A power acquisition terminal device information security protection method and system

Abstract

The application relates to the technical field of power collection terminal device information security protection, and discloses a power collection terminal device information security protection method and system. The method generates a unique identifier through the hardware characteristics of a collection device to prevent forgery and tampering. High-dimensional transformation improves the feature complexity and enhances the anti-reverse ability. Dynamic disturbance factors make the device identifier change over time to prevent replay attacks. Multi-factor key generation combines hardware characteristics, environmental parameters and time sequence factors to improve the security of the key and avoid cracking. The hierarchical challenge response mechanism enhances security through multiple levels of authentication to prevent attackers from easily bypassing verification. High-dimensional abnormal behavior detection can dynamically identify potential security threats. The multi-stage key update mechanism ensures that the device is kept encrypted for a long time, avoiding the risks caused by key leakage. The whole system works cooperatively to effectively improve the security of device authentication, data encryption and communication.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power collection terminal device information security protection, in particular to a power collection terminal device information security protection method. BACKGROUND

[0002] With the development of smart grid and Internet of Things technology, power collection terminal devices play an increasingly important role in power monitoring and management. Power collection terminal devices are usually used to monitor real-time power data, including current, voltage, power factor and other parameters, and transmit data to the central system through wireless communication. These terminal devices provide basic data support for the optimization management, fault detection, load prediction and other aspects of the power system. However, due to the openness and network interconnection characteristics of power collection terminal devices, these devices face serious security threats, including data tampering, identity forgery, information leakage, etc. Therefore, how to protect the security of power collection terminal devices while ensuring data accuracy has become a problem to be solved.

[0003] In the actual application of power collection terminal devices, information security problems are particularly prominent. Existing technologies usually use traditional encryption and identity verification mechanisms to protect the security of device communication, but as attackers' technical means become increasingly advanced, the protection capabilities of these traditional mechanisms gradually appear insufficient. For example, although the common identity verification method based on cryptography can effectively prevent man-in-the-middle attacks and replay attacks, with the improvement of computing power, password cracking attacks gradually become a threat, leading to increased risks of device information leakage and tampering. In addition, existing technologies generally ignore the security of the device itself and behavior anomaly detection, and attackers can bypass existing security protection mechanisms by forging device identity or tampering with device behavior data.

[0004] Therefore, in order to improve the security of power collection terminal devices, the present application proposes a power collection terminal device information security protection method and system, which proposes new identity authentication and abnormal behavior detection methods to adapt to the characteristics of long-term operation and dynamic behavior change of devices. These methods should have higher security, dynamic adaptability and computing efficiency, which can effectively cope with current security threats and ensure the authenticity and reliability of device data. SUMMARY

[0005] The present application provides a power collection terminal device information security protection method and system, which solves the problems mentioned in the background art.

[0006] The present application provides the following technical solutions: a power collection terminal device information security protection method, comprising:

[0007] S1, generating a dynamic unique identifier, comprising:

[0008] S11, multi-dimensional data acquisition of device hardware features;

[0009] Obtain device hardware feature parameters H={h1, h2, h3, h4}, wherein h1 is a device serial number, h2 is a MAC address, h3 is a CPU ID, and h4 is a storage device number;

[0010] wherein each h i is a numerical type, used to represent hardware features;

[0011] Convert the hardware features into binary representation and split them into bit-level representation:

[0012] B i ={b i1 ,b i2 ,……,b ij}, ;

[0013] wherein b ij is a binary bit of h i , j is the number of bits of h i , and represents rounding up;

[0014] S12, high-dimensional transformation of hardware features;

[0015] Define a transformation matrix T, which is a random n x m matrix;

[0016] ;

[0017] wherein t nm is an element of the matrix T, representing the mapping relationship of the hardware features;

[0018] Use matrix multiplication to map the hardware feature matrix M H to a new space through the transformation matrix, to obtain a new high-dimensional feature matrix:

[0019] ;

[0020] wherein is the transpose of the hardware feature matrix, and M T is the feature matrix after high-dimensional transformation;

[0021] S13, generate dynamic disturbance;

[0022] Generate a dynamic disturbance factor , whose value includes a time factor and a noise factor:

[0023] ;

[0024] wherein, is the frequency of the disturbance, t is the current timestamp, is random noise, conforming to a Gaussian distribution ;

[0025] The disturbance factor is added to the mapping matrix to generate a hardware feature matrix containing disturbances:

[0026] ;

[0027] S14, generate a final dynamic unique identifier;

[0028] A nonlinear activation function is used to generate the final unique identifier UID t ;

[0029] ;

[0030] wherein, f is a nonlinear activation function that maps the input to a specific range; is a high-dimensional feature matrix the value of the ith row and jth column; is an exponential decay factor; n is the total number of rows of the high-dimensional feature matrix; m is the number of columns of the high-dimensional feature matrix;

[0031] S2, multi-factor key generation mechanism;

[0032] S3, hierarchical challenge-response mechanism;

[0033] S4, high-dimensional anomaly behavior detection;

[0034] S5, multi-stage key update mechanism.

[0035] Optionally, the multi-factor key generation mechanism specifically includes:

[0036] S21, generate a seed;

[0037] A key seed is generated by a hash function and a unique identifier UID t :

[0038] ;

[0039] S22, introduce environmental parameter disturbance;

[0040] Use sensors to obtain environmental parameters;

[0041] The sensors include temperature sensors, humidity sensors, and geographic position sensors;

[0042] The environmental parameters include temperature, humidity, and geographic position;

[0043] Let environmental parameters E t ={e1,e2,e3}, where e1 represents temperature, e2 represents humidity, and e3 represents geographical location;

[0044] Calculate disturbance coefficient D t The impact of environmental parameters on the key:

[0045] ;

[0046] where, is the norm of the environmental parameter vector, representing the total amount of environmental parameters, is a time-dependent modulation frequency, and t is the timestamp, is the i-th element in E t ;

[0047] S23, generate the final key;

[0048] Combine the key seed K seed and the environmental disturbance D t Generate the final key:

[0049] ;

[0050] where, ⊕ represents the bitwise XOR operation, exp(-e i ) is the exponential decay of environmental parameters, indicating that the degree of influence of environmental factors on the key decreases over time, is the i-th element in E t .

