An Internet of Things information service method and system based on artificial intelligence

Abstract

The present application relates to the field of Internet of Things and information technology, and specifically to an artificial intelligence-based Internet of Things information service method and system, the method comprising: collecting monitoring data from the Internet of Things perception layer; encrypting the monitoring data as plaintext data before transmitting it to the Internet of Things application layer, adaptively adjusting the initial vectors for different rounds of plaintext data encryption and decryption by analyzing the characteristics of the plaintext data; encrypting and transmitting each round of plaintext data in the Internet of Things based on the initial vectors for each round of plaintext data encryption and decryption; and during the encrypted transmission process, performing linear regression analysis based on the similarity between any round of key stream and all previous historical rounds of key streams, and re-determining whether the encrypting party updated the original randomly generated initial vector before each round of encryption and decryption. The present application aims to ensure the security and efficiency of Internet of Things information services and improve the quality of Internet of Things information services.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things and information technology, and specifically to an Internet of Things information service method and system based on artificial intelligence. Background Art

[0002] With the rapid development of information technology, the Internet of Things (IoT) has become a key force driving global digital transformation. By connecting physical devices, sensors, and systems to the internet, the IoT makes data collection, transmission, and processing more convenient and efficient. From smart homes to smart cities, from industrial automation to agricultural monitoring, IoT applications span nearly every industry sector. Consequently, the IoT contains a vast amount of private information. Ensuring the security of private data in the IoT has become a top priority for IoT information services today. In this context, cryptographic algorithms have become an effective measure for protecting the confidentiality and integrity of IoT device data, as well as for access control and authentication.

[0003] Stream ciphers have promising application prospects due to their high security. For example, the Zu Chongzhi (ZUC) cipher, a commercially available stream cipher, is well-suited for use in IoT systems. However, as a stream cipher, the ZUC cipher relies on a one-time pad, and as a symmetric cipher, encryption and decryption require frequent key exchange and updates. This places a significant burden on resource-limited IoT systems, impacting the quality of IoT information services.

[0004] The traditional method is to use a different initialization vector for each encryption and decryption to avoid key updates. Although this reduces the burden of key updates and management and does not require complex management of initialization vectors, the initialization vectors need to be transmitted through a secure channel each time, which is also a heavy burden on IoT systems. Summary of the Invention

[0005] In order to solve the above technical problems, this application provides an Internet of Things information service method and system based on artificial intelligence. The technical solutions adopted are as follows:

[0006] In a first aspect, an embodiment of the present application provides an Internet of Things information service method based on artificial intelligence, the method comprising the following steps:

[0007] Step 1: Collect monitoring data from the IoT perception layer;

[0008] Step 2: Before the monitoring data is transmitted to the IoT application layer, the monitoring data is encrypted as plaintext data. By analyzing the characteristics of the plaintext data, the initial vectors for encryption and decryption of the plaintext data in different rounds are adaptively adjusted. Specifically:

[0009] S1, calculating the plaintext similarity coefficient of the current plaintext data based on the number of character overlaps and the similarity of character sequences between the current plaintext data and all historical plaintext data;

[0010] S2, determining the number of bits to be cyclically shifted for the original initial vector in the next round based on the plaintext similarity coefficient of the current plaintext data; performing an XOR operation on the vector after the next round of cyclic shift adjustment and the data of the first preset number of bits in the ciphertext data obtained in the current round to obtain the initial vector for the next round of plaintext data encryption and decryption;

[0011] Step 3: Encrypt and transmit each round of plaintext data in the Internet of Things based on the initial vector used in each round of plaintext data encryption and decryption. During the encrypted transmission process, perform linear regression analysis based on the similarity between any round of key stream and all previous historical rounds of key streams to re-determine whether the encrypting party updates the original randomly generated initial vector before each round of encryption and decryption.

[0012] Preferably, the character overlap quantity feature is determined by an average level of the distance between the current plaintext data and a set of all types of data sequences in each historical plaintext data; the data sequence is composed of various types of monitoring data.

[0013] Preferably, the character sequence similarity feature is determined by an average level of differences between the current plaintext data and all types of data sequences in each historical plaintext data.

[0014] Preferably, the method for calculating the plaintext similarity coefficient of the current plaintext data is:

[0015] Calculate the ratio of the number of character overlaps and the similarity of character order between the current plaintext data and each historical plaintext data;

[0016] The average level of the ratio between the current plaintext data and all the historical plaintext data is used as an exponential function with a natural constant as the base; and the calculation result of the exponential function is used as the plaintext similarity coefficient of the current plaintext data.

