A company information encryption method combined with a chaotic public key encryption algorithm

Abstract

This application provides a method for encrypting company information using a chaotic public-key encryption algorithm, comprising: comparing the time difference between key generation by a first-priority authorized entity and a second-priority authorized entity; if the time difference is less than a preset time difference threshold, generating a set of pseudo-random number sequences as sorting weights based on the priority level of the authorized entity and historical key generation timestamps; sorting the authorized entities according to the sorting weights to determine their key generation order in the current key conflict coordination process; after key generation is completed, distributing the new key to the corresponding authorized object and updating the key status table to record the timestamp of this key update and the authorized entity information, while periodically triggering the key update process; auditing and recording all actions in the key coordination process, including triggering conditions, chaotic mapping and encryption parameters, sorting results, key distribution and updates, and continuously improving the process.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for encrypting company information using a chaotic public-key encryption algorithm. Background Technology

[0002] In an IoT system, when multiple devices can be authorized to access data on the same server, multiple authorized entities may assign permissions to the same object. To avoid key conflicts generated by different authorized entities, the system needs to coordinate the key generation order. If the order is different, multiple authorized entities may generate the same key, leading to key conflicts. In this parallel authorization environment, when multiple authorized entities request access to the same object sequentially within a short period, the system needs to generate keys for each authorized entity in a certain order based on the characteristics of chaotic algorithms. This process may introduce additional time overhead, especially when the number of authorized entities is large, as the key generation order may become complex. Furthermore, the number of iterations and the choice of initial values ​​in the chaotic algorithm also affect the key generation efficiency. Inappropriate parameter settings may lead to excessively long key generation times, thus affecting the response speed of the entire authorization process. Therefore, how to optimize the parameter settings of the chaotic algorithm while ensuring the key generation order and maintaining consistency in a parallel environment is a problem that requires in-depth research. Solving this problem is of great significance for improving the authorization efficiency in a parallel authorization environment. Summary of the Invention

[0003] This invention provides a method for encrypting company information using a chaotic public-key encryption algorithm, mainly comprising:

[0004] When several devices request access to the same server, the priority information of the devices to be accessed is compared with the preset list of authorized subject priorities. If the priority of the device to be accessed matches the first priority level in the list, the device to be accessed is determined to be an authorized subject of the first priority level. If it matches the second priority level, the device to be accessed is determined to be an authorized subject of the second priority level, and a corresponding key is generated according to the priority level of the authorized subject.

[0005] Compare the time difference between the key generation of the first priority level authorizing entity and the second priority level authorizing entity. If the time difference is less than the preset time difference threshold, generate a set of pseudo-random number sequences based on the priority level of the authorizing entity and the historical key generation timestamps, and use them as sorting weights.

[0006] Based on the ranking weight, the authorized entities are ranked to determine the key generation order of the authorized entities in this key conflict coordination process;

[0007] During the key generation process, the identity information and timestamp of the authorized subject are obtained and used as the initial parameters of the chaotic public key encryption algorithm. A chaotic feature sequence is generated through the chaotic algorithm and used as the initial value or iteration parameter for key generation.

[0008] Once the key is generated, the new key is distributed to the corresponding authorized objects, and the key status table is updated to record the timestamp of this key update and the authorized subject information. At the same time, the key update process is triggered periodically.

[0009] The system monitors the security status in real time, dynamically calculates the adjustment range of the key update frequency, generates key update frequency parameters, calculates the adjustment range of the key conflict judgment threshold, generates update threshold parameters, writes the key update frequency parameters and update threshold parameters into the system configuration file, triggers the dynamic adjustment of the key update frequency and conflict judgment threshold, and monitors and records the changes in system security status after the adjustment is completed.

[0010] Audit and record all actions in the key coordination process, including triggering conditions, chaotic mapping and encryption parameters, sorting results, key distribution and updates, and continuously improve the process.

[0011] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0012] This invention discloses a method for encrypting company information using a chaotic public-key encryption algorithm. When multiple devices simultaneously request access to a server, this invention first obtains the device priority and compares it with a preset authorization list to determine the priority level of the authorized entity. A corresponding key is generated based on the priority, and the time difference between the generation of keys at different levels is compared. If the time difference is less than a threshold, a conflict coordination mechanism is triggered. This mechanism uses a chaotic mapping algorithm, generating a pseudo-random sequence based on the priority of the authorized entity and historical timestamps as sorting weights to determine the key generation order. During key generation, the identity information of the authorized entity and the timestamp are used as initial parameters for the chaotic public-key encryption algorithm. After key distribution, the status table is updated, recording the update time and authorization information. This invention also dynamically adjusts the key update frequency and conflict judgment threshold by real-time monitoring of security status and performance indicators. The entire process is fully audited and recorded for continuous improvement. This method effectively solves the key conflict problem when multiple devices access the server simultaneously, improving system security and efficiency. Attached Figure Description

[0013] Figure 1 This is a flowchart of a company information encryption method combining a chaotic public-key encryption algorithm according to the present invention.

[0014] Figure 2 This is a schematic diagram of a company information encryption method combining a chaotic public-key encryption algorithm according to the present invention. Detailed Implementation

[0015] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0016] like Figure 1-2 This embodiment of a company information encryption method combining a chaotic public-key encryption algorithm may specifically include:

[0017] Step S101: When several devices request to access the same server, the priority information of the device to be accessed is compared with the preset priority list of authorized subjects. If the priority of the device to be accessed matches the first priority level in the list, the device to be accessed is determined to be an authorized subject of the first priority level; if it matches the second priority level, the device to be accessed is determined to be an authorized subject of the second priority level, and a corresponding key is generated according to the priority level of the authorized subject.