[0051] Optionally, the hierarchical challenge-response mechanism specifically includes:

[0052] S31, generate a hierarchical challenge;

[0053] S32, response calculation;

[0054] Obtain the variables required for response calculation;

[0055] The variables include R t (i) and K final ;

[0056] The R t (i) ={r t (i)j} is the i-th layer challenge data, r t (i)j is the j-th component in the i-th layer challenge data; K finalA key generated by the device and the authentication server for encryption and verification process;

[0057] The response value is calculated using a response calculation formula, which is:

[0058] ;

[0059] Wherein, S t (i) is the i-th layer response value, indicating the response of the device according to the key and the challenge data; cos(j) is a weighting factor, indicating the contribution weight of different positions in the challenge data; is the j-th component of the i-th layer challenge data; is the key; is the total number of components of the i-th layer challenge data;

[0060] S33, response verification;

[0061] S34, enhancement of the hierarchical challenge verification process;

[0062] The dynamic adjustment formula is: ;

[0063] Wherein, , is a perturbation matrix, indicating the perturbation factor at time t; D t ' is the perturbation matrix dynamically adjusted according to the failure level i, used to generate the next challenge; α i is a dynamic adjustment factor, controlling the weight of perturbation in the challenge process;

[0064] Dynamic challenge generation formula: ;

[0065] Wherein, R t (i+1) is the i+1-th layer challenge data, which is generated based on the dynamically adjusted perturbation matrix D t ' to generate new challenge data; M H is the hardware feature matrix of the device; () is a hash operation.

[0066] Optionally, the generating hierarchical challenge specifically includes:

[0067] Obtaining variables required for hierarchical challenge;

[0068] The variables specifically include: M H , D t , i and t;

[0069] M H ={m ij} is the hardware feature matrix of the device, indicating the hardware information of the device, and each mij It is the j-th component of the i-th hardware feature;

[0070] D t ={d it} represents the environmental disturbance matrix, indicating the disturbance value at time stamp t, where each d it It is the i-th perturbation factor, which controls the difficulty of challenge generation;

[0071] i is the level index of the current challenge, indicating that the difficulty of the challenge increases;

[0072] t is a timestamp, representing the current time point, which controls the dynamic changes of the disturbance;

[0073] The challenge value for the challenge data is generated using the challenge generation formula, which is:

[0074] ;

[0075] The R t (i) Let be the challenge data for the i-th layer, representing the challenge value generated based on device characteristics and perturbations; Hash(x) represents performing a hash operation on the data x to generate a challenge value of fixed length; i·D t D represents the perturbation matrix. t The impact on the current level i.

[0076] Optionally, the response verification specifically includes:

[0077] S331. Calculate the expected response value;

[0078] ;

[0079] Among them, S expected (i) The expected response value calculated for the authentication server, representing the value based on the received challenge data R. t (i) and key K final The calculated response;

[0080] S332, Response Verification;

[0081] ;

[0082] Among them, S t (i) The response value calculated for the device; S expected (i) The expected response value calculated for the authentication server; This indicates the preset tolerance error threshold.

[0083] Optionally, the high-dimensional abnormal behavior detection specifically includes:

[0084] Collecting device behavior data, defining behavior data set as a matrix X :

[0085] ;

[0086] wherein, x mn is the nth feature of the mth behavior;

[0087] High-dimensional space mapping of behavior data, obtaining a new feature matrix Y :

[0088] ;

[0089] wherein, W is a mapping matrix, is an activation function, b is a bias term, X T is the transpose of the data matrix X;

[0090] Calculate the abnormality D(x i ) of each behavior data:

[0091] ;

[0092] wherein, is the result after projection of the behavior data, is the mean of the behavior data set, is the standard deviation of the behavior data set, D(x i ) represents the abnormality of the behavior data.

[0093] Optionally, the multi-stage key update mechanism specifically includes:

[0094] S51, update factor calculation;

[0095] Using dynamic unique identification UID t and disturbance d t to generate update factor U t :

[0096] ;

[0097] wherein, is the disturbance, UID t is the unique identification, U t is the update factor;

[0098] S52, stage-by-stage update;

[0099] In each stage, the update factor U t is used to update the key K t :

[0100] ;

[0101] Wherein, ⊕ represents the exclusive or operation, and represents the change of the key in each stage;

[0102] S53, synchronizing the final key;

[0103] At the end of the session, the keys K t and the update factor U t are integrated to generate the session key:

[0104] ;

[0105] Wherein, K session is the final session key, and T is the number of stages of key update.

[0106] A system for implementing the power collection terminal device information security protection method comprises:

[0107] A sensor module: temperature sensor, humidity sensor and geographic position sensor, wherein the temperature sensor is used for acquiring temperature data, the humidity sensor is used for acquiring humidity data, and the geographic position sensor is used for acquiring device geographic position data;

[0108] A session module: used for the session between the device and the authentication server;

[0109] A calculation module: used for executing data calculation.

[0110] The present application has the following beneficial effects:

[0111] By collecting the hardware feature parameters of the device, such as the device serial number, MAC address, CPU ID and storage device number, and converting them into binary representation, a unique hardware feature set is generated for each device. These hardware features are not easily forged or modified, and can effectively prevent attackers from bypassing system authentication by forging device identity. In addition, the multi-dimensional data collection of hardware features also provides a sufficient basis for subsequent dynamic unique identification generation, enhancing the reliability of identity recognition. Using a transformation matrix to perform high-dimensional transformation on the hardware features, the originally relatively simple hardware features can be mapped to a higher dimensional space. In this way, even if an attacker analyzes the original hardware features, it is difficult to deduce the real identity of the device from them. High-dimensional mapping increases the complexity of device identification, making tampering or forgery of hardware features more difficult, effectively enhancing the security of identity authentication. Dynamic disturbance factors, including time factors and noise factors, introduce time variation and random noise, so that the identifier of the device changes over time. The introduction of disturbance factors ensures that the unique identification of the device not only depends on the hardware features, but also dynamically changes with each authentication process. This dynamicity effectively prevents replay attacks or forgery attacks on identity identification, making each identity verification have a certain timeliness and unpredictability, greatly improving the security of the system. Finally, the dynamic unique identification UID is generated through a nonlinear activation function tThe combination of hardware features, disturbance factors and activation functions makes the identification of each device not only related to its hardware features, but also has the dynamics of time and noise. This mechanism makes the identification of the device more complex and unpredictable, further improving the resistance to malicious attacks, avoiding security problems such as replay attacks and identity forgery that are prone to occur in traditional methods using static identification. The multi-factor key generation mechanism generates a secure key by combining multiple security factors such as device hardware features, environmental parameters and timing factors, solving the risk of traditional single key authentication mechanism being vulnerable to brute force or key leakage. The multi-factor mechanism can effectively improve the randomness and unpredictability of key generation, enhancing the security of the key itself and ensuring more secure data encryption and identity verification in device communication. In the hierarchical challenge-response mechanism, multiple levels of security challenges are set to ensure that the device must pass through multiple levels of authentication at each interaction, thereby reducing the security risks caused by single point of failure. Each level of challenge and response has dynamically generated features, so even if the attacker obtains part of the response data, it is difficult to decrypt or forge the correct response, significantly improving the protection capability. High-dimensional abnormal behavior detection can detect potential security threats of devices in complex environments by analyzing the behavior characteristics of devices in multi-dimensional space. This method not only solves the limitations of traditional rule-based anomaly detection, but also dynamically adapts to changes in device behavior, and timely detects potential security threats such as abnormal behavior of devices after tampering or control. In addition, the behavior model based on high-dimensional data can effectively improve the accuracy of anomaly detection and reduce false positives and false negatives. The multi-stage key update mechanism ensures that long-running devices are always under the protection of encryption by regularly updating the key and combining device behavior characteristics. This mechanism can prevent attackers from capturing and analyzing old keys to crack device communication content, providing continuous security, especially in long-running power collection terminal devices, avoiding large-scale security risks caused by key leakage.