[0017] Preferably, the method for determining the number of bits for performing cyclic shift adjustment on the initial vector randomly generated by the encryption party for the first time in the next round is:

[0018] Calculating a difference between a plaintext similarity coefficient of the current plaintext data and the number 1 as a first difference, and calculating a difference between the natural constant and the number 1 as a second difference;

[0019] Calculate the ratio of the first difference to the second difference; and round down the product of the ratio and the number of bits of the original initial vector as the number of bits for performing cyclic shift adjustment on the original initial vector in the next round.

[0020] Preferably, the original initialization vector is an initialization vector randomly generated by the encryption party for the first time.

[0021] Preferably, the cyclic shift is a left cyclic shift.

[0022] Preferably, in step 3, the method for re-determining whether to update the original initialization vector randomly generated by the encryption party for the first time is:

[0023] Obtain the coefficient of determination for any round obtained through linear regression analysis in step 3;

[0024] Use the coefficients of determination of all rounds to predict the coefficient of determination of the next round;

[0025] When the determination coefficient of the next round is greater than the preset coefficient threshold, the encryption party randomly generates a new initial vector.

[0026] Preferably, after the encryption party updates the original initial vector randomly generated for the first time, the updated initial vector is resent to the decryption party.

[0027] In a second aspect, an embodiment of the present application also provides an artificial intelligence-based Internet of Things information service system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of any one of the above-mentioned artificial intelligence-based Internet of Things information service methods are implemented.

[0028] This application has at least the following beneficial effects:

[0029] This application first calculates a plaintext similarity coefficient based on the similarity between plaintext data to measure the degree of similarity between the plaintext data. This metric, as publicly known information accessible to both the encryption and decryption parties, facilitates subsequent, non-interactive initialization vector (IV) adjustments. The plaintext similarity coefficient and the current round's ciphertext are then used to adjust the IV for each encryption round. The degree of IV adjustment is controlled based on the plaintext similarity, helping to ensure the unpredictability of the V, thereby improving the security of IoT system information services. Finally, linear regression is used to analyze the regularity of the keystream to determine whether the original randomly generated V needs to be updated, ensuring the unpredictability of both the V and the keystream. This approach, while ensuring the security of IoT information transmission, reduces the overhead of transmitting the V in each encryption round in the ZUC encryption algorithm, helping to improve the operational efficiency of IoT systems and making the ZUC algorithm more suitable for resource-limited IoT systems. Furthermore, the algorithm can determine in real time whether the keystream remains unpredictable and promptly update the original randomly generated V, thereby ensuring the security and efficiency of IoT information services and improving the quality of IoT information services. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0031] Figure 1 A flowchart of an artificial intelligence-based IoT information service method provided in this application;

[0032] Figure 2 This is a flowchart of the adaptive adjustment process of the initialization vector during different rounds of plaintext data encryption provided by this application;

[0033] Figure 3 The encryption and decryption flow chart for each round provided for this application;

[0034] Figure 4 This is a block diagram of the encryption principle of the ZUC encryption algorithm provided in this application. DETAILED DESCRIPTION

[0035] To further illustrate the technical means and effects adopted by this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effects of an artificial intelligence-based Internet of Things information service method and system proposed in this application. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable form.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0037] The following describes in detail a specific solution of an Internet of Things information service method and system based on artificial intelligence provided by this application with reference to the accompanying drawings.

[0038] An embodiment of the present application provides an artificial intelligence-based Internet of Things information service method and system.

[0039] Specifically, the following IoT information service method based on artificial intelligence is provided. Figure 1 , the method comprises the following steps:

[0040] Step 1: Collect monitoring data from the IoT perception layer.

[0041] The IoT architecture primarily consists of the perception layer, the network layer, and the application layer. The perception layer is responsible for connecting with the physical world and collecting information from it. The network layer transmits this information through various private networks, the internet, and wired or wireless communication networks. The application layer serves as the interface between the IoT and users, processing and applying data collected by the perception layer to achieve the intelligence of the entire IoT system. During this process, the network layer is most vulnerable to third-party attacks, which can lead to the leakage of private data.