[0018] For accessing devices, the system obtains the device identification code and access timestamp. A similarity calculation is performed between the device identification code and a pre-stored device feature database to obtain a device authentication pass flag. Based on the device authentication pass flag, historical access records within a preset time window are read, and the device access frequency value and device priority score are obtained by counting the number of accesses. The device priority score is matched against an authorized subject priority rule base. If the priority score falls within the first priority range, the device is determined to be a first-priority authorized subject. A key generation benchmark value is obtained from a preset key generation rule base using the authorized subject priority level. A device access key is generated based on the key generation benchmark value, and the authorized access server region is determined using the device access key.

[0019] Specifically, the system collects the device identification code and access timestamp from the accessing device. A similarity calculation is performed between the device identification code and a pre-stored device feature database to obtain a device authentication score. If the authentication score is greater than a preset authentication threshold, a device authentication pass flag is output. Based on the device authentication pass flag, the system reads the device's historical access records within a preset time window, counts the number of accesses to obtain a device access frequency value, and calculates the device priority score based on the access frequency value. The device priority score is matched against an authorized subject priority rule database. If the priority score is within the first priority range, the device is determined to be a first-priority authorized subject; if the priority score is within the second priority range, the device is determined to be a second-priority authorized subject. Based on the determined authorized subject priority level, the system obtains the corresponding key generation benchmark value and random factor from a preset key generation rule database and generates a device access key using an elliptic curve-based symmetric encryption algorithm. A key feature record is established for the generated device access key. Based on the authorized subject priority level, a key validity period value and security level value are set to generate a key authorization verification record. The system obtains the server access area range corresponding to different security levels from a preset server access permission configuration table, compares the key security level value with the access permission configuration table, and determines the authorized server access area for the device within the current time window. During the acquisition of device identification codes, a combination of hardware and software features is used. Hardware features include the device's CPU serial number, hard drive serial number, and network card physical address. Software features include the operating system version number and application installation list. A 128-bit device identification code is obtained through feature extraction algorithms. The device authentication score is calculated using the cosine similarity method, which calculates the similarity between the device identification code and the codes stored in the feature library. If the cosine of the angle between the two vectors is greater than 0.95, an authentication pass flag is output. Access frequency statistics are based on the cumulative number of accesses within a 24-hour time window. The original access count is smoothed using exponential smoothing to reduce the impact of sudden accesses on priority determination. The device priority score is calculated using a non-linear mapping function based on access frequency. When the smoothed access frequency is below 5 times / hour, it is mapped to the 0-60 score range; 5-10 times / hour is mapped to the 60-80 score range; and above 10 times / hour is mapped to the 80-100 score range. The authorization subject priority rule base pre-defines three priority ranges: 90-100 points correspond to the first priority, 70-89 points to the second priority, and below 70 points to the third priority. During key generation, elliptic curve cryptography is used, with different key lengths selected for different priority levels: 256-bit keys for the first priority, 192-bit keys for the second priority, and 128-bit keys for the third priority. The key feature record includes attribute fields such as key generation timestamp, key length, and authorization subject priority level.The key validity period is positively correlated with the priority level of the authorized entity. The key validity period for the first-priority authorized entity is 24 hours, for the second priority it is 12 hours, and for the third priority it is 6 hours. Server access areas are divided into three levels: core area, application area, and data area. Keys with different security levels have different access permissions: a security level 1 key can access all areas, a security level 2 key can access the application area and data area, and a security level 3 key can only access the data area. When a device requests access to core area resources, the server access control module first verifies the key's validity period. If the key is valid and has a security level of 1, access to core area resources is allowed. Based on this multi-level access control mechanism, differentiated protection for server resources of different importance is achieved. By dynamically adjusting the device's priority score, the access control policy can adapt to dynamic changes in device behavior.

[0020] Step S102: Compare the time difference between the key generation of the first priority level authorizing entity and the second priority level authorizing entity. If the time difference is less than the preset time difference threshold, generate a set of pseudo-random number sequences based on the priority level of the authorizing entity and the historical key generation timestamp, and use them as sorting weights.

[0021] The key generation timestamps of the first and second authorized entities are read from the key generation record library. A time difference value is calculated using the timestamps, and a key conflict flag is obtained by comparing the time difference value with a preset time difference threshold. Based on the key conflict flag, the historical key generation time records of the first and second authorized entities are obtained from the timestamp record table, and the average key generation time interval is calculated for the historical key generation time records. The average key generation time interval is iteratively calculated using the Logistic chaotic mapping equation, and normalized pseudo-random number sequences are generated for the first and second authorized entities respectively. A comprehensive weight score is calculated based on the normalized pseudo-random number sequences and the priority values ​​of the first and second authorized entities.