[0112] By hashing the UID t , the key seed of each device has uniqueness and unpredictability. Traditional key generation methods may have the risk of key duplication or predictability, but the introduction of the hash function avoids this situation, improving the security and uniqueness of the key from the source. This method also ensures that the key seed generated by each device is dynamically changing, increasing the system's resistance to attacks. Environmental parameters such as temperature, humidity and geographic location are collected by sensors, and a disturbance coefficient D tThe environment parameters change over time, so that the dynamic change of the environment is considered each time the key is generated, enhancing the unpredictability of the key. The introduction of environmental disturbance effectively prevents attackers from obtaining the key generation rule through static analysis or reverse engineering, thereby improving the anti-cracking ability of the key. The change of environmental factors makes the key have time-varying characteristics, so that even if the attacker obtains the key at a certain time, the subsequent key cannot be calculated. In generating the final key, the key seed and the environmental disturbance are combined, and the final key is generated through the bitwise XOR operation. The exponential decay of the environmental parameters reflects the gradual weakening of the influence of the environment on the key generation over time. This process ensures that the final key not only depends on the static characteristics of the device hardware, but also combines the dynamic changes of the external environment, making the key more complex, dynamic and unpredictable. The use of bitwise XOR operation further increases the security of key generation, even if a certain factor is known by the attacker, it is still difficult to calculate the complete information of the key.

[0113] In traditional identity authentication mechanisms, usually only rely on a single level of challenge response process, vulnerable to man-in-the-middle attacks or replay attacks. And through the S31 step of hierarchical challenge generation mechanism, each layer of challenge data is based on the device and authentication server generated key, and each layer of challenge data is different from the previous layer of data, this multi-level, multi-dimensional challenge makes it difficult for attackers to bypass the authentication process by simply stealing or replaying data. This hierarchical structure enhances the complexity and security of identity verification, preventing passive cracking of single challenge data. In response calculation, by combining the weighted factor of challenge data and the key, the response value of each layer is generated, so that the calculation of each layer response value not only depends on the identity information of the device, but also adds the weight of the challenge data. This design solves the problem of fixed and easily predictable response value in traditional response value, the calculation result of each response is closely related to the position and content of the challenge data, increases the variability and complexity of the authentication process, avoids the risk of being simply simulated and forged. In the response verification process, the authentication server verifies whether the response value is correct to determine whether the device is a legitimate device. This step ensures that each layer of challenge must match the expected response value of the device, greatly improving the reliability of the authentication process and preventing identity fraud or man-in-the-middle attacks. At the same time, only the correct device can generate the expected response value in each layer of challenge, thereby further strengthening the trust relationship between the device and the server. S34 step introduces a dynamic adjustment factor and adjusts the challenge through a perturbation matrix, enhancing the adaptability and anti-attack ability of the challenge response mechanism. In traditional mechanisms, once the authentication process fails at a certain level, it may cause the interruption of the entire authentication process. In this step, by dynamically adjusting and generating new challenge data, the difficulty of the challenge can be adjusted according to the failed level, making the next layer of challenge more difficult, thereby preventing attackers from continuing identity fraud through simple reverse or cracking means. This dynamic adjustment ensures that the challenge response mechanism can maintain high security when facing complex attacks.

[0114] The variables include a device hardware feature matrix, an environmental disturbance matrix, a challenge level index, and a timestamp. The device hardware feature matrix contains unique hardware information of the device, while the environmental disturbance matrix reflects the influence of the environment on challenge generation. Through the combination of these variables, the hardware features of the device and the dynamic changes of environmental factors can be combined to generate more complex and variable challenge data. This method avoids the problem of isolating the device hardware features and environmental factors in traditional authentication methods, enhances the real-time and adaptability of the challenge, and improves the system's ability to respond to complex attacks. The challenge generation formula generates challenge data for each level according to the level index and timestamp by combining the device hardware feature matrix and the environmental disturbance matrix. This generation method combines the influence of the disturbance matrix with the challenge level index, so that the computational complexity of the challenge gradually increases and the difficulty of the challenge gradually improves as the challenge level increases. The generation of challenge data not only considers the static characteristics of the device, but also introduces the dynamic changes of the environment, thereby increasing the complexity and unpredictability of the challenge, effectively preventing the risk of breaking the authentication mechanism through simple reverse analysis or replay attacks. As the level index increases, the influence of the disturbance matrix gradually increases, and the computational complexity of the challenge gradually increases as the level increases. This design solves the problem of being difficult to cope with advanced attacks in traditional challenge generation. As the level increases, the computational complexity of the challenge increases, which can resist stronger attackers and more complex attack methods. The complexity of the challenge process increases, so even if the attacker obtains the challenge data of a certain level, it cannot directly calculate the challenge data of the next level, effectively improving the security of the system.

[0115] In the traditional authentication mechanism, the calculation of response value is usually based on simple key and challenge data operation, which is vulnerable to man-in-the-middle attacks or tampering. In step S331, the authentication server calculates the expected response value based on the received challenge data and key, and compares it with the calculation result of the device side. This design ensures that the authentication server can calculate the expected response value through complex formula, effectively preventing simple counterfeit or tampered response data behavior. In this way, the verification process of the response value becomes more accurate and secure, enhancing the credibility of authentication. Through step S332, the fault tolerance problem in identity verification is solved. In this step, the authentication server compares the response value calculated by the device with the expected response value, and compares it with the preset tolerance error threshold, to verify the identity of the device. If the response value exceeds the preset tolerance error threshold, the authentication request will be considered invalid. This design effectively solves the problem of insufficient error tolerance in traditional verification, avoiding authentication failure due to minor errors. The setting of tolerance error threshold makes the authentication process not only accurate, but also able to maintain the stability of the system and the experience of users in the face of a certain range of calculation deviation, improving the fault tolerance ability of the system. By comparing the expected response value with the actual response value, the problem of response forgery and tampering is solved. The calculation of the expected response value depends on the unique key of the device and the received challenge data, while the response value calculated by the device is based on the same challenge data and the private key of the device. Through this comparison mechanism, even if the attacker tries to forge response data or intermediate tampering data, due to the lack of correct key and hardware information, it is impossible to generate a response consistent with the expected response value, effectively avoiding forgery and tampering attacks.