[0042] The perception layer of the Internet of Things is composed of various sensors and perception terminals, so the corresponding monitoring data is obtained through the various sensors and perception terminals in the perception layer, and the data collected by each sensor and perception terminal are respectively organized into corresponding data sequences in chronological order.

[0043] Step 2: Before the monitoring data is transmitted to the IoT application layer, the monitoring data is encrypted as plaintext data. By analyzing the characteristics of the plaintext data, the initial vectors for encryption and decryption of the plaintext data in different rounds are adaptively adjusted.

[0044] After the perception layer acquires monitoring data from the physical world, it needs to be transmitted to the application layer via the internet for use. To ensure data privacy during this process, the perception layer often encrypts the data after acquiring it and transmits the encrypted data across the network.

[0045] Stream ciphers (such as ZUC ciphers) have good application prospects due to their high security. However, stream ciphers are symmetric ciphers, and their security lies in one-time-one-pad. Therefore, they require frequent key changes and exchanges, which increases the complexity of key management. IoT systems have very limited resources, making it difficult to bear such frequent key updates and complex key management.

[0046] To avoid this situation, a different initialization vector is usually used each time to increase the security of the IoT system. However, since the initialization vector is needed for both encryption and decryption, it also needs to be securely transmitted, which increases the burden on the network system and affects the quality of IoT information services. At the same time, frequent initialization vector transmission interactions also increase the risk of being obtained by the adversary.

[0047] In this application, the process flow chart of adaptive adjustment of initial vectors for different rounds of plaintext data encryption is shown in the attached figure. Figure 2 As shown, specifically:

[0048] S1, calculating the plaintext similarity coefficient of the current plaintext data based on the number of character overlaps and the similarity of character sequences between the current plaintext data and all historical plaintext data;

[0049] Existing cryptographic attack methods can be roughly categorized into four types: ciphertext-only attacks, chosen-ciphertext attacks, chosen-plaintext attacks, and known-plaintext attacks. All of these methods attempt to infer the key based on the similarity between the encrypted plaintext and the ciphertext. In other words, the higher the similarity between the plaintexts, the higher the probability of the adversary's successful inference, and the greater the likelihood of a successful attack within polynomial time. To avoid this, the ZUC encryption algorithm often uses a different initialization vector (IV) to generate the keystream for each encryption, thereby reducing the likelihood of a successful attack. However, this inevitably requires the use of a secure channel to send the initialization vector (IV), which increases the burden on IoT systems.

[0050] In order to reduce the burden of secure transmission of the initial vector IV, both the encryption and decryption parties need to be able to adjust the initial vector IV for encryption and decryption by themselves. The greater the change in the initial vector IV, the greater the unpredictability, the less likely it is to infer the key through brute force or statistical attacks (such as known plaintext attacks), and the better the confidentiality.

[0051] Correspondingly, when the plaintext data of different encryption operations are very similar in content, the ciphertext may also show similar patterns. Especially when the initial vector IV and the key stream do not change sufficiently, the adversary will attack based on this pattern, and the possibility of successful attack in polynomial time will be greater. Therefore, the similarity of the plaintext data is positively correlated with the complexity of the change of the initial vector IV. The adjustment complexity of the initial vector IV can be adaptively set according to the similarity of the plaintext data, so that the randomness of the initial vector IV is increased when the plaintext has high similarity, reducing the possibility of attackers cracking the key through comparative analysis.

[0052] When conducting a cryptographic attack, the higher the frequency of identical characters in the plaintext, the more advantageous it is for the adversary. Similarly, the closer the order of characters in the plaintext, the easier it is for the adversary to infer the password, making the attack more likely to succeed. Therefore, plaintext similarity is primarily reflected in the frequency of identical plaintext characters and the similarity of their order. The similarity of plaintext character frequency can be measured by the number of overlapping characters in the plaintext data, while the similarity of character order is the degree of similarity between different plaintext sequences.

[0053] For each type of data sequence in each plaintext data, a set of each type of data is obtained. Based on the above analysis, the plaintext similarity coefficient of the current plaintext data is calculated to measure the similarity of the plaintext data.

[0054] Where A is the plaintext similarity coefficient of the current plaintext data, N is the number of historical plaintext data, and J n is the number of characters that overlap between the current plaintext data and the nth historical plaintext data, En It is the character sequence similarity feature between the current plaintext data and the nth historical plaintext data. The exponential function is used to expand the plaintext similarity coefficient, thereby enhancing the distinction between different similarities.

