This is a flowchart of the Internet of Things data collection and processing method based on quantum encryption secure communication and the host proposed by the present invention;

[0092] Figure 2 A schematic diagram of the working principle of the quantum key distribution and negotiation mechanism of the present invention;

[0093] Figure 3 A flowchart of the link security monitoring and response mechanism of the present invention;

[0094] Figure 4This is a workflow diagram of the trust management and load balancing module of the present invention. DETAILED DESCRIPTION

[0095] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0096] refer to Figure 1-4 , an Internet of Things data collection and processing method and host based on quantum encryption secure communication, comprising the following steps:

[0097] S1. Deploy various sensors including temperature sensors, humidity sensors, pressure sensors and light intensity sensors in IoT devices to collect environmental data and related parameters in real time;

[0098] S2. Generate quantum keys using a quantum random number generator, distribute quantum keys to IoT devices and data processing hosts through a quantum key distribution system, and use a quantum state super key negotiation mechanism to dynamically adjust the frequency and method of quantum key distribution to meet the needs of different environments and sensors.

[0099] S3. The IoT device uses the received key to encrypt the collected data. In the data encryption process, a multi-layer encryption algorithm based on chaos theory is introduced to achieve multi-level encryption.

[0100] S4. The encrypted data is transmitted to the data processing host through wireless communication technology. During the transmission process, the link monitoring sensor is used to monitor the security of the communication link to detect and respond to eavesdropping in a timely manner;

[0101] S5. The data processing host receives the encrypted data and decrypts it using the quantum key to restore the original data. It combines quantum state error correction technology to automatically repair the quantum state errors generated during the transmission process.

[0102] S6. The decrypted data is processed in a data processing module, including data analysis, mining and storage. The data processing module is equipped with a quantum computing accelerator;

[0103] S7. In the process of data processing and transmission, a quantum state trust management mechanism is adopted to optimize the credibility of data transmission nodes through quantum state authentication and trust evaluation to prevent attacks from malicious nodes;

[0104] S8. Manage and process data in a distributed environment, and use dynamic load balancing technology based on adaptive learning to dynamically adjust data distribution based on node computing power and real-time data traffic.

[0105] In this implementation, S2 specifically includes:

[0106] S21, using a quantum random number generator to generate a high entropy random number R, and converting the high entropy random number into an initial quantum key K;

[0107] S22. Distribute the quantum key K to the IoT device and the data processing host through a quantum key distribution system. The quantum key distribution system includes a transmitting end and a receiving end, which are respectively deployed on the IoT device and the data processing host;

[0108] S23, adopt the quantum state super key negotiation mechanism, dynamically adjust the frequency and method of quantum key distribution, and the IoT device and data processing host generate their own initial key K 0,device and K 0,host , exchange the initial key through the quantum channel;

[0109] S24. Based on the received initial key, the IoT device and the data processing host respectively generate the negotiated quantum key K neg ;

[0110] S25. After the negotiation, the quantum key K neg Based on this, the quantum key distribution frequency f is dynamically adjusted. dis and distribution method dis , using the environment perception module and sensor data analysis module, update the quantum key distribution parameters in real time according to environmental changes and sensor status, and adjust the distribution strategy through adaptive algorithms to adapt to the needs of different environments and sensors:

[0111]

[0112] Among them, w i and v j are the weight coefficients of environmental parameters and sensor types, env i and sensor_j represent different environmental parameters and sensor types respectively;

[0113] S26, the dynamically adjusted quantum key K dyn Distribute to IoT devices and data processing hosts to maintain an efficient and secure key distribution mechanism under different environmental and sensor conditions.

