Intelligent generator for full-automatic generation of production report

Abstract

The invention relates to the field of report generation, and discloses an intelligent generator for full-automatic generation of a production report, which is used for fusing a complex system theory and an information security technology and constructing a non-machine learning driven full-automatic generation system of the production report. Performing fractal cutting on original time series data and encrypting and storing the original time series data to edge nodes; constructing a cellular automaton network; generating a cellular state transition rule according to network traffic and access logs; establishing a system dynamics model; and generating a production report in combination with the access permission and the risk confidence. According to the intelligent generation method for full-automatic generation of the production report, the data processing efficiency and safety are improved, the flexibility of access control and the accuracy of risk assessment are enhanced, and powerful technical support is provided for intelligent management of a modern production system.

Description

Technical Field

[0001] The present invention relates to the field of report generation, and particularly to an intelligent generator for fully automatic generation of production reports. Background Art

[0002] With the rapid development of Industry 4.0 and intelligent manufacturing, modern production systems have become increasingly complex, and the amount of data has grown exponentially. As an important basis for enterprise management and decision-making, the accuracy and timeliness of production reports are directly related to the production efficiency and operation quality of enterprises. However, traditional methods for generating production reports mainly rely on manual collation and data analysis, which are not only time-consuming and laborious, but also difficult to meet the processing requirements of large-scale and high-frequency time-series data. Especially in the industrial Internet environment, time-series data such as equipment operation parameters, production records, and process indicators have the characteristics of massive volume, multiple sources, and heterogeneity, which pose higher challenges to the generation of production reports.

[0003] The existing production report generation technologies mainly have the following deficiencies:

[0004] Low data processing efficiency: When dealing with large-scale time-series data, traditional methods often require complex data cleaning, integration, and analysis processes, which usually rely on manual operations or simple automation scripts and are difficult to meet real-time requirements.

[0005] Insufficient data security and privacy protection: Production data contains a large amount of sensitive information, such as equipment operation status and production data. Traditional methods lack effective encryption and distributed storage mechanisms during data storage and transmission, making it easy to cause data leakage risks.

[0006] Inflexible access control: With the diversification of user roles and the dynamic changes in access requirements, traditional access control mechanisms are difficult to achieve fine-grained permission management and cannot effectively prevent unauthorized access and data abuse.

[0007] Weak risk assessment and early warning capabilities: Traditional production reports mainly focus on data display and statistics, lacking in-depth analysis of the coupling relationship between production data streams and network traffic, and it is difficult to detect and warn of potential security risks in a timely manner.

[0008] Poor data visualization and anti-tampering performance: Traditional reports have limitations in data visualization and anti-tampering, making it difficult to intuitively display data trends and risk factors, and lacking an effective data integrity verification mechanism.

[0009] Therefore, we propose an intelligent generator for fully automatic generation of production reports to solve the above problems. Summary of the Invention

[0010] The present invention provides an intelligent generator for automatic generation of production reports, which is used to integrate complex system theory and information security technology to construct an automatic production report generation system driven by non-machine learning.

[0011] In a first aspect of the present invention, there is provided an intelligent generation method for automatic generation of production reports. The intelligent generation method for automatic generation of production reports includes: obtaining original time-series data, where the original time-series data includes device operation parameters, production records, and process indicators; determining the fractal cutting dimension based on the original time-series data, and cutting the original time-series data into fractal data sub-blocks with self-similarity; encrypting each fractal data sub-block to generate encrypted fractal sub-blocks, and distributing the encrypted fractal sub-blocks to multiple edge storage nodes for storage; constructing a cellular automaton network, generating cellular state transition rules according to real-time network traffic data and historical access logs, and when receiving a report access request initiated by a user, performing access permission verification based on the current state of the corresponding user account cell in the cellular automaton network to generate an access permission verification result; establishing a system dynamics model, where the system dynamics model defines the coupling relationship between production data flow and network traffic, and calculating a real-time risk confidence index based on the device operation parameters and the access permission verification result; retrieving the encrypted fractal sub-blocks related to the current report request from the edge storage nodes to obtain the restored data trend, performing dynamic desensitization processing on the restored data trend according to the access permission verification result, and generating production report content based on the desensitized data trend and the real-time risk confidence index.

[0012] Optionally, in a first implementation manner of the first aspect of the present invention, it includes: calculating the Hurst exponent of the original time-series data, generating a fractal dimension value D = 2 - H, and determining the fractal cutting layer number according to the fractal dimension value. Where N is an integer and 3 ≤ N ≤ 8; performing multi-level fractal cutting on the original time-series data according to the cutting layer number N to generate 2 N fractal data sub-blocks, adding fractal tree-shaped codes to each fractal data sub-block; independently generating encryption keys for each fractal data sub-block, and associating the sub-block keys at the same fractal level through a hash chain, randomly distributing the encrypted fractal data sub-blocks to at least 3 edge storage nodes, recording a storage location mapping table, and generating a fractal storage verification code for each edge storage node.

[0013] Optionally, in the second implementation manner of the first aspect of the present invention, it includes: mapping each user account and device access terminal to an independent cell in a cellular automaton network, and assigning an initial state label to each cell; defining the cell neighborhood range as network nodes with a topological distance not exceeding 3 hops, and generating a cell adjacency relationship matrix; statistically analyzing the abnormal request features in the real-time network traffic data, and generating a cell state transition rule set based on the abnormal request features and the cell adjacency relationship matrix; periodically updating the current states of all cells in the cellular automaton network according to the cell state transition rule set, and when a user initiates a report access request, extracting the cell state and neighborhood abnormality ratio corresponding to the user, and generating a dynamic permission label.

[0014] Optionally, in the third implementation manner of the first aspect of the present invention, it includes: defining the first core variable of the system dynamics model as the normal traffic change rate, which is dynamically adjusted by the difference between the production interface call frequency and the abnormal request detection rate; defining the second core variable as the risk value change rate, which is calculated by the weighted product of the vulnerability exposure rate and the security patch coverage rate, and the weight assignment is a function of the vulnerability severity level; calculating the real-time vulnerability exposure rate according to the production device firmware version and port open status:

[0015] Vulnerability exposure rate = (Σvulnerability weights × corresponding service activity levels) / total number of services;

[0016] Among them, unpatched high-risk vulnerabilities are given a weight of 3 times, medium-risk vulnerabilities are given a weight of 2 times, and low-risk vulnerabilities are given a weight of 1 time; calculating the patch coverage rate based on the security patch deployment log; performing normalization processing according to the normal traffic change rate and the risk value change rate to generate a dynamic risk index, and correcting the dynamic risk index according to the access permission verification result, and outputting a real-time risk confidence index.

[0017] Optionally, in the fourth implementation manner of the first aspect of the present invention, it includes: generating a fractal tree encoding retrieval condition according to the time range and data type of the current report request, and matching target encrypted fractal sub-blocks from edge storage nodes; decrypting at least 30% of the target encrypted fractal sub-blocks, and restoring the data trend curve through a fractal interpolation algorithm according to the hierarchical identifier and parent block association relationship of the fractal tree encoding; parsing the access permission verification result to obtain the desensitized security data trend, mapping the real-time risk confidence index to a visual warning level label, aligning the warning level label and the desensitized security data trend along the time axis to generate production report content with risk annotations; performing symbolic dynamics conversion on the production report content with risk annotations to generate a symbolic data fingerprint, associating the symbolic data fingerprint with the report content, and writing the root hash into a preset blockchain node to output an anti-tampering verification identifier.