[0055] As an implementation method of this embodiment, Where I is the number of different types of data collected by the IoT perception layer, Jc ni It is the Jaccard distance between the current plaintext data and the set of data sequences of the i-th type in the n-th historical plaintext data; In the formula, ed ni is the edit distance between the current plaintext data and the data sequence of type i in the nth historical plaintext data. Jaccard distance and edit distance can be used to measure the distance between sets and the difference between sequences, and are both well-known technologies and will not be described in detail.

[0056] It can be understood that when the similarity between the plaintext data and the historical plaintext data is high, there are more repeated characters and the character sequence of the plaintext data is close, that is, J n Larger and E n is smaller, so the plaintext similarity coefficient A is larger; conversely, when the similarity between the plaintext data and the historical plaintext data is smaller, the plaintext similarity coefficient A is also smaller.

[0057] S2, based on the plaintext similarity coefficient of the current plaintext data, determines the number of bits to be cyclically shifted for the original initial vector in the next round; performs an XOR operation on the vector after the next round of cyclic shift adjustment and the data of the first preset number of bits in the ciphertext data obtained in the current round to obtain the initial vector for the next round of plaintext data encryption and decryption.

[0058] When using the ZUC encryption algorithm to ensure the secure transmission of IoT data, during the initial encryption and transmission process, since the encryption and decryption parties do not interact or share any content, and the ZUC algorithm is a symmetric encryption algorithm, both parties must transmit the key K and the initial vector IV0 through a secure channel. In order to reduce the burden of secure transmission of the initial vector IV during subsequent encryption and decryption processes, the initial vector IV needs to be adjusted based on information known to both parties. In order to ensure the unpredictability of the initial vector IV, this adjustment cannot be a fixed rule negotiated in advance and still needs to have a high degree of randomness and difficulty in predicting. Therefore, this application adjusts the initial vector IV of the current plaintext data during the encryption process using the ZUC algorithm based on the plaintext similarity coefficient of the current plaintext data. The plaintext similarity coefficient is information known to both parties.

[0059] First, during the first round of encrypted transmission between the encryption and decryption parties, both parties use the key K and the initial vector IV0 to generate a key stream for encryption and decryption. Second, during the second round of encrypted transmission between the two parties, since the decryption party only has one plaintext data, the plaintext similarity coefficient is not known to both parties. Therefore, the key K and the initial vector IV0 must still be used to generate a key stream for encryption and decryption.

[0060] In the third and subsequent rounds of encryption, both the encryptor and decryptor can obtain the plaintext similarity coefficient, so the plaintext similarity coefficient can be used to adjust the initial vector IV. Taking the third round as an example, the decryptor now knows the plaintext of the first two rounds, so the plaintext similarity coefficient between the plaintexts of the first two rounds is publicly known information to both the encryptor and decryptor. Since the initial vector IV of the ZUC cipher is 128 bits (bit), and the plaintext similarity coefficient has a value range of [1, e], in order to improve the unpredictability of the initial vector IV, the value range of the plaintext similarity coefficient is transformed to [0, 127]. Then, the initial vector IV0 is cyclically shifted according to the transformed plaintext similarity coefficient. At the same time, since cyclic shift can only perform integer bit shifts, a rounding operation is required. The shift number of the cyclic shift is calculated as follows:

[0061] Where B is the shift number of the initial vector cyclic shift in the third round, A2 is the plaintext similarity coefficient of the second round relative to the historical round (i.e., the first round), and e is a natural constant. is a floor function. A2-1 is recorded as the first difference, and e-1 is recorded as the second difference. It can be understood that when the similarity between the plaintext data obtained by the decryption party is higher, that is, when A2 is larger, more complex and larger transformations of the initial variables are required to enhance unpredictability. In this case, a larger shift number is required to perform a cyclic shift transformation on the initial vector IV0.

[0062] Since the shift number is rounded down, the shift number of the transformed plaintext similarity coefficient is the same when the integer bits are the same. This situation will reduce the security of the system. Therefore, the initial vector adjusted by the circular shift is XORed bit by bit with the first 128 bits of the ciphertext data obtained in the previous round to obtain the initial vector IV for the third round of encryption and decryption.

[0063] By analogy, the initial vector IV for each subsequent round can be obtained. The specific process is as follows: Figure 3 、 Figure 4 As shown. Among them, Figure 3 The encryption and decryption flow chart for each round is: Figure 4 This is the encryption principle block diagram of the ZUC encryption algorithm. Figure 3 In the figure, the solid-line box is the encryption process, and the dotted-line box is the decryption process.