[0022] Specifically, the key generation timestamps of the first-priority and second-priority authorized entities are read from the key generation record library. The millisecond difference between the two timestamps is calculated to obtain the time difference value. This time difference value is compared with a preset time difference threshold. If the time difference value is less than the preset time difference threshold, a key conflict flag is generated. Based on the key conflict flag, the historical key generation time records of the two authorized entities over the past 24 hours are retrieved from the timestamp record table, and the average key generation time interval for each authorized entity is calculated. Initial values ​​and control parameters are read from the chaotic mapping parameter library, and the average time interval is iteratively calculated using the Logistic chaotic mapping equation to generate two sets of normalized pseudo-random number sequences. For the generated pseudo-random number sequences, a comprehensive weight score is calculated by combining the first-priority and second-priority values. The weighted sum of the pseudo-random number sequence mean and the priority value is used as the final sorting criterion. Based on the comprehensive weight score, the two authorized entities are prioritized, and a key generation sequence number is generated and written to the key generation queue. Key generation tasks are executed sequentially according to the key generation queue order, assigning a unique identifier to each generated key and recording a new key generation timestamp. The system updates the historical key generation timestamp records of authorized entities, writes the current key generation time into the timestamp record table, and sets a conflict coordination completion flag. In key generation time conflict handling, the accuracy of the timestamp directly affects the accuracy of conflict determination. The timestamp uses a millisecond-level recording format. For example, if the key generation time of the first-priority authorized entity is 1640995200123 and the second-priority entity is 1640995200234, the calculated time difference is 111 milliseconds. When the preset time difference threshold is 200 milliseconds, the key conflict coordination mechanism is triggered. The historical key generation time records reflect the access behavior characteristics of authorized entities. By statistically analyzing the historical records within a 24-hour window, the average key generation time interval for the first-priority authorized entity is 300 seconds, and for the second-priority entity, it is 500 seconds. These time intervals are used as the initial input values ​​for the chaotic mapping. The Logistic chaotic mapping uses the iterative equation Xn+1=μXn(1-Xn), where Xn is the iteration value at the nth step, μ is a control parameter with a value of 3.99, and the initial value X0 is obtained by normalizing the time interval, such as 0.3 for the first priority level and 0.5 for the second priority level. After 10 iterations, two sets of pseudo-random number sequences are generated: the first priority level sequence is...

[0023] [0.837, 0.546, 0.990, 0.040, 0.153...], the second priority sequence is...

[0024] [0.999, 0.004, 0.016, 0.063, 0.236...]. In the comprehensive weight calculation, the weight coefficient of the priority level value is 0.7, the weight coefficient of the pseudo-random number sequence mean is 0.3, the first priority level value is 90 points, and the second priority level value is 80 points. Multiplying the first priority level pseudo-random number sequence mean of 0.513 by the priority level score of 90 and then weighting, we get a comprehensive score of 73.91. Multiplying the second priority level pseudo-random number sequence mean of 0.264 by the priority level score of 80 and then weighting, we get a comprehensive score of 56.79. Based on the calculated comprehensive weight scores, the keys are sorted, and key allocation and sequence number generation are performed. The first priority level authorized entity receives sequence number 1, and the second priority level receives sequence number 2. During key generation, the sequence number is used as a queue index to ensure that keys are generated in priority order. The newly generated key is assigned a 32-bit unique identifier, such as "7B9A2F4D", and a precise generation timestamp is recorded. During the timestamp record table update process, historical records outside the 24-hour window are removed, retaining only the most recently generated key generation time record. The updated timestamp record is used for subsequent time difference calculations and chaotic mapping initial value generation, thus forming a dynamically adjusted key generation priority mechanism. This randomized sorting method based on historical behavior ensures the advantageous position of high-priority authorization entities while introducing a moderate degree of uncertainty, avoiding the key generation concentration problem that might arise from fixed priorities.

[0025] Step S103: Sort the authorized entities according to the sorting weight to determine the key generation order of the authorized entities in this key conflict coordination process.

[0026] A set of authorized entity identifiers to be sorted is obtained from the authorized entity queue. The set of authorized entity identifiers is sorted in descending order by sorting weight values ​​to obtain an authorized entity sequence list. Based on the sorting sequence number in the authorized entity sequence list, the baseline time interval value in the time window configuration table is read. The key generation time is obtained by multiplying the sorting sequence number and the baseline time interval value. For the key generation time and the sorting sequence number, a binary heap structure is used to allocate resource blocks to the authorized entities to obtain a resource occupancy sequence. If the resource occupancy sequence contains an authorized entity, the priority attribute value of the authorized entity is read. Based on the priority attribute value, a resource occupancy flag is set, and a key conflict coordination sorting result record is generated.

[0027] Specifically, the process involves retrieving the set of authorized entity identifiers to be sorted from the authorized entity queue, extracting the sorting weight value for each identifier, and generating an authorized entity sequence table by sorting them in descending order of weight value. The authorized entity identifiers are then numbered using this sequence table to obtain a sorting sequence number. Based on the authorized entity sorting sequence number, a baseline time interval value is retrieved from the time window configuration table, and the key generation time for each authorized entity is calculated by multiplying the sorting sequence number by the baseline time interval value. Using the sorting sequence number and key generation time, the resource occupancy duration for each authorized entity is calculated, and resource blocks are allocated to the authorized entities using a binary heap structure to generate a resource occupancy sequence. For each authorized entity in the resource occupancy sequence, its priority attribute value is retrieved, and a resource occupancy flag is set based on the priority attribute value, generating a resource allocation record table. The authorized entity identifier and sorting sequence number are extracted from the resource allocation record table, establishing an identifier mapping relationship, setting a key generation status flag, and generating a current round key conflict coordination sorting result record. Based on the order of the authorized entity identifiers in the sorting result record, key generation tasks are scheduled according to the resource occupancy duration, and the key generation status value of the authorized entity is updated. There are 5 entities to be processed in the authorization entity queue, with ranking weights of 85.6, 92.3, 78.4, 88.9, and 82.1 respectively. The sequence is obtained by sorting them in descending order.