[0116] By collecting device behavior data, the behavior data set is defined as a matrix X step, solving the problem of lack of unified standard and multi-dimensional feature integration of behavior data. In this step, the behavior data is defined as matrix X, where x mnThe nth feature representing the mth behavior. This step effectively converts the behavior data of the device into a unified format and provides a complete view of the behavior data through the integration of multi-dimensional features. In this way, the behavior data is standardized, and the multi-dimensional features of each piece of behavior data are fully considered, so that the subsequent analysis can more comprehensively capture the multi-dimensional information of the device behavior, laying the foundation for the detection of abnormal behavior. By mapping the behavior data in a high-dimensional space, a new feature matrix Y is obtained, which solves the problem of information loss caused by low data dimensionality. The behavior data X is mapped in a high-dimensional space to obtain a new feature matrix Y, where W is the mapping matrix, b is the bias term, and the activation function is used to enhance the non-linear representation of the data. This step increases the dimensionality of the data and performs non-linear transformation on the data, so that the data can be better represented in a high-dimensional space, thereby retaining more useful information and avoiding the loss of feature information caused by low-dimensional mapping. Through mapping, the potential patterns of behavior data are more accurately captured, providing a more rich feature representation for subsequent abnormal behavior detection. By calculating the abnormality degree of each behavior data step, the problem of identifying the accuracy of abnormal behavior is solved. In this step, the abnormality degree D(xi) of each behavior data is calculated, where yi is the result of the behavior data projection, Y is the mean of the behavior data set, and σ is the standard deviation of the behavior data set. By calculating the abnormality degree, the system can quantify the deviation of each behavior data from the normal behavior, and if the deviation exceeds a certain threshold, it is judged as abnormal behavior. This step effectively solves the problem that traditional anomaly detection methods cannot accurately identify small deviations or complex abnormal patterns. Through high-dimensional space mapping and standard deviation calculation, the abnormality degree of behavior data can be accurately measured, improving the accuracy and reliability of anomaly detection. Through the step of judging whether the behavior is abnormal based on the abnormality degree, the problem of single detection strategy is solved. The high-dimensional abnormal behavior detection mechanism does not rely on a single threshold judgment, but considers the projection results and statistical characteristics of the data, such as mean and standard deviation, making the calculation of abnormality degree more detailed and accurate. This process can flexibly cope with complex and variable behavior patterns, avoiding the misjudgment or omission problems that may occur in traditional methods based on fixed thresholds. At the same time, the system can adapt to the behavior characteristics of different devices or users, thereby providing dynamic and intelligent anomaly detection capabilities.

[0117] Using a dynamic unique identifier UID t and a perturbation factor t to generate an update factor U t The update factor U tCombining the unique identification of the device and the dynamic disturbance ensures that each key update is unique and dynamic, avoiding the security risks that may be caused by the long-term use of static keys. The introduction of the dynamic factor makes the key update not a simple static operation, but a dynamic process based on the device state and environmental changes, thereby effectively preventing the key from being subject to risks such as replay attacks and prediction attacks. In each stage, the key K t is updated by performing a bitwise XOR operation with the update factor U t . This step avoids the problems of singularity and irreversibility of the key update process through multiple stage-by-stage updates, improving the security of the key. Each key update depends on the key of the previous stage and the dynamically generated update factor, making the key change more complex and increasing the difficulty of cracking by attackers. The stage-by-stage update not only increases the strength of the key, but also ensures that even if part of the key is leaked, the entire key process still has high security. At the end of the session, the keys of all stages and the update factors are combined to generate a session key. This step ensures the integrity and consistency of the key by combining the keys of all stages and the update factors. This mechanism solves the coordination problem in key management and avoids security vulnerabilities caused by inconsistent or untimely synchronization of keys in different stages. At the same time, the generated session key has strong encryption protection capability, ensuring the security of data transmission during the session. Traditional key update mechanisms often rely on a single update operation, while the multi-stage key update mechanism increases the complexity of key changes through multiple update steps, preventing the risk of brute force cracking or key leakage. By updating independently in each stage, the complexity and update frequency of the key are greatly improved, making it difficult for attackers to calculate the entire key chain even if a key in a certain stage is compromised, thereby greatly improving overall security. BRIEF DESCRIPTION OF DRAWINGS

[0118] Figure 1 The flowchart of the present application is shown. DETAILED DESCRIPTION

[0119] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0120] Embodiment, refer to Figure 1 A power collection terminal device information security protection method, comprising:

[0121] S1, generating a dynamic unique identifier, comprising:

[0122] S11, multi-dimensional data acquisition of device hardware features

[0123] Obtain device hardware feature parameters H={h1, h2, h3, h4}, wherein h1 is a device serial number, h2 is a MAC address, h3 is a CPU ID, and h4 is a storage device number;

[0124] wherein each h i is a numerical type, used to represent hardware features;

[0125] Convert the hardware features into binary representation and split them into bit-level representation:

[0126] B i ={b i1 ,b i2 ,……,b ij}, ;

[0127] wherein b ij is a binary bit of h i , j is the number of bits of h i , and represents rounding up;

[0128] S12, high-dimensional transformation of hardware features

[0129] Define a transformation matrix T, which is a random n×m matrix;

[0130] ;

[0131] wherein t nm is an element of the matrix T, representing the mapping relationship of the hardware features;

[0132] Use matrix multiplication to map the hardware feature matrix M H to a new space through the transformation matrix, to obtain a new high-dimensional feature matrix:

[0133] ;

[0134] wherein is the transpose of the hardware feature matrix, and M T is the feature matrix after high-dimensional transformation;

[0135] S13, generate dynamic disturbance

[0136] Generate a dynamic disturbance factor , whose value includes a time factor and a noise factor:

[0137] ;

[0138] wherein, is the frequency of the disturbance, t is the current timestamp, is random noise, conforming to a Gaussian distribution ;

[0139] The disturbance factor is added to the mapping matrix to generate a hardware feature matrix containing disturbances:

[0140] ;

[0141] S14, generate the final dynamic unique identification;

[0142] A nonlinear activation function is used to generate the final unique identification UID t ;

[0143] ;

[0144] where, f is a nonlinear activation function that maps the input to a specific range; is a high-dimensional feature matrix the value of the ith row and jth column; is an exponential decay factor; n is the total number of rows of the high-dimensional feature matrix; m is the number of columns of the high-dimensional feature matrix;

[0145] S2, multi-factor key generation mechanism;

[0146] S3, hierarchical challenge-response mechanism;

[0147] S4, high-dimensional anomaly behavior detection;

[0148] S5, multi-stage key update mechanism.