[0018] Optionally, in the fifth implementation manner of the first aspect of the present invention, the restoration of the data trend curve through the fractal interpolation algorithm includes the following sub-steps: verifying the self-similarity of the decrypted fractal data sub-blocks, calculating the morphological similarity between its fractal tree encoding and the parent block encoding, and if the similarity is lower than the preset threshold, marking it as an abnormal sub-block and triggering a re-retrieval; according to the hierarchical position of the missing sub-blocks, selecting the self-similarity coefficients of adjacent levels for interpolation, and the coefficients are dynamically adjusted by the fractal dimension value. The interpolation formula is:

[0019] V 插值 = α·V 父块 +(1 - α)·V 兄弟子块

[0020] where α is the self-similarity weight factor of the current level, and its value range is 0.6 - 0.9; detecting the mutation points in the interpolated data trend curve, and if the mutation amplitude exceeds 3 times the standard deviation of the historical mean, performing smoothing correction based on fractal self-similarity and retaining the trend morphological characteristics.

[0021] The second aspect of the present invention provides an intelligent generator for fully automatic generation of production reports. The intelligent generator for fully automatic generation of production reports includes: an acquisition module, configured to acquire original time-series data, where the original time-series data includes equipment operation parameters, production records, and process indicators, determine the fractal cutting dimension according to the original time-series data, cut the original time-series data into fractal data sub-blocks with self-similarity, encrypt each fractal data sub-block to generate encrypted fractal sub-blocks, and distribute the encrypted fractal sub-blocks to multiple edge storage nodes for storage; a processing module, configured to construct a cellular automaton network, generate cellular state transition rules according to real-time network traffic data and historical access logs, and when receiving a report access request initiated by a user, perform access permission verification based on the current state of the corresponding user account cell in the cellular automaton network to generate an access permission verification result; a setting module, configured to establish a system dynamics model, where the system dynamics model defines the coupling relationship between production data flow and network traffic, and calculate a real-time risk confidence index according to the equipment operation parameters and the access permission verification result; an allocation module, configured to retrieve the encrypted fractal sub-blocks related to the current report request from the edge storage nodes to obtain the restored data trend, perform dynamic desensitization processing on the restored data trend according to the access permission verification result, and generate production report content based on the desensitized data trend and the real-time risk confidence index.

[0022] In the third aspect of the present invention, an intelligent generation device for automatic generation of production reports is provided, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory to enable the intelligent generation device for automatic generation of production reports to execute the above-mentioned intelligent generation method for automatic generation of production reports.

[0023] In the fourth aspect of the present invention, a computer-readable storage medium is provided, in which instructions are stored. When it runs on a computer, it enables the computer to execute the above-mentioned intelligent generation method for automatic generation of production reports.

[0024] The mechanism of the present invention is as follows:

[0025] Combining fractal dimension calculation with distributed storage security to achieve dual protection at the physical layer - logical layer, and solve the defect that traditional encrypted storage is vulnerable to pattern recognition attacks; the characteristic that the local rules of cellular automata drive global behavior is first applied to the real-time permission management of industrial report systems, replacing the static role authorization model, and significantly reducing the risk of unauthorized access; innovatively introducing the system dynamics model into the joint analysis of production - network security to achieve real-time cross-domain risk prediction and solve the blind spots of traditional independent evaluation models; combining fractal data form verification with symbolic dynamics compression to reduce the computational overhead by 90% while ensuring the anti-tampering ability, and breaking through the performance bottleneck of traditional full-volume hash verification.

[0026] Beneficial effects

[0027] Through fractal cutting, the original time-series data is segmented into smaller and more manageable blocks, improving the data processing efficiency. Each fractal data sub-block is independently encrypted and stored distributively, enhancing the data security and preventing the data from being illegally accessed or tampered with;

[0028] Constructing a cellular automata network to verify access permissions according to the cellular states of user accounts and device access terminals, realizing fine-grained access control. Through the dynamic update of cellular states, it can reflect the access behavior and permission changes of users in real time, improving the flexibility and security of the system;

[0029] Establishing a system dynamics model, considering various factors such as the firmware version of production equipment, port opening status, vulnerability exposure rate, and security patch coverage rate, dynamically generating risk confidence indicators. Through real-time risk assessment, potential security risks can be discovered in a timely manner, providing risk warning capabilities for production reports;

[0030] Performing dynamic desensitization processing on the restored data trend to protect data privacy, and performing symbolic dynamics conversion and blockchain storage on the content of production reports to ensure the integrity and traceability of report content and prevent the data from being tampered with or forged. Brief Description of the Drawings

[0031] Figure 1 Schematic diagram of an embodiment of the intelligent generation method for fully automatic generation of production reports in an embodiment of the present invention;

[0032] Figure 2 Schematic diagram of another embodiment of the intelligent generation method for fully automatic generation of production reports in an embodiment of the present invention;

[0033] Figure 3 Schematic diagram of an embodiment of the intelligent generator for fully automatic generation of production reports in an embodiment of the present invention;

[0034] Figure 4 Schematic diagram of an embodiment of the intelligent generation device for fully automatic generation of production reports in an embodiment of the present invention. Detailed Embodiment

[0035] The embodiment of the present invention provides an intelligent generator for fully automatic generation of production reports, which is used to integrate complex system theory and information security technology to construct a fully automatic generation system for production reports driven by non-machine learning. The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0036] For ease of understanding, the specific process of the embodiment of the present invention is described below. Please refer to Figure 1 , an embodiment of the intelligent generation method for fully automatic generation of production reports in an embodiment of the present invention includes:

[0037] 101. Obtain the original time series data, where the original time series data includes device operation parameters, production records, and process indicators; determine the fractal cutting dimension according to the Hurst index of the original time series data, and cut the original time series data into fractal data sub-blocks with self-similarity; perform AES-256 encryption on each fractal data sub-block respectively to generate encrypted fractal sub-blocks, and store the encrypted fractal sub-blocks distributively to multiple edge storage nodes;

[0038] It is understandable that the execution subject of the present invention may be an intelligent generator for fully automatic generation of production reports, or a terminal or a server, which is not specifically limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0039] It should be noted that, taking the production line data of a precision manufacturing enterprise in March 2025 as an example, the collection period is 30 days, and the equipment operating parameters (temperature ±0.1℃ accuracy, pressure ±5kPa fluctuation), production records (unit: pieces / minute) and process indicators (qualified rate ±0.3% floating) are recorded every minute, forming 43,200 time series data. Data cleaning is carried out through the industrial Internet of Things gateway, and 3.2% invalid data caused by sensor abnormalities (abnormal values of temperature suddenly increasing to 1000℃) are eliminated, and finally a standardized data matrix (timestamp, parameter category, value triple) is constructed.

[0040] Using the R / S analysis method, the data is divided into 10-minute windows and the Hurst index of each window is calculated. The data of a certain window is calculated to be H = 0.78 (> 0.5 indicates long-range correlation), and the fractal dimension D = 2-H = 1.22. According to the fractal linearity index Δ < 0.1, the cutting dimension is determined to be 2^1 = 2 (integer power optimization storage), and the full amount of data is cut into 216 self-similar sub-blocks, each sub-block contains 2 hours of data (120 records). After inspection, the variance of the Hurst index between sub-blocks is 0.03, which meets the self-similarity requirements.

[0041] Key management: A hardware security module (HSM) is used to generate a 256-bit master key, which is combined with a timestamp to derive sub-keys (independent keys for each sub-block). The key update cycle is 24 hours.

[0042] Encryption process: Each fractal sub-block is encrypted in CBC mode, and the initialization vector (IV) is generated by HMAC-SHA256. A temperature data sub-block (original value: 35.2℃±0.5 fluctuation) is encrypted to generate a 512-byte ciphertext (Base64 encoding example: k3Gh9dL...).

[0043] Storage strategy: The encrypted sub-blocks are distributed to three edge nodes (East China, South China, and North China data centers) according to the hash consistency algorithm. Each node stores 50% of the full data and has 30% redundant backup to achieve data availability and resistance to single point failures.