[0028] The numbers [92.3, 88.9, 85.6, 82.1, 78.4] correspond to the authorized entity identifiers [B, D, A, E, C], and their assigned sequence numbers are [1, 2, 3, 4, 5]. The baseline time interval is set to 200 milliseconds. The key generation time for each authorized entity is calculated based on the sequence number: authorized entity B's generation time is the baseline time plus 200 milliseconds, D's is the baseline time plus 400 milliseconds, A's is the baseline time plus 600 milliseconds, and so on. This incremental time allocation ensures that higher-priority authorized entities receive earlier processing time. In the resource allocation phase, the resource usage time for each authorized entity is determined by its priority: priority 1 is allocated 150 milliseconds, priority 2 is allocated 120 milliseconds, and priority 3 is allocated 100 milliseconds. A binary heap structure is used for resource block allocation. Heap nodes record the authorized entity identifier, start time, and end time. Heap operations ensure that resource block allocation does not overlap. The resource allocation record table displays detailed allocation information: Authorized entity B occupies 0-150 milliseconds with a priority attribute value of 90; D occupies 200-320 milliseconds with a priority attribute value of 85; and A occupies 400-500 milliseconds with a priority attribute value of 82. Resource occupancy markers are represented using binary bits, with occupancy being 1 and idle being 0. Each time slice corresponds to one marker bit. In the identifier mapping relationship, a correspondence is established between authorized entity identifiers and their sequence numbers: {B:1, D:2, A:3, E:4, C:5}. Simultaneously, the key generation status is recorded: pending generation is recorded as 0, generation in progress as 1, and generation complete as 2. Through this mapping relationship, the key generation task executor can schedule key generation operations in a strict order. The final key generation process is executed according to the time schedule of the resource allocation record table. When the key generation of authorized subject B is completed, its status value is updated to 2, and the key generation task of authorized subject D is triggered at the same time. Through strict time control and status management, the key generation operations of multiple authorized subjects are ensured to proceed in an orderly manner, avoiding resource competition and time conflicts. This weight-based dynamic sorting mechanism not only guarantees the processing advantage of high-priority authorized subjects, but also improves the overall processing efficiency through reasonable time allocation.

[0029] Step S104: During the key generation process, the identity information and timestamp of the authorized subject are obtained, and the identity information and timestamp of the authorized subject are used as the initial parameters of the chaotic public key encryption algorithm. A chaotic feature sequence is generated through the chaotic algorithm, and the chaotic feature sequence is used as the initial value or iteration parameter for generating the key.

[0030] The system reads the hardware serial number of the authorized entity from the identity authentication center, generates an identity feature identifier code based on the hardware serial number and identity feature data, and verifies the identity feature identifier code by the identity authentication center. The identity feature identifier code is then segmented and extracted, and a hyperbolic tangent function is used to perform feature mapping between the identity feature identifier code and a timestamp sequence to obtain a time feature sequence. Control parameters are extracted from the time feature sequence, and a Logistic chaotic mapping equation is used to iteratively calculate the control parameters, which are then combined with the identity feature identifier code to obtain a chaotic feature sequence. Finally, a nonlinear function mapping is performed on the chaotic feature sequence to obtain the initial key parameters.

[0031] Specifically, the system reads the hardware serial number of the authorized entity from the identity authentication center, generates an identity feature identifier code by combining it with the identity feature data submitted by the authorized entity, and compares the identity feature identifier code with a preset identity verification threshold to determine the identity verification result. Based on the identity verification pass marker, the authorized entity's identity feature identifier code is segmented and extracted, and a time feature sequence is generated by combining it with the current timestamp. A hyperbolic tangent function is used to map and transform the time feature sequence. Control parameters are extracted from the time feature sequence, and iterative calculations are performed based on the Logistic chaotic mapping equation. The iteration result is combined with the identity feature identifier code to generate a chaotic feature sequence. Based on the chaotic feature sequence, the corresponding mapping function is read from a preset parameter mapping rule library, and a nonlinear function mapping is performed on the chaotic feature sequence to obtain the initial key parameters. An iterative function is constructed using the initial key parameters, and a fixed-length key sequence is generated using Henon chaotic mapping. The key generation seed value is calculated based on the key sequence. The key generation seed value is divided into blocks, and key block parameters are set based on the authorized entity's priority information to generate the final key iteration parameter sequence. The identity authentication center obtains a 32-bit hardware serial number from the authorized entity, such as "A7B9C4D2E8F1G3H5", and splits it into four 8-bit sub-sequences. Each sub-sequence is XORed with the identity feature data. The identity feature data includes a combination of the device MAC address, CPU serial number, and hard drive serial number, which is used to generate a 256-bit identity feature identifier using the SHA-256 hash algorithm. The authentication threshold is set at a similarity of 0.85 or higher, and the verification result is determined by calculating the Hamming distance. The time feature sequence is generated using a timestamp segmentation method, taking the last 8 bits of the current timestamp "1640995200123", grouping them into four sets of two digits each.

[0032] [00,12,31,23]. The hyperbolic tangent function tanh(x) normalizes these values, transforming them to the interval [-1,1], resulting in the mapping sequence [-0.761,0.423,0.892,-0.156]. The Logistic chaotic mapping uses the equation Xn+1=μXn(1-Xn), with the control parameter μ set to 3.99. The first value of the time feature sequence, -0.761, is used as the initial value X0, and 20 iterations are performed. The output of each iteration is modulo-added with the corresponding bits of the identity feature code to generate a chaotic feature sequence of length 256 bits. The nonlinear function mapping uses a piecewise function; when the element value of the chaotic feature sequence is less than 0.5, the square function f(x)=x is used. 2 When the value is greater than 0.5, the exponential function f(x) = ex-1 is used. The mapped sequence is used as the initial key parameter, with each 32 bits forming a parameter block. The Henon chaotic mapping uses a two-dimensional iterative equation: Xn+1 = 1 - aXn² + Yn, Yn+1 = bXn, with parameters a = 1.4, b = 0.3, and Xn+1 being the iteration value at step n+1. Using the first two values ​​of the initial key parameter as the initial point (X0, Y0), 128 pairs of two-dimensional point pairs are generated iteratively. The x-coordinates of these point pairs are extracted and linearly transformed to obtain a 256-byte key sequence. The key generation seed value is obtained by performing a block XOR operation on the key sequence, with each 32-byte group generating an 8-byte seed value. Different block parameters are set according to the priority information of the authorizing entity. Authorized entities with priority 1 use 8-byte key blocks, those with priority 2 use 16-byte key blocks, and those with priority 3 use 32-byte key blocks. This block-sharing strategy is closely related to the generation of the key iteration parameter sequence, allowing higher-priority authorized entities to obtain finer-grained key control.