[0064] Among them, the initial vectors of each round are:

[0065] IV1=IV2=IV0

[0066]

[0067] …

[0068]

[0069] Where, IV j is the initial vector for the jth round of encryption and decryption, IV0 is the initial vector randomly generated by the sender when the first encrypted transmission is made, and c j-1 is the ciphertext of the j-1th round, c j-1 (∧128) is the ciphertext c j-1 The first 128 bits of B j is the number of bits adjusted by the j-th round of cyclic shift, is an XOR operation, and <<< is a circular shift operation. In this embodiment, <<< is a left circular shift operation.

[0070] Step 3: Encrypt and transmit each round of plaintext data in the Internet of Things based on the initial vector used in each round of plaintext data encryption and decryption. During the encrypted transmission process, perform linear regression analysis based on the similarity between any round of key stream and all previous historical rounds of key streams to re-determine whether the encrypting party updates the original randomly generated initial vector before each round of encryption and decryption.

[0071] Following the procedure in step 2, the initial variable IV is gradually transformed and the keystream for the corresponding round is obtained. However, since all keystreams are generated based on the initial variable IV0 and the corresponding ciphertext, the probability of cracking the initial variable IV0 increases with increasing encryption rounds. To ensure the unpredictability of the initial variables and keystream, the initial variables need to be updated. Since an adversary infers the key and initial variables by analyzing the keystream and ciphertext, the probability of a successful polynomial-time attack increases as the keystreams become increasingly similar. With fewer encryption rounds, a successful keystream attack is virtually impossible. Therefore, updating the initial variables, i.e., performing keystream similarity and linear regression tests, is only necessary when the number of rounds exceeds α. In this embodiment, α is set to 10. The encryption server performs keystream similarity and linear regression tests, as well as updating the initial variables.

[0072] Taking the xth round as an example, the similarity between the key stream of the xth round and the key streams of each previous round is calculated. In this embodiment, the similarity is calculated using cosine similarity, with the corresponding round as the horizontal coordinate and the cosine similarity between rounds as the vertical coordinate. A series of coordinate points can be obtained. Cosine similarity is a well-known technology and will not be described in detail. For example, the cosine similarity between the key streams of the xth round and the x-1th round is y x-1 , then the corresponding coordinate point is (x-1,y x-1 ). Take all the coordinate points obtained in the xth round as input, use linear regression, and output the coefficient of determination R of the linear regression model. x .

[0073] In this manner, starting from the αth round, the determination coefficient of the linear regression model of each subsequent round is obtained in sequence. Linear regression is a well-known technique and will not be described in detail. All the determination coefficients are taken as input, and linear regression is used to sequentially obtain the linear regression model of the determination coefficients. The linear regression model of the determination coefficients is then used to predict the determination coefficient of the next round of encryption. When the predicted determination coefficient is greater than the coefficient threshold γ, the current initial vector is considered no longer secure. The encrypting party will initiate an initial vector update and send a new randomly generated initial vector to the decrypting party via a secure channel. In this embodiment, the coefficient threshold γ is 0.4.

[0074] It is understandable that when the linear regression model of the determination coefficient fits better, it means that there is a certain predictable pattern in the change of the determination coefficient, which also reflects that the corresponding key stream has a certain similarity and predictability, which means that the initial vector and key stream no longer meet the requirements of unpredictability, and there is a certain risk of data leakage, which means that the security and service quality of the Internet of Things information service are affected.

[0075] This secure transmission method for IoT information does not require the transmission of initial variables in each round of encryption. At the same time, the initial variables can be automatically adjusted based on the similarity between plaintext data. Since the length of the initial variables is limited, in order to improve security, the ciphertext and the adjusted initial variables are XORed to obtain the final initial variables. The security of the key stream is then predicted in combination with linear regression to determine whether the key stream is still unpredictable.

[0076] This approach reduces the overhead of initial variable transmission while also ensuring security. The transformation of the initial variables is linked to the historical plaintext, ensuring their unpredictability. Even if an adversary can decrypt one round through brute force, they will still be unable to decrypt subsequent rounds. Furthermore, while ensuring the security of IoT system data transmission, the ZUC encryption algorithm improves its operational efficiency within the IoT system, ensuring the security and efficiency of IoT information services and enhancing their quality.