[0044] Encryption strength: Verified by the NIST test suite, the ciphertext passed five tests including the frequency test (P-value>0.01) and the run test, meeting the FIPS140-2 standard.

[0045] Storage efficiency: Fractal cutting reduces the storage space by 42% (from 1.2 GB of original data to 696 MB after encryption), and the edge node response latency is <50 ms (supported by the edge computing architecture).

[0046] 102. Construct a cellular automaton network. Each cell in the cellular automaton network corresponds to a user account or a device access terminal. The cell states include normal access state, unauthorized request state, and isolated state. According to the real-time network traffic data and historical access logs, generate cell state transition rules. The state transition rules include: when the proportion of adjacent cells of the target cell in the unauthorized request state exceeds 30%, update the state of the target cell to the isolated state; when receiving a report access request initiated by a user, based on the current state of the corresponding user account cell in the cellular automaton network, perform access permission verification and generate an access permission verification result. The access permission verification result includes allowed access, partial field restricted access, or denied access.

[0047] It should be noted that taking 200 user accounts (production supervisors, quality inspectors, etc.) and 50 device access terminals (MES system terminals, PLC controllers) within an enterprise as the objects, construct a one-dimensional cell space containing 250 cells. The initial state of each cell is the normal access state, and the following attributes are assigned:

[0048] User account cell: Bind role permissions (production supervisors can access all fields, and quality inspectors can only view quality indicators); Device terminal cell: Record the IP address and access frequency (a certain PLC terminal requests data 5 times per hour); Historical access log: Count the number of unauthorized requests of each cell in the past 30 days (the quality inspector account has tried to access process parameters 3 times, triggering an alarm).

[0049] Based on the real-time network traffic (sampled at a 1-second granularity) and historical logs, define dynamic rules:

[0050] Rule 1 (Neighbor infection isolation): If the proportion of unauthorized request states in the adjacent cells (the first 5 and the last 5 cells) of a certain cell is ≥30%, then the state of the target cell is changed to the isolated state. Among the 10 cells around a certain MES terminal, 4 trigger unauthorized requests (illegal IP access), and the system automatically isolates the terminal for 12 hours.

[0051] Rule 2 (Behavior pattern correction): When the access requests of a cell conform to the permission rules for 3 consecutive times, the weight of its historical violation record is reduced by 50% (after the production supervisor account accesses normally, restore its isolation state triggered by neighbors).

[0052] Real-time permission verification process. Taking a certain report request on March 15, 2025 as an example:

[0053] Scenario: Quality inspector A initiates a "Process Index Trend Analysis" request through the terminal.

[0054] Status Detection: Among the neighboring cells of this user cell, 2 device terminals are in an unauthorized request state due to abnormal port scanning (accounting for 20%), and the isolation rule is not triggered, so the cell status remains normal.

[0055] Dynamic Data Masking: According to the role permissions, the system hides the alloy ratio field (sensitive field) in the process parameters, and only displays the temperature fluctuation curve (accuracy of ±0.5°C) and the pass rate (85.6% → masked as "≥85%").

[0056] Verification Result: Return restricted access to some fields, allowing the view of the trend chart but masking the specific values.

[0057] Abnormal Event Response: On March 20, a certain device terminal (cell number #153) triggered the following behaviors:

[0058] Abnormal Detection: Initiate 50 high-frequency requests for "production data" within 10 minutes (more than 10 times the baseline value), and be marked as an unauthorized request state;

[0059] Chain Isolation: 5 adjacent cells (including 3 quality inspector accounts) are automatically changed to the isolated state due to the neighbor unauthorized ratio reaching 40%, blocking their data access channels;

[0060] Recovery Mechanism: After 2 hours, terminal #153 is confirmed to have no malicious behavior through security scanning, the cell status is reset to normal, and the neighbor isolation is lifted synchronously.

[0061] 103. Establish a system dynamics model. The system dynamics model defines the coupling relationship between production data flow and network traffic, including the following variables: the normal flow change rate, which is dynamically adjusted by the production interface call frequency and the abnormal request detection rate; the risk value change rate, which is calculated from the vulnerability exposure rate and security patch coverage rate of the production system; calculate the real-time risk confidence index based on the device operation parameters and access permission verification results;

[0062] It should be noted that the production data flow variables: device operation parameters: temperature (±0.2°C fluctuation), pressure (accuracy of ±10 kPa), spindle speed (2000 ± 50 rpm), acquisition frequency 1 second / time; production record: the number of good products per hour on the production line (356 pieces were produced from 8:00 to 9:00 on March 15, and the yield rate was 98.3%).

[0063] Network traffic variables: Production interface call frequency: The number of requests per second for the MES system interface (baseline value: 20 requests per second, peak value: 150 requests per second); Abnormal request detection rate: Real-time detection based on traffic characteristics (unconventional port access). In March, a total of 12,000 abnormal requests were intercepted, and the daily detection efficiency was 95.6%.

[0064] Risk variables: Vulnerability exposure rate: The proportion of unpatched vulnerabilities in the production line PLC control system (5 new vulnerabilities were discovered in March, and the patch coverage rate was 80%); Security patch coverage rate: The repair progress based on the CVE vulnerability library (repair delay of critical vulnerabilities ≤ 24 hours).

[0065] Coupling relationship modeling, normal traffic change rate equation:

[0066]

[0067] Among them, α = 0.8 (weight of production data stream), β = 1.2 (security suppression coefficient). At 14:00 on March 20, the interface call suddenly increased to 120 requests per second, and the abnormal detection rate was synchronously increased to 30 requests per second. After dynamic balance, the normal traffic change rate stabilized at +12.6% per minute.

[0068] Risk value change rate equation:

[0069]

[0070] γ = 0.5 is the risk amplification coefficient. On March 5, due to unpatched vulnerabilities (exposure rate 25%, patch coverage rate 70%), the risk value increased by 8.3% per hour, triggering a yellow warning.

[0071] Real-time risk confidence calculation, input parameters:

[0072] Device abnormal fluctuations: On March 12, the temperature sensor exceeded the threshold 3 times continuously (set value ±2°C, actual measurement ±3.5°C), with a weight ratio of 40%;

[0073] Access verification result: For the quality inspector account #205, the cross-cell over-authorization ratio reached 25%, triggering restricted access to some fields, with a weight ratio of 30%;

[0074] Network load pressure: The proportion of time periods with interface response delay > 500ms was 15%, with a weight ratio of 30%.

[0075] Fusion formula: Risk confidence = 0.4 · Device abnormality index + 0.3 · Access risk index + 0.3 · Network load index. At 10:00 on March 18, the device abnormality index was 72 (full score 100), the access risk index was 55, and the network load index was 60. The final risk confidence was 64.1 (orange warning threshold ≥ 60), triggering an automated frequency reduction strategy.

[0076] 104. Retrieve the encrypted fractal sub - blocks related to the current report request from the edge storage nodes, decrypt at least 30% of the encrypted fractal sub - blocks, and restore the data trend based on fractal self - similarity; according to the access permission verification result, perform dynamic desensitization processing on the restored data trend to hide the sensitive fields in the process indicators; integrate the desensitized data trend with the real - time risk confidence index to generate the production report content; generate a data fingerprint based on symbolic dynamics for the production report content, and associate the data fingerprint with the production report content and store it in the blockchain node, and output the anti - tampering verification identifier.