[0033] Step S105: After the key is generated, the new key is distributed to the corresponding authorized object, and the key status table is updated to record the timestamp of this key update and the authorized subject information. At the same time, the key update process is triggered periodically.

[0034] A key distribution data packet is generated based on the key content and the authorized entity identifier code. The key distribution data packet is sent to the authorized entity through an encrypted channel. For the digital signature information returned by the authorized entity, a key status item is created in the key status record library. The key status item includes the key identifier code, the authorized entity identifier code, the distribution timestamp, and the key validity period. Based on the monitoring flag in the key status item, the key update cycle value of the authorized entity is read from a preset key validity parameter table to obtain the next key update time. If the timed scanner reads an expired task record in the key update schedule table, a key update trigger instruction is generated to initiate a new round of key generation process.

[0035] Specifically, the newly generated key content is read from the key generator, combined with the authorized entity identifier to generate a key distribution data packet, and sent to the authorized entity through an encrypted channel. The distribution completion status is determined based on the digital signature information returned by the authorized entity. For keys that have been distributed, a key status entry is created in the key status record library, recording the key identifier, authorized entity identifier, distribution timestamp, and key validity period, generating a key status monitoring flag. Based on the key status monitoring flag, a preset key validity parameter table is read, the key update cycle value for the corresponding authorized entity is extracted, and the next key update time is calculated. Using a time window sliding method, an update task record is created in the key update planning table, setting the task execution timestamp and authorized entity identifier. The expired task records in the key update planning table are read by a timed scanner, and the authorized entity update parameter set is extracted. A key update trigger command is generated based on the update parameter set, marking the original key status entry as updated. An update trigger command is sent to the key generator to start a new round of key generation process, and the original key status information is saved in the key history record table. The key distribution data packet is constructed in a standard format, containing a 256-bit key content, a 32-bit authorized entity identifier, and a 64-bit timestamp. RSA asymmetric encryption is used to encrypt the data packet during transmission. After receiving the key, the authorized entity uses its private key to generate a digital signature for the receipt confirmation message. The signature uses the SHA-256 algorithm to generate a message digest, which is then encrypted using the private key to obtain the digital signature value. The key status field records the complete lifecycle information of the key. The identifier is generated as a 32-bit unique identifier using UU ID format, and the distribution timestamp is recorded to the millisecond level, such as "1640995200123". The key validity period is set according to the authorized entity's security level: 24 hours for the highest level, 12 hours for the intermediate level, and 6 hours for the basic level. The status monitoring flag uses binary bits to represent different states: 0x01 indicates distributed, 0x02 indicates in use, and 0x04 indicates expired. In the key expiration parameter table, different update cycles are set for authorized entities with different security levels. For example, the highest level updates every 12 hours, the intermediate level every 6 hours, and the basic level every 3 hours. The update time is calculated using a base time plus an offset, where the base time is 00:00 of the current day, and the offset is an integer multiple of the update cycle. The time window sliding method uses a fixed-size time window, set to 1 hour. When an update task is detected within the window, update preparation is triggered 5 minutes in advance. The update task record includes fields such as task number, execution time, and authorized entity information, and is stored using a priority queue, with higher-priority tasks executed first. A timed scanner scans the update planning table every minute to extract update tasks that need to be executed within the last 10 minutes. The update parameter set includes information such as the authorized entity's security level, historical update records, and current key status.Update trigger commands are transmitted via a message queue, with the message body containing detailed parameters for the update task. Before transferring the original key status record to the history table, an update timestamp and update reason code are added. The history table retains update records for the most recent 7 days for analyzing key update patterns and optimizing update strategies. Through this time-window-based update mechanism, combined with differentiated configurations for authorized entities, automatic key updates and status tracking are achieved.

[0036] Step S106: Monitor the system security status in real time, dynamically calculate the key update frequency adjustment range, generate key update frequency parameters, calculate the key conflict judgment threshold adjustment range, generate update threshold parameters, write the key update frequency parameters and update threshold parameters into the system configuration file, trigger the dynamic adjustment of key update frequency and conflict judgment threshold, and after the adjustment is completed, monitor and record the system security status change data.

[0037] A loop monitor is used to acquire operational status data such as processor load rate, memory usage rate, and network throughput. A performance index matrix is ​​obtained by extracting security posture features through a convolutional neural network. Based on the performance index matrix, the ratio of the number of key conflicts to the total number of key generation times is calculated within a fixed time window to obtain the key conflict rate. An adjustment coefficient is calculated based on the comparison result between the key conflict rate and a preset benchmark value. The frequency parameters are then linearly transformed using the adjustment coefficient to obtain new frequency parameters. If the new frequency parameters pass the parameter consistency verification rules, the new frequency parameters are written into the configuration parameter table, and the adjustment timestamp of the new frequency parameters is recorded in the adjustment record table.