[0114] In this implementation, S3 specifically includes:

[0115] S31. The IoT device receives the dynamically adjusted quantum key K dyn After that, the collected data is encrypted;

[0116] S32. Perform preliminary encryption on the data using a multi-layer encryption algorithm based on chaos theory to generate preliminary encrypted data D1:

[0117] D1=C1(data,Kdyn)=sin(a·data+b·Kdyn)×cos(c·data+d·Kdyn);

[0118] Among them, C1 represents the encryption function of chaos theory, data is the collected original data, K dyn is the dynamically adjusted quantum key, a, b, c and d are the parameters of chaotic encryption;

[0119] S33, re-encrypting the initially encrypted data D1 to generate re-encrypted data D2;

[0120] S34, performing multi-level encryption processing on the data D2 after the secondary encryption, and finally generating multi-level encrypted data D final :

[0121] Dfinal = Cn(Dn-1, Kdyn);

[0122] Among them, C n Denotes the nth level encryption function of chaos theory, D n-1 Encrypted data for the n-1th layer, K dyn is the dynamically adjusted quantum key, and n is the number of encryption layers;

[0123] S35, the multi-level encrypted data D final It is stored in the secure storage module of the IoT device and transmitted to the data processing host through a secure communication channel.

[0124] In this implementation manner, the S4 specifically includes:

[0125] S41, the multi-level encrypted data D final Transmit to the data processing host via wireless communication technology;

[0126] S42, during the data transmission process, using a link monitoring sensor to monitor the communication link in real time, the link monitoring sensor including a detection module and an analysis module, and obtaining a communication link status parameter link_status in real time;

[0127] S43, the link monitoring sensor detects and identifies potential eavesdropping and tampering behaviors by analyzing the acquired communication link status parameter link_status, and generates a security alarm S alert ;

[0128] S44, upon detection of a security alarm S alert When eavesdropping is indicated, link monitoring sensors immediately trigger security response mechanisms, including reallocating communication channels and adjusting encryption parameters;

[0129] S45, the data processing host receives the encrypted data D transmitted final After that, first verify the generated security alert S alert , confirm the security of data transmission, and if it is safe, perform subsequent data processing.

[0130] In this implementation manner, S5 specifically includes:

[0131] S51, the data processing host receives the multi-level encrypted data D transmitted via wireless communication technology final ;

[0132] S52, using the negotiated quantum key K neg Decrypt the received encrypted data layer by layer and restore the decrypted data D n-1 :

[0133]

[0134] in, Denotes the decryption function of the nth layer, D n-1 Decrypted data for layer n-1, K neg is the negotiated quantum key, g is the multi-level encryption parameter, α i is the decryption coefficient;

[0135] S53. Continue to use the quantum key K neg Decrypted data D layer by layer n-1 Decrypt and recover the next layer of data D n-2 , until the initial decrypted data D1 is restored;

[0136] S54, perform final decryption on the initially decrypted data D1 to restore the original data data:

[0137]

[0138] in, represents the decryption function of the initial encryption, D1 is the initial encrypted data, K neg is the negotiated quantum key, a, b, c and d are decryption parameters;

[0139] S55. Combined with quantum state error correction technology, error correction is performed on the recovered original data to repair the quantum state errors generated during the transmission process:

[0140]

[0141] Among them, data is the original data recovered, data corrected is the data after error correction, β i and γ are error correction parameters, errori is the quantum state error;

[0142] S56, the data after error correction corrected Perform verification and verify the data through the verification function V:

[0143]

[0144] Where V represents the verification function, δ i and ω i To verify the parameters.

[0145] In this implementation manner, the S7 specifically includes:

[0146] S71. In the process of data processing and transmission, the quantum state trust management mechanism is adopted to calculate the trust value T of each data transmission node through the quantum state authentication and trust evaluation module. node ,The trust value is calculated comprehensively based on quantum state parameters and historical behavior data to evaluate the reliability of each node;

[0147] S72, update the trust value of each node in real time through the trust evaluation module, and generate a node trust evaluation report R trust , the report combines the communication link status parameters to dynamically reflect the current credibility of the node:

[0148]

[0149] Among them, τ represents the trust evaluation function, link_status is the communication link status parameter, and λ is the evaluation parameter;

[0150] S73. According to the trust assessment report R trust ,Dynamically adjust the data transmission path, select nodes with high trust values ​​for data transmission, and the path selection process is based on the principle of maximizing the trust value;

[0151] S74. During the data transmission process, the trust management mechanism continuously monitors the trust value changes of the nodes, promptly discovers and isolates untrustworthy nodes, and updates the node list L nodes ;

[0152] S75. Perform trust verification on the data transmitted through the trust management mechanism. The trust verification process analyzes the error-corrected data to ensure that it has not been tampered with or interfered with.