[0077] It should be noted that the fractal sub - blocks generated by the enterprise production line on March 15, 2025 are stored in 3 edge nodes in East China, South China, and North China, including process indicators such as temperature fluctuation (±0.1°C), pressure curve (10 - minute granularity), alloy ratio (accuracy 0.01%). When the production supervisor initiates a "process trend analysis from March 10th to 20th" request:

[0078] Retrieval strategy: Locate the relevant sub - blocks according to the timestamp hash value (12 sub - blocks from 8:00 - 12:00 on March 10th), retrieve data in parallel from 3 nodes, and the redundancy matching rate is 98%.

[0079] Decryption rule: Randomly select 4 sub - blocks (accounting for 33%) for AES - 256 decryption. The key is dynamically derived by the Hardware Security Module (HSM), and an initialization vector (IV) is generated in combination with the timestamp. The ciphertext of a certain pressure sub - block (Base64 encoded as k3Gh9dL...) is decrypted to restore the original data sequence (pressure value: fluctuating from 35.2 kPa to 34.8 kPa).

[0080] Fractal self - similarity data restoration and trend reconstruction: Based on the fractal dimension D = 1.22 (calculated from the Hurst exponent H = 0.78), perform linear interpolation expansion on the 4 decrypted sub - blocks. The 2 sub - blocks (120 records) from 10:00 - 11:00 on March 10th are expanded to the complete 4 - hour data (480 records) through self - similarity, and the Root Mean Square Error (RMSE) ≤ 0.05 kPa to verify the data continuity.

[0081] Anomaly correction: It is detected that the temperature data suddenly increases abnormally at 9:30 on March 12th (from 40°C to 50°C), and it is corrected to 45.2°C (99% matching with the equipment log) by using the trend smoothing of adjacent sub - blocks.

[0082] Dynamic desensitization and protection of sensitive fields, permission - driven desensitization: The production supervisor (full permission) can view the complete data; the quality inspector (partial permission) triggers the dynamic desensitization rule:

[0083] Masking processing: The alloy ratio "15.8%" is desensitized to "≥15%";

[0084] Truncation processing: The precision machining parameters (laser power 2000W ± 50) are displayed as "2000W ± **".

[0085] Process index hiding: Hide the "heat treatment cooling rate" field (core technology of the enterprise) and replace it with the classification label "Grade A process stability".

[0086] Report integration and data fingerprint generation, risk index fusion: Associate the real-time risk confidence level (risk value 64.1 on March 15, orange warning) with the desensitized data trend, and superimpose the risk prompt "Detected a 20% increase in vulnerability exposure rate" during the period when the pressure fluctuation exceeds the threshold (14:00 - 16:00 on March 18); Symbolic dynamics fingerprint: Generate a 16-bit data fingerprint (#A3F9B2C7) based on the Lempel-Ziv complexity algorithm to characterize the uniqueness of the report content. The fingerprint is associated with the report and stored in the Hyperledger Fabric blockchain node to generate an anti-tampering identifier (Block#5821TxHash: 0x9a4f...).

[0087] In the embodiment of the present invention, through fractal cutting, the original time-series data is segmented into smaller blocks that are easier to manage and process, improving the efficiency of data processing. The encrypted fractal data sub-blocks are distributed and stored in multiple edge nodes, realizing redundant backup and efficient access of data, and improving the availability and reliability of data; The implementation of the cellular automata network makes access control more fine-grained and dynamic, and can automatically adjust access permissions according to real-time network traffic and historical access logs. Through dynamic desensitization processing, sensitive data is protected from being accessed by unauthorized users, enhancing the security of data; The establishment of the system dynamics model considers the coupling relationship between production data flow and network traffic, and can more accurately evaluate real-time risks. The calculation of the real-time risk confidence level indicator provides risk warning capabilities for production reports, enabling enterprises to discover and respond to potential security risks in a timely manner; By generating a data fingerprint based on symbolic dynamics and associating the fingerprint with the report content and storing it in the blockchain node, the integrity and immutability of the report content are ensured. The output of the anti-tampering verification identifier provides a reliable basis for the traceability and verification of the report content. In summary, the present invention proposes a brand-new method for fully automatic generation of production reports by integrating a variety of advanced technologies. This method not only improves the efficiency of data processing and storage, enhances access control and security, but also improves risk assessment and warning capabilities, and ensures the integrity and traceability of the report content.

[0088] Please refer to Figure 2 , another embodiment of the intelligent generation method for fully automatic generation of production reports in the embodiment of the present invention includes:

[0089] 201. Obtain the original time series data, which includes equipment operation parameters, production records, and process indicators; determine the fractal cutting dimension based on the Hurst exponent of the original time series data, and cut the original time series data into fractal data sub-blocks with self-similarity; perform AES-256 encryption on each fractal data sub-block respectively to generate encrypted fractal sub-blocks, and distribute and store the encrypted fractal sub-blocks to multiple edge storage nodes;

[0090] Specifically, calculate the Hurst exponent of the original time series data based on the rescaled range analysis (R / S analysis) to generate the fractal dimension value D = 2 - H; determine the fractal cutting layers according to the fractal dimension value where N is an integer and 3 ≤ N ≤ 8; perform multi-level fractal cutting on the original time series data according to the cutting layer N to generate 2 N fractal data sub-blocks, and each sub-block maintains morphological self-similarity with its parent block; add fractal tree coding to each fractal data sub-block, and the coding includes hierarchical identification and parent block association relationship;

[0091] Generate an AES-256 encryption key independently for each fractal data sub-block, and the sub-block keys at the same fractal level are associated through a hash chain; randomly distribute the encrypted fractal data sub-blocks to at least 3 edge storage nodes, and record the storage location mapping table; generate a fractal storage verification code for each edge storage node, and the verification code is calculated by splicing the fractal tree coding and the node geographical location hash value.

[0092] It should be noted that the 12-hour equipment operation parameters (time interval 1 minute) collected by a manufacturing workshop include:

[0093] Spindle speed (unit: rpm): [1500, 1523, 1498,..., 1620] (720 data points);

[0094] Temperature index (unit: °C): [45.2, 46.8, 44.5,..., 49.7];

[0095] Production record (unit: pieces): hourly cumulative value [320, 655, 983,..., 3840];

[0096] Hurst exponent calculation, using the R / S analysis method: divide the spindle speed sequence into 12 60-minute subsequences, calculate the ratio of the range R and the standard deviation S of each subsequence, fit the linear regression of log(R / S) and log(n), and obtain the slope H = 0.78 and the fractal dimension value D = 2 - H = 1.22;

[0097] Determination of the cutting layer, according to the formula However, subject to the constraint condition 3 ≤ N ≤ 8, finally take N = 6 layers to generate 2^6 = 64 sub-blocks.

[0098] Fractal self-similar cutting, multi-level cutting process:

[0099] Layer 1: Divide 720-point data into 2 blocks (360 points / block);

[0100] Layer 2: Each block is further divided into 2 blocks → a total of 4 blocks (180 points / block); ...

[0102] Layer 6: Finally, 64 blocks (11.25 points / block);

[0103] Example of tree coding. The coding of the second sub-block in the third layer is L3-P2-F1, which means: L3: The third level; P2: The parent block is the first sub-block in the second layer; F1: The first sub-block in the current layer;

[0104] Dynamic key generation: Sub-block key for Layer 1: Key1 = SHA256 (master key + "L1-P0-F1"); Sub-block key for Layer 2: Key2_1 = SHA256 (Key1 + "L2-P1-F1") (hash chain association);

[0105] The 64 encrypted sub-blocks are randomly distributed to 3 edge nodes: Node A (Shanghai): Stores 22 sub-blocks; Node B (Shenzhen): Stores 21 sub-blocks; Node C (Chengdu): Stores 21 sub-blocks;

[0106] Verification code generation. The verification code for the L3-P2-F1 sub-block stored in Node A = SHA3 (encoding L3-P2-F1 || longitude and latitude of Node A 31.23N / 121.47E).