[0149] By collecting the hardware feature parameters of the device, such as the device serial number, MAC address, CPU ID and storage device number, and converting them into binary representation, a unique hardware feature set is generated for each device. These hardware features are not easily forged or modified, and can effectively prevent attackers from bypassing system authentication by forging device identity. In addition, the multi-dimensional data collection of hardware features also provides a sufficient basis for subsequent dynamic unique identification generation, enhancing the reliability of identity recognition. Using a transformation matrix to perform high-dimensional transformation on the hardware features, the originally relatively simple hardware features can be mapped to a higher dimensional space. In this way, even if an attacker analyzes the original hardware features, it is difficult to deduce the real identity of the device from them. High-dimensional mapping increases the complexity of device identification, making tampering or forgery of hardware features more difficult, effectively enhancing the security of identity authentication. Dynamic disturbance factors, including time factors and noise factors, introduce time variation and random noise, so that the identifier of the device changes over time. The introduction of disturbance factors ensures that the unique identification of the device not only depends on the hardware features, but also dynamically changes with each authentication process. This dynamicity effectively prevents replay attacks or forgery attacks on identity identification, making each identity verification have a certain timeliness and unpredictability, greatly improving the security of the system. Finally, the dynamic unique identification UID is generated through a nonlinear activation function tThe combination of hardware features, disturbance factors and activation functions makes the identification of each device not only related to its hardware features, but also has the dynamics of time and noise. This mechanism makes the identification of the device more complex and unpredictable, further improving the resistance to malicious attacks and avoiding security problems such as replay attacks and identity forgery that are vulnerable to traditional methods using static identification. The multi-factor key generation mechanism generates a secure key by combining multiple security factors such as device hardware features, environmental parameters and timing factors, solving the risk of traditional single key authentication mechanism being vulnerable to brute force or key leakage. The multi-factor mechanism can effectively improve the randomness and unpredictability of key generation, enhancing the security of the key itself and ensuring more secure data encryption and identity verification in device communication. In the hierarchical challenge-response mechanism, multiple levels of security challenges are set to ensure that the device must pass through multiple levels of authentication at each interaction, thereby reducing the security risks caused by single point of failure. Each level of challenge and response has dynamically generated features, so that even if the attacker obtains part of the response data, it is difficult to decrypt or forge the correct response, significantly improving the protection capability. High-dimensional abnormal behavior detection can detect potential security threats of devices in complex environments by analyzing the behavior characteristics of devices in multi-dimensional space. This method not only solves the limitations of traditional rule-based anomaly detection, but also dynamically adapts to changes in device behavior, and timely discovers potential security threats such as abnormal behavior of devices after tampering or control. In addition, the behavior model based on high-dimensional data can effectively improve the accuracy of anomaly detection and reduce false positives and false negatives. The multi-stage key update mechanism ensures that devices running for a long time are always under the protection of encryption by regularly updating the key and combining device behavior characteristics. This mechanism can prevent attackers from capturing and analyzing old keys to crack device communication content, providing continuous security, especially in long-running power collection terminal devices, avoiding large-scale security risks caused by key leakage.

[0150] The multi-factor key generation mechanism specifically includes:

[0151] S21, generating a seed;

[0152] Through a hash function and a unique identifier UID t Generate a key seed:

[0153] ;

[0154] S22, introduce environmental parameter disturbance;

[0155] Use sensors to obtain environmental parameters;

[0156] The sensors include temperature sensors, humidity sensors and geographic position sensors;

[0157] The environmental parameters include temperature, humidity, and geographical location.

[0158] The environmental parameters E t ={e1,e2,e3}, where e1 represents temperature, e2 represents humidity, and e3 represents geographical location.

[0159] The disturbance coefficient D t The influence of environmental parameters on the key:

[0160] ;

[0161] where, is the norm of the environmental parameter vector, representing the total amount of environmental parameters, is the time-dependent modulation frequency, and t is the time stamp, is the i-th element in E t .

[0162] S23, generating a final key;

[0163] Combining the key seed K seed and the environmental disturbance D t Generating a final key:

[0164] ;

[0165] where, ⊕ represents the bitwise XOR operation, exp(-e i ) is the exponential decay of the environmental parameter, indicating that the degree of influence of environmental factors on the key decreases over time, is the i-th element in E t .

[0166] Through the hash processing of UID t , the key seed of each device has uniqueness and unpredictability. Traditional key generation methods may have the risk of key duplication or predictability, but through the introduction of the hash function, this situation is avoided, improving the security and uniqueness of the key from the source. This method also ensures that the key seed generated by each device is dynamically changing, increasing the system's attack resistance. Through the sensor to collect environmental parameters such as temperature, humidity, and geographical location, and introduce the disturbance coefficient D tThe environmental parameters change over time, so that the dynamic change of the environment is considered each time the key is generated, enhancing the unpredictability of the key. The introduction of environmental disturbance effectively prevents attackers from obtaining the key generation rule through static analysis or reverse engineering, thereby improving the anti-cracking ability of the key. The change of environmental factors makes the key have time-varying characteristics, so that even if an attacker obtains the key at a certain time, the key at the subsequent time cannot be calculated. In generating the final key, the key seed and the environmental disturbance are combined, and the final key is generated through the bitwise XOR operation. The exponential decay of the environmental parameters reflects the gradual weakening of the influence of the environment on the key generation over time. This process ensures that the final key not only depends on the static characteristics of the device hardware, but also combines the dynamic changes of the external environment, making the key more complex, dynamic and unpredictable. The use of the bitwise XOR operation further increases the security of key generation, so that even if a certain factor is known by an attacker, it is still difficult to calculate the complete information of the key.

[0167] The hierarchical challenge-response mechanism specifically comprises:

[0168] S31, generating a hierarchical challenge;

[0169] S32, response calculation;

[0170] Obtaining variables required for response calculation;

[0171] The variables include R t (i) and K final ;

[0172] The R t (i) ={r t (i)j} is the i-th layer challenge data, r t (i)j is the j-th component in the i-th layer challenge data; K final is a key generated by the device and the authentication server together, used for encryption and verification process;

[0173] The response value is calculated using the response calculation formula, and the response calculation formula is:

[0174] ;

[0175] Wherein, S t (i) is the i-th layer response value, indicating the response of the device according to the key and the challenge data; cos(j) is a weighting factor, indicating the contribution weight of different positions in the challenge data; is the j-th component of the i-th layer challenge data; is the key; Total number of components of the challenge data for the i-th layer;

[0176] S33, response verification;

[0177] S34, enhancement of the hierarchical challenge verification process;

[0178] The dynamic adjustment formula is: ;

[0179] wherein, is a disturbance matrix, indicating a disturbance factor at time t; D t is a dynamically adjusted disturbance matrix according to the failure level i, used to generate the next challenge; a i is a dynamic adjustment factor, controlling the weight of the disturbance in the challenge process;

[0180] Dynamic challenge generation formula: ;

[0181] wherein, R t (i+1) is the i+1-th layer challenge data, generating new challenge data based on the dynamically adjusted disturbance matrix D t is a hardware feature matrix of the device; H is a hash operation.