[0153] In this implementation manner, S8 specifically includes:

[0154] S81. Deploy multiple data processing nodes in a distributed environment. Each node has different computing power and processing performance. The node set is defined as N = {n1, n2, ..., n k}, where k is the number of nodes;

[0155] S82. Collect the computing capacity parameter C of each node i and real-time data flow parameter F i , define the comprehensive capability parameter of the node as P i ;

[0156] S83, using adaptive learning algorithm to calculate the comprehensive capability parameter P of each node i Perform dynamic evaluation and update to generate the initial load distribution strategy L initial :

[0157]

[0158] Among them, γ is the allocation coefficient, D is the total data volume, L initial The initial load distribution amount for each node;

[0159] S84, the initial load distribution strategy L initial Applied to data processing nodes, according to the comprehensive capability parameter P of the node i Dynamically distribute data to various data processing nodes;

[0160] S85. During the data processing process, monitor the processing status and data flow changes of each node in real time, and update the comprehensive capability parameter P of the node. i , and dynamically adjust the load distribution strategy L:

[0161]

[0162] Among them, γ ′ is the new distribution coefficient, P i (t) is the comprehensive capacity parameter of the node at time t, D(t) is the total data volume at time t, and L is the load distribution after dynamic adjustment;

[0163] S86, using an adaptive learning algorithm to provide feedback and optimize the adjustment results of the load distribution process through real-time monitoring and feedback adjustment;

[0164] S87. In a distributed environment, the processed data is summarized and integrated to generate the final processing result R, which is then transmitted to the central management system through a secure communication channel for storage and analysis.

[0165] In this embodiment, the host includes the following components:

[0166] Quantum random number generator: used to generate high-entropy random numbers and convert random numbers into initial quantum keys;

[0167] Quantum key distribution system: including the transmitter and receiver, which are deployed on IoT devices and data processing hosts respectively, and distribute quantum keys to IoT devices and data processing hosts through quantum channels;

[0168] Quantum state super key negotiation mechanism: used to dynamically adjust the frequency and method of quantum key distribution. The IoT device and the data processing host generate initial keys respectively, and exchange the initial keys through the quantum channel to generate the negotiated quantum key.

[0169] Data encryption and decryption module: IoT devices use dynamically adjusted quantum keys to perform multi-level encryption on the collected data. After receiving the encrypted data, the data processing host uses the negotiated quantum keys to decrypt layer by layer and restore the original data.

[0170] Wireless communication module: used to transmit encrypted data through wireless communication technology, monitor the security of the communication link during the transmission process, and promptly detect and respond to eavesdropping;

[0171] Quantum state error correction and verification module: used to perform error correction on the recovered original data, repair the quantum state errors generated during the transmission process, and verify the corrected data;

[0172] Trust management and load balancing module: Calculate the trust value of each data transmission node through quantum state authentication and trust evaluation, generate node trust evaluation reports, dynamically adjust the data transmission path, select nodes with high trust values ​​for data transmission, monitor the changes in node trust values, promptly discover and isolate untrusted nodes, manage and process data, use adaptive learning algorithms to dynamically adjust data distribution according to the computing power of nodes and real-time data traffic, and generate load distribution strategies;

[0173] Data aggregation and integration module: used to aggregate and integrate processed data, generate final processing results, and transmit them to the central management system through a secure communication channel for storage and analysis.

[0174] Embodiment 1:

[0175] In order to verify the feasibility of the present invention in implementation, the present invention is applied in a smart city environment, and the air quality of the entire city needs to be monitored and analyzed in real time so that timely measures can be taken to improve the air quality. To this end, a large number of environmental sensors are deployed, including temperature, humidity, pressure and air pollutant sensors. These sensors are distributed in various corners of the city, and the collected data is transmitted to the central data processing center through the Internet of Things network. Due to the extensiveness and sensitivity of data transmission, it is very important to ensure the security and reliability of data transmission. The Internet of Things data collection and processing method based on quantum encryption secure communication proposed in the present invention is applied in this scenario.