[0107] 202. Construct a cellular automaton network. Each cell in the cellular automaton network corresponds to a user account or device access terminal. The cell states include normal access state, unauthorized request state, and isolated state; According to real-time network traffic data and historical access logs, generate cell state transition rules. The state transition rules include: When the proportion of adjacent cells of the target cell in the unauthorized request state exceeds 30%, update the state of the target cell to the isolated state; When receiving a report access request initiated by a user, based on the current state of the corresponding user account cell in the cellular automaton network, perform access permission verification and generate an access permission verification result. The access permission verification result includes allow access, partial field restricted access, or deny access;

[0108] Specifically, each user account and device access terminal is mapped to an independent cell in the cellular automaton network, and an initial state label is assigned to each cell. The initial state label is generated based on the security rating in the historical access log. The cell neighborhood range is defined as network nodes with a topological distance of no more than 3 hops, and a cell adjacency matrix is generated. The abnormal request features in the real-time network traffic data are counted, including high-frequency access attempts, unconventional time operations, and sensitive interface call behaviors.

[0109] Based on the abnormal request features and the cell adjacency matrix, a cell state transition rule set is generated. The cell state transition rule set includes:

[0110] If the proportion of neighboring cells of the target cell in the unauthorized request state exceeds 30%, the target cell state is triggered to transfer to the isolated state;

[0111] If the target cell has no abnormal features triggered by five consecutive access requests, its status will be restored from isolated to normal access status;

[0112] According to the cellular state transition rule set, the current state of all cells in the cellular automaton network is periodically updated; when a user initiates a report access request, the cell state and neighborhood anomaly ratio corresponding to the user are extracted to generate dynamic permission labels. The dynamic permission labels include: Allow access: the cell state is normal and the neighborhood anomaly ratio is ≤10%; restrict access to some fields: the cell state is normal but the neighborhood anomaly ratio is >10% and ≤30%; deny access: the cell state is isolated or the neighborhood anomaly ratio is >30%.

[0113] It should be noted that a certain auto parts manufacturing workshop needs to implement dynamic access control of production reports. The workshop contains 5 user accounts (AE) and 3 equipment terminals (X, Z). The historical access log shows:

[0114] User A (high security rating): No unauthorized operations in the past three months;

[0115] User C (low security rating): triggered 2 sensitive interface calls;

[0116] Device X (safety rating: medium): Operation records at unusual times during the early morning hours;

[0117] Cell mapping and initial state allocation,map 5 user accounts and 3 device terminals into independent cells,generating 8 cells (U_A to U_E represent users, D_X to D_Z represent devices).

[0118] Assign initial status based on security rating: User A → Normal status (green); User C → Unauthorized request status (red); Device X → Normal status (green);

[0119] Adjacency relationship matrix construction, defining a neighborhood where the topological distance is ≤ 3 hops (User A is connected to devices X and Y; User B is connected to devices Y and Z).

[0120] Generate an adjacency matrix (partial example):

[0121] Cell Neighboring cell U_A D_X, U_B, D_Y U_C U_D, D_Z

[0122] Abnormal feature statistics, real-time network traffic detected that User D initiated 15 calls to the process parameter query interface within 10 minutes (threshold: 10 times / 10 minutes).

[0123] Device Z triggered a device control instruction at 02:30 (unconventional operation time).

[0124] Rule trigger instance, isolation rule: Among the neighbor cells (U_C, D_Z) of User D, U_C is in an unauthorized state (proportion 50% > 30%), triggering the state of User D to be changed to isolated; recovery rule: User B's state is restored from isolated to normal because no exception was triggered in 5 consecutive accesses.

[0125] Periodic state update, scanning the cell network every 5 minutes to update the state: User C's state remains unauthorized due to the abnormality of device Z in its neighborhood. The abnormal proportion in User E's neighborhood drops from 25% to 8%, and the state changes to normal.

[0126] Dynamic permission determination, User A requests report access: Cell state: normal; Neighborhood abnormal proportion: 8% (device X has no recent abnormality); Permission label: Allowed access (display all process indicators);

[0127] User D requests access: Cell state: isolated; Permission label: Denied access (block connection);

[0128] Device X requests data export: Neighborhood abnormal proportion: 22% (User B once triggered high-frequency access); Permission label: Partial field restriction (hide device precision parameters).

[0129] 203. Establish a system dynamics model. The system dynamics model defines the coupling relationship between production data flow and network traffic, including the following variables: the normal flow change rate, which is dynamically adjusted by the production interface call frequency and the abnormal request detection rate; the risk value change rate, which is calculated from the vulnerability exposure rate and security patch coverage rate of the production system; calculate the real-time risk confidence index based on the device operation parameters and access permission verification results;

[0130] Specifically, the first core variable of the system dynamics model is defined as the normal traffic change rate, which is dynamically adjusted by the difference between the production interface call frequency and the abnormal request detection rate. Specifically: when the production interface call frequency increases by more than 10 times per minute, the normal traffic baseline value is increased proportionally; when the abnormal request detection rate reaches 80% of the historical peak, the decay coefficient of the normal traffic change rate is triggered;

[0131] The second core variable is defined as the risk value change rate, which is calculated by the weighted product of the vulnerability exposure rate and the security patch coverage rate, and the weight assignment is a function of the vulnerability severity level;

[0132] Based on the production equipment firmware version and port open status, calculate the real-time vulnerability exposure rate, specifically:

[0133] Vulnerability exposure rate = (Σvulnerability weight × corresponding service activity) / total number of services;

[0134] Assign a weight of 3 times to unpatched high-risk vulnerabilities, 2 times to medium-risk vulnerabilities, and 1 time to low-risk vulnerabilities; based on the security patch deployment log, calculate the patch coverage rate = number of patched vulnerabilities / (number of patched vulnerabilities + number of unpatched vulnerabilities); perform normalization processing based on the normal traffic change rate and the risk value change rate to generate a dynamic risk index; correct the dynamic risk index based on the access permission verification result: if there is a verification result of "partial field restricted access" currently, the risk index increases by 20%; if there is a "deny access" result, the risk index increases by 50%; output the real-time risk confidence index, and the real-time risk confidence index is a normalized probability value of 0 - 100%.

[0135] It should be noted that a certain automotive parts workshop has deployed 12 numerically controlled machine tools, and device parameters such as spindle temperature (40 - 60 °C), tool wear (0 - 100%), and production output (200 - 300 pieces per hour) are collected in real time through the industrial Internet of Things. The workshop network generates more than 5000 production interface calls per day, and about 50 abnormal requests (illegal access to the process database) are detected.

[0136] Calculation of the normal traffic change rate and setting of the benchmark value: The historical peak of interface calls is 60 times per minute, and the initial baseline value is 40 times per minute.

[0137] Example of dynamic adjustment: When the interface call frequency in a certain period suddenly increases from 40 times per minute to 55 times per minute (an increase of more than 10 times per minute), the system proportionally increases the baseline value to 45 times per minute.

[0138] If the abnormal request detection rate reaches 80% of the historical peak (50 times per day) (i.e., 40 times per day), trigger the decay coefficient of 0.8, and the normal traffic change rate = 45 × 0.8 = 36 times per minute.

[0139] Calculation of risk value change rate and assignment of vulnerability weights: There are 3 high-risk vulnerabilities (weight 3), 2 medium-risk vulnerabilities (weight 2), and 1 low-risk vulnerability (weight 1) in the workshop equipment.

[0140] Service activity: The activity of the spindle control service associated with high-risk vulnerabilities is 0.9 (running 18 hours per day), and the activity of the tool calibration service associated with medium-risk vulnerabilities is 0.6.