[0182] ​In the traditional identity authentication mechanism, usually only rely on a single level of challenge response process, vulnerable to man-in-the-middle attacks or replay attacks. And through the S31 step of hierarchical challenge generation mechanism, each layer of challenge data is based on the device and authentication server generated key, and each layer of challenge data is different from the previous layer of data, this multi-level, multi-dimensional challenge makes it difficult for attackers to bypass the authentication process by simply stealing or replaying data. This hierarchical structure enhances the complexity and security of identity verification, preventing passive cracking of single challenge data. In response calculation, by combining the weighting factor of challenge data and the key, the response value of each layer is generated, so that the calculation of each layer response value not only depends on the identity information of the device, but also adds the weight of the challenge data. This design solves the problem of fixed and easily predictable response value in traditional response value, and the calculation result of each response is closely related to the position and content of the challenge data, which increases the variability and complexity of the authentication process, avoiding the risk of being simply simulated and forged. In the response verification process, the authentication server verifies whether the response value is correct to determine whether the device is a legitimate device. This step ensures that each layer of challenge must match the expected response value of the device, greatly improving the reliability of the authentication process and preventing identity fraud or man-in-the-middle attacks. At the same time, only the correct device can generate the expected response value in each layer of challenge, thereby further strengthening the trust relationship between the device and the server. S34 step introduces a dynamic adjustment factor and dynamically adjusts the challenge through a perturbation matrix, enhancing the adaptability and anti-attack ability of the challenge response mechanism. In the traditional mechanism, once the authentication process fails at a certain level, it may cause the interruption of the entire authentication process. In this step, by dynamically adjusting and generating new challenge data, the difficulty of the challenge can be adjusted according to the failed level, making the next layer of challenge more difficult, thereby preventing attackers from continuing identity fraud through simple reverse or cracking means. This dynamic adjustment ensures that the challenge response mechanism can maintain high security when facing complex attacks.

[0183] The hierarchical challenge is generated, specifically including:

[0184] Obtaining variables required for hierarchical challenge;

[0185] The variables specifically include: M H , D t , i and t;

[0186] The M H ={m ij} is a device hardware feature matrix, representing the hardware information of the device, each m ij is the jth component of the ith hardware feature;

[0187] is an environmental disturbance matrix, representing the disturbance value at time stamp t, each d it is the i-th disturbance factor, controlling the difficulty of challenge generation;

[0188] i is the level index of the current challenge, representing the increasing difficulty of the challenge, and the computational complexity of the challenge gradually increases with the increase of the number of layers;

[0189] t is the time stamp, representing the current time point, controlling the dynamic change of the disturbance;

[0190] The challenge value of the challenge data is generated using the challenge generation formula, which is:

[0191] ;

[0192] The R t (i) is the i-th layer challenge data, representing the challenge value generated based on the device characteristics and disturbance; Hash(x) represents the hash operation on the data x, generating a fixed length challenge value; i·D t represents the disturbance matrix D t has an impact on the current level i, and with the increase of i, the disturbance increases, generating more complex challenges.

[0193] The variables include a device hardware feature matrix, an environmental disturbance matrix, a challenge level index, and a timestamp. The device hardware feature matrix contains unique hardware information of the device, and the environmental disturbance matrix reflects the influence of the environment on challenge generation. Through the combination of these variables, the hardware features of the device and the dynamic changes of environmental factors can be combined to generate more complex and variable challenge data. This method avoids the problem of isolating the device hardware features and environmental factors in traditional authentication methods, enhances the real-time and adaptability of the challenge, and improves the ability of the system to respond to complex attacks. The challenge generation formula generates challenge data for each level according to the level index and the timestamp by combining the device hardware feature matrix and the environmental disturbance matrix. This generation method combines the influence of the disturbance matrix with the challenge level index, so that the computational complexity of the challenge gradually increases and the difficulty of the challenge gradually increases as the challenge level increases. The generation of challenge data not only considers the static features of the device, but also introduces the dynamic changes of the environment, thereby increasing the complexity and unpredictability of the challenge and effectively preventing the risk of breaking the authentication mechanism through simple reverse analysis or replay attacks. As the level index increases, the influence of the disturbance matrix gradually increases, and the computational complexity of the challenge gradually increases as the level increases. This design solves the problem of being difficult to respond to high-level attacks in traditional challenge generation. When the level increases, the computational complexity of the challenge increases, which can resist stronger attackers and more complex attack methods. The complexity of the challenge process increases, so even if the attacker obtains the challenge data of a certain level, it cannot directly calculate the challenge data of the next level, effectively improving the security of the system.

[0194] The response verification specifically includes:

[0195] S331, calculating an expected response value;

[0196] ;

[0197] Wherein, S expected (i) is the expected response value calculated by the authentication server, which represents the response calculated according to the received challenge data R t (i) and the key K final ;

[0198] S332, response verification;

[0199] ;

[0200] Wherein, S t (i) is the response value calculated by the device; S expected (i) is the expected response value calculated by the authentication server; represents a preset tolerance error threshold.

[0201] In the traditional authentication mechanism, the calculation of the response value is usually based on simple key and challenge data operation, which is vulnerable to man-in-the-middle attacks or tampering. In step S331, the authentication server calculates the expected response value based on the received challenge data and key, and compares it with the result calculated by the device. This design ensures that the authentication server can calculate the expected response value through complex formula, effectively preventing simple counterfeit or tampered response data. In this way, the verification process of the response value becomes more accurate and secure, enhancing the credibility of authentication. Through step S332, the fault tolerance problem in identity verification is solved. In this step, the authentication server verifies the identity of the device by comparing the response value calculated by the device with the expected response value, and comparing it with the preset tolerance error threshold. If the response value exceeds the preset tolerance error threshold, the authentication request will be considered invalid. This design effectively solves the problem of insufficient error tolerance in traditional verification, avoiding authentication failure due to minor errors. The setting of the tolerance error threshold makes the authentication process not only accurate, but also able to maintain the stability of the system and the experience of the user when facing a certain range of calculation deviations, improving the fault tolerance of the system. By comparing the expected response value with the actual response value, the problem of response forgery and tampering is solved. The calculation of the expected response value depends on the unique key of the device and the received challenge data, while the response value calculated by the device is based on the same challenge data and the private key of the device. Through this comparison mechanism, even if the attacker tries to forge the response data or the man-in-the-middle tampers the data, due to the lack of correct key and hardware information, it is impossible to generate a response consistent with the expected response value, thus effectively avoiding the attack of forgery and tampering.

[0202] The high-dimensional anomaly behavior detection specifically includes:

[0203] Collecting device behavior data, defining the behavior data set as a matrix X :

[0204] ;

[0205] Where x mn is the nth feature of the mth behavior;

[0206] Mapping the behavior data in a high-dimensional space to obtain a new feature matrix Y :

[0207] ;

[0208] Where W is the mapping matrix, is the activation function, b is the bias term, X T is the transpose of the data matrix X;

[0209] D(x i ) is the abnormal degree of each behavior data:

[0210] ;

[0211] wherein, is the result of the behavior data projection, is the mean of the behavior data set, is the standard deviation of the behavior data set, and D(x i ) represents the abnormal degree of the behavior data.