[0176] A large number of environmental sensors are deployed in various corners of the city. These sensors collect air quality data in real time and transmit and process data through the method of the present invention. First, each sensor node is equipped with a quantum random number generator to generate high entropy random numbers and convert them into initial quantum keys. These quantum keys are securely distributed to sensor nodes and central data processing hosts through a quantum key distribution system.

[0177] The sensor node uses the received quantum key to encrypt the collected data. The encryption process uses a multi-level encryption algorithm based on chaos theory to ensure that the data cannot be cracked or tampered with during transmission. The encrypted data is transmitted to the central data processing host through wireless communication technology. During the data transmission process, the improved link monitoring sensor monitors the security of the communication link in real time, and promptly detects and responds to any eavesdropping or tampering. After receiving the encrypted data, the central data processing host uses the negotiated quantum key to decrypt layer by layer to restore the original data. The decryption process combines quantum state error correction technology to automatically repair quantum state errors that may occur during the transmission process to ensure the accuracy and integrity of the data. Subsequently, the central data processing host analyzes and processes the decrypted data to monitor and improve the city's air quality in real time.

[0178] From January 2023 to June 2023, we deployed the IoT data acquisition and processing system of the present invention in a certain city. Specific test locations include multiple scenarios such as city centers, industrial areas, residential areas and parks. The following is a demonstration of specific data and beneficial effects: In early January 2023, a total of 50 environmental sensor nodes were deployed in the city center. Each node collects data once an hour, including temperature, humidity, pressure and PM2.5 concentration. The data is transmitted to the central data processing host through the method of the present invention. The test results show that during the 6-month monitoring process, the security and reliability of data transmission have been significantly improved. Through quantum encryption technology, all transmitted data has not been cracked or tampered with, and the link monitoring sensor has not found any eavesdropping behavior.

[0179] The security of data transmission is also guaranteed in the 30 sensor nodes deployed in the industrial area. The link monitoring sensor monitors the communication link in real time and detects and responds to two possible eavesdropping behaviors. By timely adjusting the communication channel and encryption parameters, data transmission is not affected in any way, and data accuracy and integrity are effectively guaranteed. In residential areas and parks, 40 and 20 sensor nodes are deployed respectively. The data show that based on the method of the present invention, the security and reliability of data transmission have achieved the expected results. Especially in the case of high data traffic, the system dynamically adjusts data allocation through an adaptive learning algorithm to ensure the efficient operation of the system.

[0180] In the city center, 50 sensor nodes transmitted data 1,200 times a day, and a total of 216,000 times in 6 months. No data leakage or tampering was found during data transmission, and the integrity and accuracy of the data reached 100%. In the industrial area, 30 sensor nodes transmitted data 720 times a day, and a total of 129,600 times in 6 months. Two eavesdropping behaviors were detected by link monitoring sensors. After detection, the communication channel and encryption parameters were adjusted in time, which did not affect data transmission. The integrity and accuracy of the data reached 99.98%. In residential areas and parks, 60 sensor nodes transmitted data 1,440 times a day, and a total of 259,200 times in 6 months. The system uses an adaptive learning algorithm to dynamically adjust data distribution according to the computing power of the node and the real-time data flow, and the integrity and accuracy of the data reaches 99.99%.

[0181] Table 1 Environmental sensor node data

[0182]

[0183] It can be seen from the above table that from January 2023 to June 2023, we deployed environmental sensor nodes in the city center, industrial area, residential area and park of a certain city, and applied the IoT data acquisition and processing method based on quantum encryption secure communication proposed in this invention. 50 sensor nodes were deployed in the city center, each node transmitted data 1200 times a day, and the total number of transmissions in 6 months was 216,000 times. During the transmission process, no eavesdropping was detected, the number of data leakage and tampering were both 0, and the integrity and accuracy of the data reached 100%. 30 sensor nodes were deployed in the industrial area, each node transmitted data 720 times a day, and the total number of transmissions in 6 months was 129,600 times. During the transmission process, 2 eavesdropping behaviors were detected, but they were discovered and responded to in time by the improved link monitoring sensor, without causing any data leakage or tampering, and the integrity and accuracy of the data reached 99.98% respectively. 60 sensor nodes were deployed in residential areas and parks, each node transmitted data 1440 times a day, and the total number of transmissions in 6 months was 259,200 times. During the transmission process, no eavesdropping was detected, the number of data leakage and tampering were both 0, and the integrity and accuracy of the data reached 99.99% respectively.