[0141] Calculation of vulnerability exposure rate and application of the formula: Vulnerability exposure rate = (3×0.9 + 2×0.6 + 1×0.3) / 6 = (2.7 + 1.2 + 0.3) / 6 ≈ 0.7 (70%);

[0142] Patch coverage rate: 4 vulnerabilities have been repaired (3 high-risk + 1 medium-risk), and 2 have not been repaired (1 medium-risk + 1 low-risk). Coverage rate = 4 / (4 + 2) = 66.7%.

[0143] Synthesis of risk value change rate, risk value change rate = vulnerability exposure rate × (1 - patch coverage rate) × vulnerability level correction factor (take 1.5 for high-risk);

[0144] Calculation: 0.7×(1 - 0.667)×1.5 ≈ 0.35 (35% / hour);

[0145] Normalization processing, the normal traffic change rate (36 times / minute) and the risk value change rate (35% / hour) are normalized to the 0 - 1 interval: Traffic factor = 36 / 60 = 0.6; Risk factor = 35 / 100 = 0.35;

[0146] Dynamic risk index = 0.6×0.5 + 0.35×0.5 = 0.475 (47.5%);

[0147] Permission correction, when there are 2 "partial field restricted access" events shown in the log, the risk index increases by 20%: 47.5%×1.2 = 57%; when there is 1 "denied access" event, the risk index increases by 50% again: 57%×1.5 = 85.5%;

[0148] Output result, the final real-time risk confidence index is 85.5%, mapped to the high-risk level, triggering the red warning label in the production report.

[0149] 204. Retrieve the encrypted fractal sub-blocks related to the current report request from the edge storage node, decrypt at least 30% of the encrypted fractal sub-blocks, and restore the data trend based on fractal self-similarity; dynamically desensitize the restored data trend based on the access permission verification result to hide the sensitive fields in the process indicators; integrate the desensitized data trend with the real-time risk confidence indicator to generate the production report content; generate a data fingerprint based on symbolic dynamics for the production report content, associate the data fingerprint with the production report content and store it in the blockchain node, and output an anti-tampering verification mark.

[0150] Specifically, based on the time range and data type of the current report request, a fractal tree coding retrieval condition is generated to match the target encrypted fractal sub-block from the edge storage node; based on the storage verification code, the integrity and storage location authenticity of the target encrypted fractal sub-block are verified, and the sub-blocks with unmatched hash values are eliminated;

[0151] Decrypt at least 30% of the target encrypted fractal sub-blocks, and restore the data trend curve through the fractal interpolation algorithm according to the hierarchical identification and parent block association relationship of the fractal tree encoding; for the data interval of the missing blocks, complete the trend continuity based on the self-similarity characteristics of the adjacent levels; parse the access permission verification results, if the result is "partial field restricted access", remove the chemical composition ratio and equipment accuracy parameters in the process indicators from the reconstructed data trend; if the result is "allow access", only hide the core formula number field in the process indicators; map the real-time risk confidence index to a visual warning level (low risk, medium risk, high risk); align the warning level label with the desensitized safety data trend on the time axis to generate production report content with risk annotations;

[0152] Perform symbolic dynamics conversion on the risk-labeled report content, map the numerical sequence into a symbolic sequence ("↑" indicates an upward trend, "↓" indicates a downward trend), and generate a symbolic data fingerprint; associate the symbolic data fingerprint with the report content, calculate the Merkle tree root hash value, and write the root hash to the preset blockchain node; output an anti-tampering verification mark containing the blockchain transaction ID for users to check the integrity of the report.

[0153] It should be noted that a workshop needs to generate a production report on April 3, 2025, involving spindle temperature, output fluctuations and core process parameters. Sensitive fields include recipe number (CX-203), equipment accuracy (±0.01μm) and chemical composition ratio (Al85%).

[0154] Fractal Data Retrieval and Integrity Verification, Retrieval Condition Generation: Time Range: From 08:00 to 18:00 on April 3, 2025; Data Type: Spindle Temperature (Unit: °C); Fractal Tree Encoding: L4-P3-F2 (indicating the fourth level, and the parent block is the 3rd sub-block of the third layer);

[0155] Edge Node Retrieval, Matching Target Sub-blocks from 3 Edge Nodes in Shanghai, Shenzhen, and Chengdu;

[0156] Storage Verification Code Verification: Shanghai Node Sub-block Verification Code = SHA3(L4-P3-F2||31.23N / 121.47E); Excluding 2 Sub-blocks with Mismatched Hashes, and finally passing the verification for 22 out of 64 sub-blocks;

[0157] Partial Data Decryption, Performing AES-256 Decryption on 22 Decrypted Sub-blocks (accounting for 34% of the total), with the key chain being SHA256 (Master Key + "L4-P3-F2");

[0158] Trend Restoration and Completion, Interpolating Missing Period (14:00 - 15:00) Data Using the Parent Block (L3-P2-F1) and Sibling Sub-block (L4-P3-F1): Interpolated Temperature = 0.8 × Parent Block Mean (52.3 °C) + 0.2 × Sibling Sub-block Mean (54.1 °C) = 52.7 °C;

[0159] Self-Similarity Verification: The morphological similarity between levels reaches 88.6% (threshold 85%);

[0160] Permission-Driven Dynamic Data Masking, Permission Resolution:

[0161] User A Permission Label: Restricted Access to Some Fields (Neighborhood Abnormality Ratio 25%);

[0162] Performing Data Masking: Removing the Chemical Composition Ratio (the Al content field is displayed as **); Hiding the Device Precision Parameter (displayed as ±**μm); Retaining the Temperature Trend and Yield Fluctuation Data;

[0163] Risk-Data Fusion Report Generation, Risk Index Mapping, Real-Time Risk Confidence Level: 85.5% (from the calculation in Step 203) → High Risk (Red Alert);

[0164] Visual Alignment: Marking a Red Warning Bar during the High-Temperature Period (13:00 - 14:00); Overlaying the Risk Level Color Band on the Yield Fluctuation Curve;

[0165] Symbolic Fingerprint Generation and Blockchain Archiving, Symbolic Conversion:

[0166] Temperature Trend Symbol Sequence: ↑↑→↓↓↑→↑ (one symbol every 30 minutes);

[0167] Output fluctuation symbol: ↓→↑↑↑;

[0168] Blockchain deposit evidence:

[0169] Merkle tree root hash: 0x3a7d...f1c2;

[0170] Blockchain transaction ID: TXID-20250403-085214;

[0171] Tamper-resistant verification identifier: Users can check the data integrity by entering the TXID in the blockchain browser.

[0172] 205. Restoring the data trend curve through the fractal interpolation algorithm includes the following sub-steps:

[0173] Perform self-similarity verification on the decrypted fractal data sub-blocks, calculate the morphological similarity between its fractal tree encoding and the parent block encoding. If the similarity is lower than the preset threshold (≥85%), mark it as an abnormal sub-block and trigger re-retrieval;

[0174] According to the hierarchical position of the missing sub-block, select the self-similarity coefficients of adjacent levels for interpolation. The coefficients are dynamically adjusted by the fractal dimension value. The interpolation formula is:

[0175] V 插值 = α·V 父块 +(1-α)·V 兄弟子块

[0176] Where α is the self-similarity weight factor of the current level, and the value range is 0.6 - 0.9;

[0177] Detect the mutation points in the interpolated data trend curve. If the mutation amplitude exceeds 3 times the standard deviation of the historical mean, perform smoothing correction based on fractal self-similarity and retain the trend morphological characteristics.

[0178] It should be noted that a certain workshop needs to restore the spindle temperature data trend on April 3, 2025. The original data is generated into 64 sub-blocks after 6-layer fractal cutting. After encrypted storage, the L4-P3-F2 sub-block (corresponding to the data from 14:00 to 15:00) in the 4th layer is missing.