[0212] By collecting device behavior data, the behavior data set is defined as a matrix X step, which solves the problems of lack of unified standard and multi-dimensional feature integration of behavior data. In this step, the behavior data is defined as a matrix X, wherein x mnThe nth feature represents the mth behavior. This step effectively converts the behavior data of the device into a unified format, and provides a complete view of the behavior data through the integration of multi-dimensional features. In this way, the behavior data is standardized, and the multi-dimensional features of each piece of behavior data are fully considered, so that the subsequent analysis can more comprehensively capture the multi-dimensional information of the device behavior, laying the foundation for the detection of abnormal behavior. By mapping the behavior data in a high-dimensional space, a new feature matrix Y is obtained, which solves the problem of information loss caused by low-dimensional data. The behavior data X is mapped in a high-dimensional space to obtain a new feature matrix Y, where W is the mapping matrix, b is the bias term, and the activation function is used to enhance the non-linear representation of the data. This step increases the dimension of the data and performs non-linear transformation on the data, so that the data can be better expressed in a high-dimensional space, thereby retaining more useful information and avoiding the loss of feature information caused by low-dimensional mapping. Through mapping, the potential patterns of the behavior data are more accurately captured, providing a more rich feature representation for subsequent abnormal behavior detection. By calculating the abnormality degree of each piece of behavior data, the problem of identifying abnormal behavior is solved. In this step, the abnormality degree D(xi) of each piece of behavior data is calculated, where yi is the projection result of the behavior data, Y is the mean of the behavior data set, and σ is the standard deviation of the behavior data set. By calculating the abnormality degree, the system can quantify the deviation degree of each piece of behavior data from the normal behavior, and if the deviation exceeds a certain threshold, it is judged as abnormal behavior. This step effectively solves the problem that traditional abnormal detection methods cannot accurately identify small deviations or complex abnormal patterns. Through high-dimensional space mapping and standard deviation calculation, the abnormality degree of the behavior data can be accurately measured, improving the accuracy and reliability of the abnormal detection. Through the step of judging whether the behavior is abnormal based on the abnormality degree, the problem of single detection strategy is solved. The high-dimensional abnormal behavior detection mechanism does not rely on a single threshold judgment, but considers the projection result and statistical characteristics of the data, such as the mean and standard deviation, so that the calculation of the abnormality degree is more detailed and accurate. This process can flexibly cope with complex and variable behavior patterns, avoiding the misjudgment or omission problem that may occur in traditional methods based on fixed thresholds. At the same time, the system can adapt to the behavior characteristics of different devices or users, thereby providing dynamic and intelligent abnormal detection capability.

[0213] The multi-stage key update mechanism specifically includes:

[0214] S51, update factor calculation;

[0215] using a dynamic unique identifier UID t and a disturbance d t generate an update factor U t :

[0216] ;

[0217] wherein, is a disturbance, UID t is a unique identifier, U t is an update factor;

[0218] S52, update stage by stage;

[0219] At each stage, the update factor U t is used to update the key K t :

[0220] ;

[0221] wherein, represents a bitwise XOR operation, and represents the change of the key at each stage;

[0222] S53, synchronize the final key;

[0223] At the end of the session, the keys K t of all stages and the update factor U t are integrated to generate the session key:

[0224] ;

[0225] wherein, K session is the final session key, and T is the number of stages of key update.

[0226] The update factor U t is generated using the dynamic unique identifier UID t and the disturbance factor t. The update factor U t combines the unique identifier of the device and the dynamic disturbance, ensuring that each key update is unique and dynamic, avoiding the security risks that may be caused by long-term use of static keys. The introduction of the dynamic factor makes the key update no longer a simple static operation, but a dynamic process based on the device state and environmental changes, thereby effectively preventing the key from being subject to risks such as replay attacks and prediction attacks. At each stage, the key K t is updated through a bitwise XOR operation with the update factor U tThe updating is performed. This step avoids the problems of singleness and irreversibility of the key updating process through multiple stage-by-stage updates, improving the security of the key. Each key update depends on the key of the previous stage and the dynamically generated update factor, making the key change more complex and increasing the difficulty of cracking by attackers. The stage-by-stage update not only increases the strength of the key, but also ensures that even if part of the key is leaked, the entire key process still has high security. At the end of the session, the keys of all stages and the update factors are combined to generate a session key. This step ensures the integrity and consistency of the key by combining the keys of all stages and the update factors. This mechanism solves the coordination problem in key management and avoids security vulnerabilities caused by inconsistent or untimely synchronization of different stage keys. At the same time, the generated session key has strong encryption protection capability, ensuring the security of data transmission during the session. Traditional key update mechanisms often rely on a single update operation, while the multi-stage key update mechanism increases the complexity of key changes through multiple update steps, preventing the risk of brute force cracking or leaking of the key. By updating independently at each stage, the complexity and update frequency of the key are greatly improved, making it difficult for attackers to calculate the entire key chain even if a stage key is compromised, thereby greatly improving overall security.

[0227] The embodiment also provides a system for information security protection of a power collection terminal device, including:

[0228] A sensor module: a temperature sensor, a humidity sensor, and a geographic position sensor, wherein the temperature sensor is configured to acquire temperature data, the humidity sensor is configured to acquire humidity data, and the geographic position sensor is configured to acquire device geographic position data;

[0229] A session module: configured to perform a session between the device and an authentication server;

[0230] A calculation module: configured to perform data calculation.