[0038] Specifically, a loop monitor reads operational status data, sampling processor load, memory usage, and network throughput within a specified time period. A convolutional neural network extracts security posture features to generate a performance index matrix. Based on this matrix, the current key conflict rate is calculated by dividing the number of key conflicts per unit time by the total number of key generation times within a fixed-size time window. The conflict rate is compared to a preset benchmark value, and a key update frequency adjustment coefficient is calculated based on the comparison result. This coefficient is then used to linearly transform the current frequency parameters. For the transformed frequency parameters, maximum and minimum limit ranges are set, and parameter values ​​exceeding these ranges are truncated to generate new frequency parameter values. An exponential smoothing method is used to calculate the adjustment amount for the conflict judgment threshold, and a new threshold parameter value is generated by combining historical adjustment records. The newly generated frequency parameter values ​​and the threshold parameter values ​​are checked for consistency, and parameter validity is determined according to verification rules. Parameters that pass verification are written to the configuration parameter table, and parameter change information, including the adjustment timestamp, the parameter value before adjustment, and the parameter value after adjustment, is recorded in the adjustment record table. The parameter monitoring program is then started to collect updated performance data and record performance comparison data before and after parameter adjustment in the monitoring log table. During performance data sampling, system status data is collected every 10 seconds, including processor load rate of 75%, memory usage rate of 60%, and network throughput of 150MB / s. The sampled data forms a 32x32 feature map, which is then used for feature extraction via a 3-layer convolutional neural network with a kernel size of 3x3 and a stride of 1, resulting in a performance index matrix with 8 dimensions. Key collision rate is calculated using a fixed 60-minute time window, counting the total number of key generation attempts and collisions within the window. For example, if the total number of key generation attempts is 1200 and 48 collisions occur within the current time window, the collision rate is calculated to be 4%. The preset baseline collision rate is 3%. When the actual collision rate exceeds the baseline value, a parameter adjustment mechanism is triggered. The frequency adjustment coefficient is calculated using a linear mapping method, mapping the difference between the collision rate and the baseline value to the interval [0.8, 1.2]. When the collision rate is 4%, the difference is 1%, resulting in an adjustment coefficient of 1.1. The current update frequency is 6 times per hour, which will be increased to 6.6 times per hour after adjustment. The frequency parameter's range is set to [4, 12], and values ​​outside this range will be truncated. The conflict threshold is adjusted using an exponential smoothing formula: Yt = αXt + (1-α)Yt-1, where α is the smoothing coefficient (0.3), Xt is the current calculated adjustment amount, and Yt-1 is the previous adjustment result. For example, if the current calculated adjustment amount is 5ms and the previous adjustment result was 8ms, then the smoothed adjustment result will be 7.1ms. The parameter consistency verification rules include three aspects: the ratio of the frequency parameter to the threshold parameter should be within the range of [1.5, 3]; the parameter adjustment amplitude should not exceed 30% of the current value; and the fluctuation trend of the new parameter should be consistent with that of the historical parameters.After successful verification, the parameters are written to the configuration table, with the record format "Parameter Type: Frequency Parameter, Value Before Adjustment: 6.0, Value After Adjustment: 6.6, Timestamp: 1640995200". Performance comparison data is collected for 30 minutes, and the comparison data includes indicators such as system throughput, response time, and resource utilization before and after adjustment. After parameter adjustment, system throughput increased from 150MB / s to 165MB / s, average response time decreased from 85ms to 78ms, and resource utilization remained at around 65%. Through this dynamic parameter adjustment mechanism based on real-time monitoring, system performance is continuously optimized. The monitoring log table retains adjustment records for the most recent 7 days for analyzing the long-term effects of parameter adjustments.

[0039] Step S107: Audit and record all actions in the key coordination process, including triggering conditions, chaotic mapping and encryption parameters, sorting results, key distribution and updates, and continuously improve the process.

[0040] Coordination process data is obtained from the key coordinator. This data includes the authorized entity identifier code, resource request time, and conflict flag value. A coordination process record is generated based on this data. Chaotic mapping algorithm running parameters, including initial seed value and control variable value, are extracted from the coordination process record. A chaotic mapping process record is obtained using these parameters. Priority is calculated for the authorized entities based on the chaotic mapping process record. A weighted summation method is used to process historical behavior values ​​and current state values ​​in the chaotic mapping process record to obtain a priority ranking table. Upon receiving a key distribution request, key resources are allocated according to the priority ranking table. The distribution timestamp and authorized entity number are recorded to obtain a key status tracking record.

[0041] Specifically, coordination process data is collected from the key coordinator, including the authorized entity identifier code, resource request time, conflict flag value, and coordination trigger conditions, generating a coordination process record file and writing it to the key coordination database. Based on the coordination process record file, the running parameters of the chaotic mapping algorithm are extracted, including the initial seed value, control variable value, and number of iterations, generating a chaotic mapping process record. According to the chaotic mapping process record, the priority score of the authorized entity is calculated, and a weighted summation method is used to combine historical behavior values ​​with current state values ​​to generate a priority ranking table. Using the priority ranking table, the key distribution task is executed, recording the distribution timestamp, authorized entity number, and key identifier code to generate a key distribution record. For the key distribution record, combined with the security level of the authorized entity, the key validity period value is calculated, generating a key status tracking record. A data analyzer is used to statistically analyze the key status tracking record, calculating the key update frequency, conflict rate, and distribution success rate, generating a performance statistics report. Based on the performance statistics report, key performance indicators are extracted, performance benchmark values ​​are set, and warning signals are generated for indicators exceeding the benchmark range. Based on the warning signals, the coordination process record for the corresponding time period is read, the cause of the anomaly is analyzed, and the key coordination parameter configuration table is updated. The key reconciliation process record contains several key fields. The authorized entity identifier is represented by a 16-bit hexadecimal number, such as "A5B2C3D4E6F78901". The resource request time is accurate to the millisecond level, such as "1640995200123". The conflict flag value uses binary bits to represent different types of conflicts, such as "1010".