[0184] The present invention uses quantum encryption and link monitoring technology to ensure that all transmitted data is not cracked or tampered with, thereby ensuring the security and reliability of the data. In different environments, the data transmission of sensor nodes can remain efficient, and the system dynamically adjusts data allocation through an adaptive learning algorithm to ensure the efficient operation of the system. The system is designed to be modular, and different modules can be flexibly configured to meet specific needs, enhancing the applicability and flexibility of the system. Its application in the smart city environment significantly improves the security and reliability of IoT data transmission, ensures the accuracy and integrity of data during collection, transmission and processing, and helps to achieve efficient management and safe operation of smart cities.

[0185] In summary, the application of the present invention in the smart city environment fully demonstrates its significant advantages in data transmission security and reliability, solves key problems in the prior art, and has great application prospects and promotion value. Through quantum encryption technology, improved link monitoring sensors and quantum state error correction technology, the present invention provides an efficient, reliable and secure method for collecting and processing IoT data.

[0186] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for collecting and processing Internet of Things data based on quantum encryption secure communication, characterized in that: The steps include: S1. Deploy various sensors including temperature sensors, humidity sensors, pressure sensors and light intensity sensors in IoT devices to collect environmental data and related parameters in real time; S2. Generate quantum keys using a quantum random number generator, distribute quantum keys to IoT devices and data processing hosts through a quantum key distribution system, and use a quantum state super key negotiation mechanism to dynamically adjust the frequency and method of quantum key distribution to meet the needs of different environments and sensors. S3. The IoT device uses the received key to encrypt the collected data. In the data encryption process, a multi-layer encryption algorithm based on chaos theory is introduced to achieve multi-level encryption. S4. The encrypted data is transmitted to the data processing host through wireless communication technology. During the transmission process, the link monitoring sensor is used to monitor the security of the communication link to detect and respond to eavesdropping in a timely manner; S5. The data processing host receives the encrypted data and decrypts it using the quantum key to restore the original data. It combines quantum state error correction technology to automatically repair the quantum state errors generated during the transmission process. S6. The decrypted data is processed in a data processing module, including data analysis, mining and storage. The data processing module is equipped with a quantum computing accelerator; S7. In the process of data processing and transmission, a quantum state trust management mechanism is adopted to optimize the credibility of data transmission nodes through quantum state authentication and trust evaluation to prevent attacks from malicious nodes; S8, manage and process data in a distributed environment, using dynamic load balancing technology based on adaptive learning to dynamically adjust data allocation based on node computing power and real-time data traffic; The S5 specifically includes: S51, the data processing host receives the multi-level encrypted data D transmitted via wireless communication technology final ; S52, using the negotiated quantum key K neg Decrypt the received encrypted data layer by layer and restore the decrypted data D n-1 : in, Denotes the decryption function of the nth layer, D n-1 Decrypted data for layer n-1, K neg is the negotiated quantum key, g1 is the multi-level encryption parameter, α i is the decryption coefficient; S53. Continue to use the quantum key K neg Decrypted data D layer by layer n-1 Decrypt and recover the next layer of data D n-2 , until the initial decrypted data D1 is restored; S54, perform final decryption on the initially decrypted data D1 to restore the original data data: in, represents the decryption function of the initial encryption, D1 is the initial encrypted data, K neg is the negotiated quantum key, a, b, c and d are decryption parameters; S55. Combined with quantum state error correction technology, error correction is performed on the recovered original data to repair the quantum state errors generated during the transmission process: Among them, data is the original data recovered, data corrected is the data after error correction, β i and γ are error correction parameters, error i is the quantum state error; S56, the data after error correction corrected Perform verification and verify the data through the verification function V: Where V represents the verification function, δ i and ω i To verify the parameters.