[0231] It should be noted that, in this document, relational terms such as first and second and the like can only be used to distinguish one entity or action from another entity or action, without necessarily requiring or implying any such actual relationship or order between or among the entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0232] The above merely describes the preferred embodiments of the present application, and it should be pointed out that, for those skilled in the art, several improvements and refinements can be made without departing from the technical principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for information security protection of power data acquisition terminal equipment, characterized in that, include: S1. Generate a dynamic unique identifier, including: S11. Multi-dimensional data acquisition of equipment hardware characteristics; Obtain the device hardware characteristic parameters H={h1,h2,h3,h4}, where h1 is the device serial number, h2 is the MAC address, h3 is the CPU ID, and h4 is the storage device number. Among them, each h i It is a numeric type used to represent hardware characteristics; Convert hardware features into binary representations and break them down into bit-level representations: B i ={b i1 ,b i2 ,……,b ij }, ; Among them, b ij It is h i The binary bits, j is h i The number of digits, Indicates rounding up; S12, High-dimensional transformation of hardware features; Define a transformation matrix T, which is specifically an n×m random matrix; ； Among them, t nm These are elements of matrix T, representing the mapping relationship of hardware features; Use matrix multiplication to transform the hardware feature matrix M H By transforming the matrix to map to a new space, we obtain a new high-dimensional feature matrix: ； in, It is the transpose of the hardware feature matrix, M T It is the feature matrix after high-dimensional transformation; S13, Generate dynamic disturbances; Generate dynamic disturbance factor Its value includes a time factor and a noise factor: ； in, Where t is the frequency of the disturbance and t is the current timestamp. It is random noise, conforming to a Gaussian distribution. ; Disturbance factor Add to the mapping matrix to generate a hardware feature matrix containing the perturbation: ； S14. Generate the final dynamic unique identifier; The final unique identifier (UID) is generated using a non-linear activation function. t ; ； in, f It is a non-linear activation function that maps the input to a specific range; High-dimensional feature matrix The value in the i-th row and j-th column; is the exponential decay factor; n is the total number of rows in the high-dimensional feature matrix; m is the number of columns in the high-dimensional feature matrix; S2, Multi-factor key generation mechanism; The multi-factor key generation mechanism specifically includes: S21, Generate seeds; Using a hash function and a unique identifier UID t Generate key seed: ； S22. Introduce environmental parameter disturbances; Use sensors to acquire environmental parameters; The sensors include a temperature sensor, a humidity sensor, and a geographic location sensor; The environmental parameters include temperature, humidity, and geographical location; Let environmental parameter E t ={e1,e2,e3}, where e1 represents temperature, e2 represents humidity, and e3 represents geographical location; Calculate the disturbance coefficient D t The impact of environmental parameters on the key: ； in, It is the norm of the environmental parameter vector, representing the total amount of environmental parameters. It is a time-dependent modulation frequency, where t is a timestamp. For E t The i-th element in; S23. Generate the final key; Combine key seed K seed and environmental disturbance D t Generate the final key: ； Where ⊕ represents the bitwise XOR operation, exp(-e i This represents the exponential decay of environmental parameters, indicating that the influence of environmental factors on the key weakens over time. For E t The i-th element in; S3, Layered Challenge Response Mechanism; S4, High-dimensional abnormal behavior detection; S5, multi-stage key update mechanism.

2. The information security protection method for power data acquisition terminal equipment according to claim 1, characterized in that, The tiered challenge response mechanism specifically includes: S31. Generate layered challenges; S32, Response Calculation; Obtain the variables required for response calculation; The variables include R t (i) and K final ; The R t (i) ={r t (i)j Let} represent the challenge data for the i-th layer, and r t (i)j It is the j-th component in the i-th layer of challenge data; K final A key jointly generated by the device and the authentication server, used for encryption and verification processes; The response value is calculated using the response calculation formula, which is: ； Among them, S t (i) Let be the response value of the i-th layer, representing the device's response based on the key and challenge data; cos(j) is the weighting factor, representing the contribution weight of different positions in the challenge data; Let j be the j-th component of the i-th layer challenge data; For the key; The total number of components in the i-th layer of challenge data; S33, Response Verification; S34. Enhancement of the tiered challenge verification process; The dynamic adjustment formula is: ; Among them, D t ={d it } is the perturbation matrix, representing the perturbation factor at time t; D t 'α' is the perturbation matrix dynamically adjusted according to the failure level i, used to generate the next challenge; i To dynamically adjust the factor and control the weight of the disturbance during the challenge process; Dynamic challenge generation formula: ; Among them, R t (i+1) For the (i+1)th layer challenge data, based on the dynamically adjusted perturbation matrix D t Generate new challenge data; M H This is the hardware feature matrix of the device; () represents a hash operation.

3. The information security protection method for power data acquisition terminal equipment according to claim 2, characterized in that, The generation of hierarchical challenges specifically includes: Obtain the variables required for the tiered challenge; The variables specifically include: M H D t i and t; The M H ={m ij } represents the device hardware feature matrix, indicating the device's hardware information, with each m... ij It is the j-th component of the i-th hardware feature; Let be the environmental disturbance matrix, representing the disturbance value at time stamp t, where each is an environmental disturbance matrix. It is the i-th perturbation factor, which controls the difficulty of challenge generation; i is the level index of the current challenge, indicating that the difficulty of the challenge increases; t is a timestamp, representing the current time point, which controls the dynamic changes of the disturbance; The challenge value for the challenge data is generated using the challenge generation formula, which is: ； The R t (i) Let be the challenge data for the i-th layer, representing the challenge value generated based on device characteristics and perturbations; Hash(x) represents performing a hash operation on the data x to generate a challenge value of fixed length; i·D t D represents the perturbation matrix. t The impact on the current level i.

4. The information security protection method for power data acquisition terminal equipment according to claim 3, characterized in that, The response verification specifically includes: S331. Calculate the expected response value; ； Among them, S expected (i) The expected response value calculated for the authentication server, representing the value based on the received challenge data R. t (i) and key K final The calculated response; S332, Response Verification; ； Among them, S t (i) The response value calculated for the device; S expected (i) The expected response value calculated for the authentication server; This indicates the preset tolerance error threshold.

5. The information security protection method for power data acquisition terminal equipment according to claim 4, characterized in that, The high-dimensional abnormal behavior detection specifically includes: Collect device behavior data and define the behavior dataset as a matrix. X : ； Where, x mn It is the nth feature of the mth behavior; By mapping the behavioral data into a high-dimensional space, a new feature matrix is ​​obtained. Y : ； Where W is the mapping matrix, It is the activation function, b is the bias term, and X is the activation function. T It is the transpose of the data matrix X; Calculate the anomaly degree D(x) for each behavioral data point. i ): ； in, It is the result of projecting behavioral data. It is the mean of the behavioral dataset. It is the standard deviation of the behavioral dataset, D(x) i () indicates the degree of abnormality of the behavioral data.

6. The information security protection method for power data acquisition terminal equipment according to claim 5, characterized in that, The multi-stage key update mechanism specifically includes: S51, Calculation of update factor; Use dynamic unique identifier UID t and disturbance d t Generate update factor U t : ； in, It's a disturbance, UID t Unique identifier, U t It is the update factor; S52, Phased Updates; At each stage, the update factor U is utilized. t For key K t Update: ； Where ⊕ represents the bitwise XOR operation, indicating the change of the key at each stage; S53, Synchronize the final key; At the end of the session, the key K for all phases is... t and update factor U t Perform synthesis to generate session keys: ； Among them, K session It is the final session key, and T is the number of key update stages.

7. A system employing the information security protection method for power acquisition terminal equipment as described in claim 6, characterized in that, include: Sensor module: temperature sensor, humidity sensor and geolocation sensor, wherein the temperature sensor is used to acquire temperature data, the humidity sensor is used to acquire humidity data, and the geolocation sensor is used to acquire the geographical location data of the device; Session module: Used for sessions between the device and the authentication server; Calculation module: Used to perform data calculations.

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