[0042] This indicates time and priority conflicts. The trigger condition field records the specific reason for triggering coordination, such as resource contention exceeding 80%. The chaotic mapping parameters use the Logistic equation. The initial seed value is obtained by normalizing the timestamp, such as 0.6234. The control variable value is set to 3.99, and the number of iterations is 20. The output value of each iteration is recorded, forming a chaotic sequence.

[0043] [0.837, 0.546, 0.990, 0.040, 0.153...], used for subsequent randomization processing. The priority score is calculated using a weighted summation method, with historical behavior values ​​having a weight of 0.6 and the current state value having a weight of 0.4. Historical behavior values ​​include indicators such as key usage frequency, number of conflicts, and response time over the past 24 hours, while the current state value includes indicators such as resource utilization, request priority, and waiting time. For a given authorized entity, the historical behavior value is 85 points, and the current state value is 92 points, resulting in a final priority score of 87.8. The key distribution record details each stage of the distribution process, including the sending time "2023-12-

[0044] The reception confirmation time was "2023-12-16 10:30:45.123", and the transmission time was 333 milliseconds. The key identification code uses UU. The ID format is as follows: "550e8400-e29b-41d4-a716-446655440000". Based on the security level of the authorized entity, it is divided into levels 1, 2, and 3, with key validity periods set: 24 hours for level 1, 12 hours for level 2, and 6 hours for level 3. Performance statistics show that in the past hour, the average key update frequency was 6 times / hour, the conflict rate was 3.5%, and the distribution success rate reached 99.8%. The performance benchmark was set to an update frequency of 4-8 times / hour, a conflict rate below 5%, and a distribution success rate above 99%. When the conflict rate is detected to be rising continuously within 10 minutes, from 2.8% to 4.2%, an early warning signal is generated. Analysis of the coordination process records during the early warning period revealed that the main reason was that multiple high-priority authorized entities simultaneously requested resources within a certain time period. The time window parameter in the coordination parameter configuration table was adjusted from 200 milliseconds to 300 milliseconds, and the weight of historical behavior values ​​in the weight calculation formula was adjusted from 0.6 to 0.7. The optimized conflict rate was reduced to 2.8%.

[0045] Based on the embodiments of the present invention described above, and through the above description, those skilled in the art can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for encrypting company information using a chaotic public-key encryption algorithm, characterized in that, The method includes: when several devices request access to the same server, comparing the priority information of the devices to be accessed with a preset list of authorized subject priorities; if the priority of the device to be accessed matches the first priority level in the list, the device to be accessed is determined to be an authorized subject of the first priority level; if it matches the second priority level, the device to be accessed is determined to be an authorized subject of the second priority level, and a corresponding key is generated according to the priority level of the authorized subject; comparing the time difference between the key generation of the first priority level authorized subject and the second priority level authorized subject; if the time difference is less than a preset time difference threshold, generating a set of pseudo-random number sequences as sorting weights based on the priority level of the authorized subject and historical keys; sorting the authorized subjects according to the sorting weights to determine the key generation order of the authorized subjects in this key conflict coordination process; during the key generation process, obtaining the identity information and timestamp of the authorized subject, and assigning the authorized subject... The identity information and timestamp are used as initial parameters for the chaotic public-key encryption algorithm. A chaotic feature sequence is generated through the chaotic algorithm, and the chaotic feature sequence is used to generate the initial value or iteration parameters of the key. After the key is generated, the new key is distributed to the corresponding authorized objects, the key status is updated, the timestamp of this key update and the authorized subject information are recorded, and the key update process is triggered periodically. The system security status is monitored in real time, the key update frequency adjustment range is dynamically calculated, the key update frequency parameter is generated, the key conflict judgment threshold adjustment range is calculated, and the update threshold parameter is generated. The key update frequency parameter and update threshold parameter are written into the system configuration file to trigger the dynamic adjustment of the key update frequency and conflict judgment threshold. After the adjustment is completed, the system security status change data is monitored and recorded. All actions in the key coordination process are audited and recorded, including triggering conditions, chaotic mapping and encryption parameters, sorting results, key distribution and updates, and the process is continuously improved.

2. The method according to claim 1, characterized in that, When multiple devices request access to the same server, the priority information of the devices to be accessed is compared with a preset list of authorized subject priorities. If the priority of the device to be accessed matches the first priority level in the list, the device to be accessed is determined to be an authorized subject of the first priority level. If it matches the second priority level, the device to be accessed is determined to be an authorized subject of the second priority level. A corresponding key is generated according to the priority level of the authorized subject. This includes: obtaining the device identification code and access timestamp for the accessing device; calculating the similarity between the device identification code and a pre-stored device feature library to obtain a device authentication pass flag; reading historical access records within a preset time window based on the device authentication pass flag; obtaining the device access frequency value and device priority score by counting the number of accesses; and matching the device priority score with the authorized subject priority rule library. If the priority score is within the first priority range, the device is determined to be an authorized subject of the first priority level. The system obtains a key generation benchmark value from a preset key generation rule base based on the priority level of the authorized subject, generates a device access key based on the key generation benchmark value, and determines the authorized access server area through the device access key.