2. The method for collecting and processing Internet of Things data based on quantum encryption secure communication according to claim 1 is characterized in that: The S2 specifically includes: S21, using a quantum random number generator to generate a high entropy random number R, and converting the high entropy random number into an initial quantum key K; S22. Distribute the quantum key K to the IoT device and the data processing host through a quantum key distribution system. The quantum key distribution system includes a transmitting end and a receiving end, which are respectively deployed on the IoT device and the data processing host; S23, adopt the quantum state super key negotiation mechanism, dynamically adjust the frequency and method of quantum key distribution, and the IoT device and data processing host generate their own initial key K 0,device and K 0,host , exchange the initial key through the quantum channel; S24. Based on the received initial key, the IoT device and the data processing host respectively generate the negotiated quantum key K neg ; S25. After the negotiation, the quantum key K neg Based on this, the quantum key distribution frequency f is dynamically adjusted. dis and distribution method dis , using the environment perception module and sensor data analysis module, update the quantum key distribution parameters in real time according to environmental changes and sensor status, and adjust the distribution strategy through adaptive algorithms to adapt to the needs of different environments and sensors: Among them, w i and v j are the weight coefficients of environmental parameters and sensor types, env i and sensor_j represent different environmental parameters and sensor types respectively; S26, the dynamically adjusted quantum key K dyn Distribute to IoT devices and data processing hosts to maintain an efficient and secure key distribution mechanism under different environmental and sensor conditions.

3. The method for collecting and processing Internet of Things data based on quantum encryption secure communication according to claim 1 is characterized in that: The S3 specifically includes: S31. The IoT device receives the dynamically adjusted quantum key K dyn After that, the collected data is encrypted; S32. Perform preliminary encryption on the data using a multi-layer encryption algorithm based on chaos theory to generate preliminary encrypted data D1: D1=C1(data,Kdyn)=sin(a·data+b·Kdyn)×cos(c·data+d·Kdyn); Among them, C1 represents the encryption function of chaos theory, data is the collected original data, K dyn is the dynamically adjusted quantum key, a, b, c and d are the parameters of chaotic encryption; S33, re-encrypting the initially encrypted data D1 to generate re-encrypted data D2; S34, performing multi-level encryption processing on the data D2 after the secondary encryption, and finally generating multi-level encrypted data D final : Dfinal = Cn(Dn-1, Kdyn); Among them, C n Denotes the nth level encryption function of chaos theory, D n-1 For the n-1th layer of encrypted data, K dyn is the dynamically adjusted quantum key, and n is the number of encryption layers; S35, the multi-level encrypted data D final It is stored in the secure storage module of the IoT device and transmitted to the data processing host through a secure communication channel.

4. The method for collecting and processing Internet of Things data based on quantum encryption secure communication according to claim 1 is characterized in that: The S4 specifically includes: S41, the multi-level encrypted data D final Transmit to the data processing host via wireless communication technology; S42, during the data transmission process, using a link monitoring sensor to monitor the communication link in real time, the link monitoring sensor including a detection module and an analysis module, and obtaining a communication link status parameter link_status in real time; S43, the link monitoring sensor detects and identifies potential eavesdropping and tampering behaviors by analyzing the acquired communication link status parameter link_status, and generates a security alarm S alert ; S44, upon detection of a security alarm S alert When eavesdropping is indicated, link monitoring sensors immediately trigger security response mechanisms, including reallocating communication channels and adjusting encryption parameters; S45, the data processing host receives the encrypted data D transmitted final After that, first verify the generated security alert S alert , confirm the security of data transmission, and if it is safe, perform subsequent data processing.