[0077] Based on the same inventive concept as the above method, an embodiment of the present application also provides an artificial intelligence-based Internet of Things information service system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of an artificial intelligence-based Internet of Things information service method described in any one of the above methods are implemented.

[0078] The various embodiments in this application are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0079] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, the phrase "including a ..." defines an element, does not exclude the presence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0080] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not invented herein.

[0081] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. An Internet of Things information service method based on artificial intelligence, characterized in that: The method comprises the following steps: Step 1: Collect monitoring data from the IoT perception layer; Step 2: Before the monitoring data is transmitted to the IoT application layer, the monitoring data is encrypted as plaintext data. By analyzing the characteristics of the plaintext data, the initial vectors for encryption and decryption of the plaintext data in different rounds are adaptively adjusted. Specifically: S1, calculating the plaintext similarity coefficient of the current plaintext data based on the number of character overlaps and the similarity of character sequences between the current plaintext data and all historical plaintext data; S2, determining the number of bits to be cyclically shifted for the original initial vector in the next round based on the plaintext similarity coefficient of the current plaintext data; performing an XOR operation on the vector after the next round of cyclic shift adjustment and the data of the first preset number of bits in the ciphertext data obtained in the current round to obtain the initial vector for the next round of plaintext data encryption and decryption; Step 3: Encrypt and transmit each round of plaintext data in the Internet of Things based on the initial vector used in each round of plaintext data encryption and decryption. During the encrypted transmission process, perform linear regression analysis based on the similarity between any round of key stream and all previous historical rounds of key streams to re-determine whether the encrypting party updates the original randomly generated initial vector before each round of encryption and decryption.

2. The method for providing information services for the Internet of Things based on artificial intelligence according to claim 1, wherein: The character overlap quantity feature is determined by the average level of the distance between the current plaintext data and the set of all types of data sequences in each historical plaintext data; the data sequence is composed of data of the same type in the monitoring data.

3. The method for providing information services for the Internet of Things based on artificial intelligence according to claim 2, wherein: The character sequence similarity feature is determined by an average level of differences between the current plaintext data and all types of data sequences in each historical plaintext data.

4. The method for providing information services for the Internet of Things based on artificial intelligence according to claim 1, wherein: The calculation method of the plaintext similarity coefficient of the current plaintext data is: Calculate the ratio of the number of character overlaps and the similarity of character order between the current plaintext data and each historical plaintext data; The average level of the ratio between the current plaintext data and all the historical plaintext data is used as an exponential function with a natural constant as the base; and the calculation result of the exponential function is used as the plaintext similarity coefficient of the current plaintext data.

5. The method for providing information services for the Internet of Things based on artificial intelligence according to claim 4, wherein: The method for determining the number of bits of the next round of cyclic shift adjustment of the initial vector randomly generated by the encryption party for the first time is: Calculating a difference between a plaintext similarity coefficient of the current plaintext data and the number 1 as a first difference, and calculating a difference between the natural constant and the number 1 as a second difference; calculating a ratio of the first difference to the second difference; A value obtained by rounding down a product of the ratio and the number of bits of the original initial vector is used as the number of bits for performing cyclic shift adjustment on the original initial vector in the next round.

6. The method for providing information services for the Internet of Things based on artificial intelligence according to claim 5, wherein: The original initialization vector is the initialization vector randomly generated by the encryption party for the first time.

7. The method for providing information services for the Internet of Things based on artificial intelligence according to claim 1, wherein: The cyclic shift is a left cyclic shift.

8. The method for providing information services for the Internet of Things based on artificial intelligence according to claim 1, wherein: In step 3, the method for re-determining whether to update the original initialization vector randomly generated by the encryption party for the first time is: Obtain the coefficient of determination for any round obtained through linear regression analysis in step 3; Use the coefficients of determination of all rounds to predict the coefficient of determination of the next round; When the determination coefficient of the next round is greater than the preset coefficient threshold, the encryption party randomly generates a new initial vector.

9. The method for providing information services for the Internet of Things based on artificial intelligence according to claim 8, wherein: After the encryption party updates the original initialization vector randomly generated for the first time, the updated initialization vector is resent to the decryption party.

10. An artificial intelligence-based Internet of Things information service system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the artificial intelligence-based Internet of Things information service method as described in any one of claims 1 to 9 are implemented.

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