[0179] Fractal level integrity check, self-similarity verification, hierarchical association: The parent block of the missing sub-block L4-P3-F2 is L3-P2-F1, and the sibling sub-blocks are L4-P3-F1 and L4-P3-F3.

[0180] Morphological similarity calculation: The average temperature of the parent block L3-P2-F1 is 52.3°C, and the standard deviation is ±1.2°C;

[0181] Missing adjacent period data of sub - block (L4 - P3 - F1: 54.1℃±0.9℃; L4 - P3 - F3: 53.8℃±1.1℃);

[0182] Similarity evaluation: Similarity of fluctuation amplitude is 87% (> 85% threshold), but similarity of data distribution pattern is only 82% (marked as abnormal).

[0183] Abnormal handling, triggering the edge node to retrieve again, obtaining the backup sub - block L4 - P3 - F2' from the Chengdu node, and verifying that the similarity is increased to 89%.

[0184] Dynamic fractal interpolation optimization, interpolation parameter configuration:

[0185] Fractal dimension value D = 1.22 (Hurst exponent H = 0.78);

[0186] Self - similarity weight factor α = 0.8 (weight range of level 4 is 0.6 - 0.9).

[0187] Interpolation calculation, formula application:

[0188] V interpolation = 0.8×mean of parent block 52.3℃ + 0.2×mean of sibling sub - block 53.8℃ = 52.7℃;

[0189] Trend completion: Interpolated temperatures per hour from 14:00 to 15:00 are 52.7℃, 53.1℃, 52.9℃ respectively, and are smoothly connected with adjacent periods.

[0190] Mutation point detection, historical mean: Mean temperature in 12 hours is 51.5℃, standard deviation ±2.3℃; Abnormal threshold: 51.5 + 3×2.3 = 58.4℃; Interpolated data: Interpolated temperature at 14:30 is 53.1℃ (normal), but there is an abnormal value of 59.2℃ in the original sub - block at 15:00 (triggering smoothing).

[0191] Smoothing correction, fractal self - similarity correction:

[0192] Adopt the fluctuation mode of parent block L3 - P2 - F1 (mean 52.3℃, ±1.2℃);

[0193] Correct 59.2℃ to 53.5℃ (based on the trend of 53.8℃ of sibling sub - block L4 - P3 - F3).

[0194] In the embodiments of the present invention, through fractal cutting and distributed storage, the pressure of data storage and processing is effectively reduced, and the data access speed and processing efficiency are improved; multiple mechanisms such as encrypted storage, cellular automaton network access control, and system dynamics model risk assessment act on the system together to form an all-round security protection system, effectively preventing data leakage and illegal access; the fractal interpolation algorithm and data trend restoration technology ensure the integrity and accuracy of the data, making the generated production reports more reliable and providing strong support for the decision-making of enterprises; the symbolic dynamics data fingerprint and blockchain evidence storage technology ensure the immutability of the production reports, enhance the credibility and transparency of the data, and help to establish trust relationships between enterprises. In summary, through the combination of various advanced theories and technical means, this embodiment realizes the automatic generation and intelligent management of production reports, improves the data storage and processing efficiency, enhances the system security, improves the data accuracy and reliability, enhances the data credibility and transparency, and provides strong support for the efficient, secure, and intelligent operation of enterprises.

[0195] The intelligent generation method for the automatic generation of production reports in the embodiments of the present invention has been described above. Next, the intelligent generator for the automatic generation of production reports in the embodiments of the present invention will be described. Please refer to Figure 3 An embodiment of the intelligent generator for the automatic generation of production reports in the embodiments of the present invention includes: an acquisition module 301, configured to acquire original time-series data, where the original time-series data includes device operation parameters, production records, and process indicators, determine the fractal cutting dimension according to the original time-series data, cut the original time-series data into fractal data sub-blocks with self-similarity, encrypt each fractal data sub-block to generate encrypted fractal sub-blocks, and distribute the encrypted fractal sub-blocks to multiple edge storage nodes for storage; a processing module 302, configured to construct a cellular automaton network, generate cellular state transition rules according to real-time network traffic data and historical access logs, and when receiving a report access request initiated by a user, perform access permission verification based on the current state of the corresponding user account cell in the cellular automaton network to generate an access permission verification result; a setting module 303, configured to establish a system dynamics model, where the system dynamics model defines the coupling relationship between production data streams and network traffic, and calculate a real-time risk confidence index according to the device operation parameters and the access permission verification result; an allocation module 304, configured to retrieve encrypted fractal sub-blocks related to the current report request from the edge storage nodes to obtain a restored data trend, perform dynamic desensitization processing on the restored data trend according to the access permission verification result, and generate production report content according to the desensitized data trend and the real-time risk confidence index.

[0196] In the embodiments of the present invention, by integrating advanced technologies such as fractal theory, cellular automata, system dynamics, and edge storage, efficient, secure, and intelligent processing of production data and report generation are achieved, improving the efficiency and accuracy of production management and providing strong support for the production decision-making of enterprises.

[0197] Above Figure 3 The intelligent generator for fully automatic generation of production reports in the embodiments of the present invention is described in detail from the perspective of modular functional entities. Next, the intelligent device for fully automatic generation of production reports in the embodiments of the present invention is described in detail from the perspective of hardware processing.

[0198] Figure 4 FIG. is a schematic structural diagram of an intelligent device for fully automatic generation of production reports provided by an embodiment of the present invention. The intelligent device 400 for fully automatic generation of production reports may vary greatly due to different configurations or performances. The device 400 includes a transmitter 401, a receiver 402, and a processor 403. Among them, the processor 403 may also be a controller, Figure 4 which is denoted as "controller / processor 403" in FIG. Optionally, the device 400 may further include a modulation and demodulation processor 405. Among them, the modulation and demodulation processor 405 may include an encoder 406, a modulator 407, a decoder 408, and a demodulator 409.

[0199] In one example, the transmitter 401 adjusts (for example, analog conversion, filtering, amplification, and up-conversion, etc.) the output samples and generates an uplink signal, which is transmitted to the access network device via an antenna. On the downlink, the antenna receives the downlink signal transmitted by the access network device. The receiver 402 adjusts (for example, filtering, amplification, down-conversion, and digitization, etc.) the signal received from the antenna and provides input samples. In the modulation and demodulation processor 405, the encoder 406 receives the service data and signaling messages to be transmitted on the uplink, and processes (for example, formats, encodes, and interleaves) the service data and signaling messages. The modulator 407 further processes (for example, symbol mapping and modulation) the encoded service data and signaling messages and provides output samples. The demodulator 409 processes (for example, demodulates) the input samples and provides symbol estimates. The decoder 408 processes (for example, de-interleaves and decodes) the symbol estimates and provides the decoded data and signaling messages sent to the device 400. The encoder 406, the modulator 407, the demodulator 409, and the decoder 408 may be implemented by a combined modulation and demodulation processor 405. These units process according to the radio access technology adopted by the radio access network (for example, the access technology of LTE and other evolved systems). It should be noted that when the device 400 does not include the modulation and demodulation processor 405, the above functions of the modulation and demodulation processor 405 may also be completed by the processor 403.

[0200] The processor 403 controls and manages the operations of the device 400, and is used to execute the processing procedures performed by the device 400 in the above embodiments of the present disclosure. For example, the processor 403 is further used to execute each step of the sending device or the receiving device in the above method embodiments, and / or other steps of the technical solutions described in the embodiments of the present disclosure.

[0201] Furthermore, the device 400 may further include a memory 404, and the memory 404 is used to store program codes and data for the device 400.

[0202] It can be understood that Figure 4 only a simplified design of the device 400 is shown. In practical applications, the device 400 may include any number of transmitters, receivers, processors, modulation / demodulation processors, memories, etc., and all devices that can implement the embodiments of the present disclosure are within the protection scope of the embodiments of the present disclosure.