5. The method for collecting and processing Internet of Things data based on quantum encryption secure communication according to claim 1 is characterized in that: The S7 specifically includes: S71. In the process of data processing and transmission, the quantum state trust management mechanism is adopted to calculate the trust value T of each data transmission node through the quantum state authentication and trust evaluation module. node ,The trust value is calculated comprehensively based on quantum state parameters and historical behavior data to evaluate the reliability of each node; S72, update the trust value of each node in real time through the trust evaluation module, and generate a node trust evaluation report R trust , the report combines the communication link status parameters to dynamically reflect the current credibility of the node: Among them, τ represents the trust evaluation function, link_status is the communication link status parameter, and λ is the evaluation parameter; S73. According to the trust assessment report R trust ,Dynamically adjust the data transmission path, select nodes with high trust values ​​for data transmission, and the path selection process is based on the principle of maximizing the trust value; S74. During the data transmission process, the trust management mechanism continuously monitors the trust value changes of the nodes, promptly discovers and isolates untrustworthy nodes, and updates the node list L nodes ; S75. Perform trust verification on the data transmitted through the trust management mechanism. The trust verification process analyzes the error-corrected data to ensure that it has not been tampered with or interfered with.

6. The method for collecting and processing Internet of Things data based on quantum encryption secure communication according to claim 1 is characterized in that: The S8 specifically includes: S81. Deploy multiple data processing nodes in a distributed environment. Each node has different computing power and processing performance. The node set is defined as N = {n1, n2, ..., n k }, where k is the number of nodes; S82. Collect the computing capacity parameter C of each node i and real-time data flow parameter F i , define the comprehensive capability parameter of the node as P i ; S83, using adaptive learning algorithm to calculate the comprehensive capability parameter P of each node i Perform dynamic evaluation and update to generate the initial load distribution strategy L initial : Among them, γ is the allocation coefficient, D is the total data volume, L initial The initial load distribution amount for each node; S84, the initial load distribution strategy L initial Applied to data processing nodes, according to the comprehensive capability parameter P of the node i Dynamically distribute data to various data processing nodes; S85. During the data processing process, monitor the processing status and data flow changes of each node in real time, and update the comprehensive capability parameter P of the node. i , and dynamically adjust the load distribution strategy L: Among them, γ′ is the new distribution coefficient, P i (t) is the comprehensive capacity parameter of the node at time t, D(t) is the total data volume at time t, and L is the load distribution after dynamic adjustment; S86, using an adaptive learning algorithm to provide feedback and optimize the adjustment results of the load distribution process through real-time monitoring and feedback adjustment; S87. In a distributed environment, the processed data is summarized and integrated to generate the final processing result R, which is then transmitted to the central management system through a secure communication channel for storage and analysis.

7. The method for collecting and processing Internet of Things data based on quantum encryption secure communication according to claim 1 is applied to an Internet of Things data collection and processing host based on quantum encryption secure communication, characterized in that: The host includes the following components: Quantum random number generator: used to generate high-entropy random numbers and convert random numbers into initial quantum keys; Quantum key distribution system: including the transmitter and receiver, which are deployed on IoT devices and data processing hosts respectively, and distribute quantum keys to IoT devices and data processing hosts through quantum channels; Quantum state super key negotiation mechanism: used to dynamically adjust the frequency and method of quantum key distribution. The IoT device and the data processing host generate initial keys respectively, and exchange the initial keys through the quantum channel to generate the negotiated quantum key. Data encryption and decryption module: IoT devices use dynamically adjusted quantum keys to perform multi-level encryption on the collected data. After receiving the encrypted data, the data processing host uses the negotiated quantum keys to decrypt layer by layer and restore the original data. Wireless communication module: used to transmit encrypted data through wireless communication technology, monitor the security of the communication link during the transmission process, and promptly detect and respond to eavesdropping; Quantum state error correction and verification module: used to perform error correction on the recovered original data, repair the quantum state errors generated during the transmission process, and verify the corrected data; Trust management and load balancing module: Calculate the trust value of each data transmission node through quantum state authentication and trust evaluation, generate node trust evaluation reports, dynamically adjust the data transmission path, select nodes with high trust values ​​for data transmission, monitor the changes in node trust values, promptly discover and isolate untrusted nodes, manage and process data, use adaptive learning algorithms to dynamically adjust data distribution according to the computing power of nodes and real-time data traffic, and generate load distribution strategies; Data aggregation and integration module: used to aggregate and integrate processed data, generate final processing results, and transmit them to the central management system through a secure communication channel for storage and analysis.

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