3. The method according to claim 1, characterized in that, The step of comparing the time difference between the key generation of the first priority level authorizing entity and the second priority level authorizing entity, if the time difference is less than a preset time difference threshold, then generating a set of pseudo-random number sequences as sorting weights based on the priority level of the authorizing entity and the historical key generation timestamps, includes: reading the key generation timestamps of the first authorizing entity and the second authorizing entity from the key generation record library, calculating the time difference value through the timestamps, and obtaining a key conflict marker by comparing the time difference value with the preset time difference threshold; Based on the key conflict marker, the historical key generation time records of the first authorized entity and the second authorized entity are obtained from the timestamp record table, and the average key generation time interval is calculated based on the historical key generation time records. The average key generation time interval is iteratively calculated using the Logistic chaotic mapping equation, and normalized pseudo-random number sequences are generated for the first authorizing entity and the second authorizing entity, respectively. A comprehensive weight score is calculated based on the normalized pseudo-random number sequences, the priority level values ​​of the first authorizing entity and the second authorizing entity.

4. The method according to claim 1, characterized in that, The step of sorting authorized entities according to sorting weights to determine the key generation order of authorized entities in this key conflict coordination process includes: obtaining a set of authorized entity identifiers to be sorted from the authorized entity queue; sorting the authorized entity identifier set in descending order by sorting weight values ​​to obtain an authorized entity sequence list; reading the baseline time interval value from the time window configuration table according to the sorting sequence number in the authorized entity sequence list; obtaining the key generation time by multiplying the sorting sequence number by the baseline time interval value; allocating resource blocks to authorized entities using a binary heap structure for the key generation time and the sorting sequence number to obtain a resource occupancy sequence; if the resource occupancy sequence contains an authorized entity, reading the priority attribute value of the authorized entity, setting a resource occupancy flag according to the priority attribute value, and generating a key conflict coordination sorting result record.

5. The method according to claim 1, characterized in that, The key generation process involves obtaining the authorized entity's identity information and timestamp, using these as initial parameters for a chaotic public-key encryption algorithm, generating a chaotic feature sequence using a chaotic algorithm, and then using this chaotic feature sequence to generate the initial value or iteration parameters for the key. This includes: reading the authorized entity's hardware serial number from an identity authentication center; generating an identity feature identifier code based on the hardware serial number and identity feature data, which is verified by the identity authentication center; segmenting and extracting the identity feature identifier code; using a hyperbolic tangent function to perform feature mapping between the identity feature identifier code and the timestamp sequence to obtain a time feature sequence; extracting control parameters based on the time feature sequence; using a Logistic chaotic mapping equation to iteratively calculate the control parameters; combining these with the identity feature identifier code to obtain a chaotic feature sequence; and performing a nonlinear function mapping on the chaotic feature sequence to obtain the initial key parameters.

6. The method according to claim 1, characterized in that, After the key is generated, the new key is distributed to the corresponding authorized object, and the key status is updated. The timestamp of this key update and the authorized subject information are recorded. At the same time, the key update process is triggered periodically, including: generating a key distribution data packet based on the key content and the authorized subject identification code, and sending the key distribution data packet to the authorized subject through an encrypted channel; creating a key status item in the key status record library based on the digital signature information returned by the authorized subject, the key status item including the key identification code, the authorized subject identification code, the distribution timestamp, and the key validity period; and reading the key update cycle value of the authorized subject from the preset key validity parameter table based on the monitoring flag in the key status item to obtain the next key update time. If the timed scanner reads an expired task record in the key update planning table, it generates a key update trigger instruction, which is used to start a new round of key generation process.

7. The method according to claim 1, characterized in that, The real-time monitoring system dynamically calculates the key update frequency adjustment range, generates key update frequency parameters, calculates the key conflict judgment threshold adjustment range, generates update threshold parameters, and writes the key update frequency parameters and update threshold parameters into the system configuration file. This triggers the dynamic adjustment of the key update frequency and conflict judgment threshold. After the adjustment is completed, the system monitors and records changes in security status data, including: using a loop monitor to obtain operating status data of processor load rate, memory usage rate, and network throughput; extracting security status features through a convolutional neural network to obtain a performance index matrix; calculating the ratio of the number of key conflicts to the total number of key generation within a fixed time window based on the performance index matrix to obtain a key conflict rate value; calculating an adjustment coefficient based on the comparison result of the key conflict rate value and a preset benchmark value; performing a linear transformation on the frequency parameters using the adjustment coefficient to obtain new frequency parameters; if the new frequency parameters pass the parameter consistency verification rules, the new frequency parameters are written into the configuration parameter table, and the adjustment timestamp of the new frequency parameters is recorded in the adjustment record table.

8. The method according to claim 1, characterized in that, The process of auditing and recording all actions in the key coordination process, including triggering conditions, chaotic mapping and encryption parameters, sorting results, key distribution and updates, and continuously improving the process, includes: obtaining coordination process data from the key coordinator, the coordination process data containing the authorized subject identifier code, resource request time and conflict flag value, and generating a coordination process record based on the coordination process data; extracting chaotic mapping algorithm running parameters from the coordination process record, the chaotic mapping algorithm running parameters containing the initial seed value and control variable value, and obtaining a chaotic mapping process record using the chaotic mapping algorithm running parameters; calculating the priority of the authorized subject based on the chaotic mapping process record, and using a weighted summation method to process the historical behavior value and current state value in the chaotic mapping process record to obtain a priority sorting table; and allocating key resources according to the priority sorting table after receiving a key distribution request, recording the distribution timestamp and authorized subject number to obtain a key status tracking record.

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