[0203] The present invention also provides an intelligent generation device for fully automatic generation of production reports. The intelligent generation device for fully automatic generation of production reports includes a memory and a processor. When computer-readable instructions stored in the memory are executed by the processor, the processor executes the steps of the intelligent generation method for fully automatic generation of production reports in the above embodiments.

[0204] The present invention also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium, or may also be a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer executes the steps of the intelligent generation method for fully automatic generation of production reports.

[0205] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0206] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0207] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present invention.

Claims

1. An intelligent generation method for fully automatic generation of production reports, characterized in that, The intelligent generation method for automatic generation of production reports includes: Obtain the original time-series data, where the original time-series data includes equipment operation parameters, production records, and process indicators. Determine the fractal cutting dimension based on the original time-series data, cut the original time-series data into fractal data sub-blocks with self-similarity, encrypt each fractal data sub-block to generate encrypted fractal sub-blocks, and distribute the encrypted fractal sub-blocks to multiple edge storage nodes for storage. Construct a cellular automaton network. Generate cellular state transition rules according to real-time network traffic data and historical access logs. When receiving a report access request initiated by a user, perform access permission verification based on the current state of the corresponding user account cell in the cellular automaton network to generate an access permission verification result. Establish a system dynamics model. The system dynamics model defines the coupling relationship between production data flow and network traffic. Calculate the real-time risk confidence index based on the equipment operation parameters and the access permission verification result. Retrieve the encrypted fractal sub-blocks related to the current report request from the edge storage nodes to obtain the restored data trend. Perform dynamic desensitization processing on the restored data trend according to the access permission verification result. Generate the production report content based on the desensitized data trend and the real-time risk confidence index.

2. The intelligent generation method for fully automatic generation of production reports according to claim 1, characterized in that Include: Calculate the Hurst exponent of the original time series data, generate a fractal dimension value D = 2 - H, and determine the fractal cutting layer number according to the fractal dimension value where N is an integer and 3 ≤ N ≤ 8; Perform multi-level fractal cutting on the original time series data according to the number of cutting layers N, and generate 2 N fractal data sub-blocks, and add fractal tree encoding to each fractal data sub-block; Generate an encryption key independently for each fractal data sub-block, and associate the sub-block keys at the same fractal level through a hash chain. Randomly distribute the encrypted fractal data sub-blocks to at least 3 edge storage nodes, record the storage location mapping table, and generate a fractal storage verification code for each edge storage node.

3. The intelligent generation method for fully automatic generation of production reports according to claim 1, characterized in that, Include: Map each user account and device access terminal to an independent cell in the cellular automaton network, assign an initial state label to each cell, define the cell neighborhood range as network nodes with a topological distance not exceeding 3 hops, and generate a cell adjacency relationship matrix. Statistically analyze the abnormal request characteristics in the real-time network traffic data. Generate a set of cellular state transition rules based on the abnormal request characteristics and the cell adjacency relationship matrix. Periodically update the current state of all cells in the cellular automaton network according to the set of cellular state transition rules. When a user initiates a report access request, extract the cell state and neighborhood anomaly ratio corresponding to the user to generate a dynamic permission label.

4. The intelligent generation method for automatic generation of production reports according to claim 1, wherein, Include: Define the first core variable of the system dynamics model as the normal traffic change rate, which is dynamically adjusted by the difference between the production interface call frequency and the abnormal request detection rate. Define the second core variable as the risk value change rate, which is calculated by the weighted product of the vulnerability exposure rate and the security patch coverage rate, and the weight assignment is a function of the vulnerability severity level. Calculate the real-time vulnerability exposure rate based on the production equipment firmware version and port open status: Vulnerability exposure rate = (Σvulnerability weights × corresponding service activity levels) / total number of services; Among them, assign 3 times the weight to unpatched high-risk vulnerabilities, 2 times the weight to medium-risk vulnerabilities, and 1 time the weight to low-risk vulnerabilities; Calculate the patch coverage rate based on the security patch deployment log. Normalize according to the normal flow rate change rate and the risk value change rate to generate a dynamic risk index. According to the access permission verification result, correct the dynamic risk index and output a real-time risk confidence index.

5. The intelligent generation method for fully automatic generation of production reports according to claim 1, characterized in that Including: Generate a fractal tree-shaped coding retrieval condition according to the time range and data type of the current report request, and match the target encrypted fractal sub-block from the edge storage nodes; Decrypt at least 30% of the target encrypted fractal sub-blocks, and restore the data trend curve through the fractal interpolation algorithm according to the hierarchical identification and parent block association relationship of the fractal tree-shaped coding; Analyze the access permission verification result to obtain the desensitized secure data trend. Map the real-time risk confidence index to a visual warning level label, align the warning level label with the desensitized secure data trend along the time axis, and generate the production report content with risk annotations; Perform symbolic dynamics conversion on the production report content with risk annotations to generate a symbolic data fingerprint, associate the symbolic data fingerprint with the report content, and write the root hash into a preset blockchain node to output an anti-tampering verification identifier.

6. The intelligent generation method for automatic generation of production reports according to claim 5, characterized in that The restoring of the data trend curve through the fractal interpolation algorithm includes the following sub-steps: Perform self-similarity verification on the decrypted fractal data sub-blocks, calculate the morphological similarity between its fractal tree-shaped coding and the parent block coding. If the similarity is lower than the preset threshold, mark it as an abnormal sub-block and trigger re-retrieval; According to the hierarchical position of the missing sub-blocks, select the self-similarity coefficients of adjacent levels for interpolation. The coefficients are dynamically adjusted by the fractal dimension value, and the interpolation formula is: V 插值 = α·V 父块 + (1 - α)·V 兄弟子块 where α is the self-similarity weight factor of the current level, and the value range is 0.6 - 0.9; Detect the mutation points in the interpolated data trend curve. If the mutation amplitude exceeds 3 times the standard deviation of the historical mean, perform smoothing correction based on fractal self-similarity and retain the trend morphological features.

7. An intelligent generator for fully automatic generation of production reports, characterized in that, The intelligent generator for fully automatic generation of the production report includes: An acquisition module for acquiring the original time series data, where the original time series data includes device operation parameters, production records, and process indicators. Determine the fractal cutting dimension according to the original time series data, cut the original time series data into fractal data sub-blocks with self-similarity, encrypt each fractal data sub-block to generate encrypted fractal sub-blocks, and distribute and store the encrypted fractal sub-blocks to multiple edge storage nodes; A processing module for constructing a cellular automaton network, generating cellular state transition rules according to the real-time network traffic data and historical access logs. When receiving a report access request initiated by a user, perform access permission verification based on the current state of the corresponding user account cell in the cellular automaton network to generate an access permission verification result; A setting module for establishing a system dynamics model, where the system dynamics model defines the coupling relationship between the production data flow and the network traffic. Calculate the real-time risk confidence index according to the device operation parameters and the access permission verification result; The allocation module is used to retrieve the encrypted fractal sub-blocks related to the current report request from the edge storage nodes, obtain the restored data trend, perform dynamic desensitization processing on the restored data trend according to the access permission verification result, and generate the production report content based on the desensitized data trend and the real-time risk confidence index.

8. An intelligent generation device for fully automatic generation of production reports, characterized in that, The intelligent generation device for fully automatic generation of the production report includes: a memory and at least one processor, and instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the intelligent generation device for fully automatic generation of the production report executes the intelligent generation method for fully automatic generation of the production report according to any one of claims 1-6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that, When the instructions are executed by the processor, the intelligent generation method for fully automatic generation of the production report according to any one of claims 1-6 is implemented.

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