Oil and Gas Internet of Things Communication Data Encryption Storage Management System

Abstract

The present application belongs to the field of information security, and particularly relates to a communication data encryption storage management system based on oil and gas Internet of Things. The system comprises an Internet of Things terminal perception cluster, an edge computing node, a data heat monitoring and analyzing module, a physical environment risk sensor, a data entropy increase management unit, a dynamic phase change control engine, a communication data encryption device, a central security storage server and an abnormal intrusion prevention terminal. The present application monitors data access frequency and downhole pipe network environment risk, calculates comprehensive entropy weight values by using the data entropy increase management unit and issues phase change instructions, dynamically defines data as gaseous, trend or solid by the dynamic phase change control engine, and then matches differential encryption protocol stacks and storage topology paths. The present application introduces a physical phase change model, solves the balance problem of oil and gas Internet of Things computing load and security strength, and improves the defense response speed and security of data under extreme risk.

Description

Technical Field

[0001] This invention belongs to the field of information security, specifically relating to an encrypted storage management system for communication data based on the Internet of Things for oil and gas. Background Technology

[0002] With the accelerated digital transformation of the energy industry, the application of oil and gas Internet of Things (IoT) technology is becoming increasingly widespread in exploration and development, production operations, and pipeline transportation, becoming a key infrastructure for improving the efficiency of oil and gas resource utilization and ensuring industrial safety. Oil and gas IoT systems, through sensor clusters deployed at work sites, collect multi-dimensional key data in real time, such as downhole pressure, flow rate, temperature, and production conditions. Relying on industrial communication networks, they achieve the aggregation and interaction of massive amounts of data, providing fundamental data support for production optimization and remote monitoring decision-making.

[0003] Encrypted storage management of communication data in the oil and gas Internet of Things (IoT) is a crucial step in ensuring the security of core sensitive information in energy production and business continuity. This technological approach aims to prevent industrial data from being illegally stolen, tampered with, or eavesdropped on in harsh physical environments or open communication links through encryption algorithms and storage strategies, ensuring end-to-end security and data integrity from terminal sensors to cloud data centers.

[0004] Traditional oil and gas IoT security mechanisms face a contradiction between limited computing resources for edge devices and the need for high-intensity security defenses when processing massive amounts of dynamic data. Existing technologies mostly employ a single full-data encryption mode, which leads to communication delays and system energy overload when processing high-frequency, low-sensitivity production data, making it difficult to balance transmission efficiency and data security.

[0005] Static encryption strategies lack the ability to perceive the dynamic attributes of data and the risks of the external environment in real time. They cannot adjust the encryption strategy according to the data's popularity, sensitivity, and the real-time evolution of the sensor environment, resulting in a lag in the system's defense response under extreme risk scenarios. Due to the lack of an instantaneous blocking mechanism for abnormal intrusion behavior, traditional systems struggle to achieve immediate locking and discrete protection of data status when facing malicious attacks, making it easy for critical production data to be leaked during transmission. Summary of the Invention

[0006] The purpose of this invention is to provide an encrypted storage management system for communication data based on the Internet of Things for oil and gas, which can effectively solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The oil and gas Internet of Things (IoT) communication data encryption and storage management system includes an IoT terminal sensing cluster, edge computing nodes, communication data encryption devices, dynamic phase change control engine, data heat monitoring and analysis module, physical environment risk sensor, data entropy increase management unit, central secure storage server, and abnormal intrusion prevention terminal. The IoT terminal sensing cluster is used to collect multi-dimensional physical quantity data from the oil and gas production operation site in real time, and encapsulate the physical quantity data into an initial data packet and send it to the edge computing node. The edge computing node preprocesses the received initial data packet, extracts data feature information, and forwards the data stream to be processed carrying the data feature information to the data heat monitoring and analysis module and the physical environment risk sensor. The data heat monitoring and analysis module receives the data stream to be processed, calculates the real-time heat index of each group of data based on the access frequency, number of calls and life cycle parameters of the data within a preset time, and uses it as the first reference dimension for defining the physical state of the data. The physical environment risk sensor is deployed downhole or at key nodes of the pipeline network in oil and gas operations to acquire ambient temperature, pressure and chemical medium concentration in real time. Based on the deviation of the ambient temperature, pressure and chemical medium concentration from the preset environmental threshold, the environmental risk coefficient is calculated and used as the second reference dimension for defining the physical state of the data. The data entropy increase management unit establishes a dynamic topology evaluation model based on entropy value changes according to the information sensitivity level, the real-time heat index and the environmental risk coefficient in the data feature information. By calculating the comprehensive entropy weight of the data to be encrypted, it outputs a phase transition command for the current data set. The dynamic phase change control engine receives the phase change conversion command and dynamically defines the data as gaseous data, trending data, or solid data according to the magnitude of the comprehensive entropy weight. It also matches the corresponding encryption protocol stack and storage topology path for data of different states. The communication data encryption device performs differentiated encryption algorithm processing on the data to be processed in the transmission link according to the instructions of the dynamic phase change control engine. The central secure storage server is used to receive data processed by the communication data encryption device and allocate physical storage space according to the data's state level to achieve high-strength discrete storage or lightweight streaming storage. The abnormal intrusion prevention terminal monitors the access behavior of the communication link and storage system in real time. When it detects an unauthorized illegal access request or abnormal traffic fluctuation, it instantly sends a phase change trigger signal to the dynamic phase change control engine, forcing all non-solid-state data in transit to be immediately converted into solid-state storage mode.

[0008] Preferably, when calculating the real-time heat index, the data heat monitoring and analysis module uses a sliding time window algorithm to count the data packet throughput per unit time. If the throughput is greater than a preset throughput threshold and the access frequency is in a preset high-frequency range, the data is determined to have high kinetic energy attributes.

[0009] Furthermore, the physical environment risk sensor compares the sensed downhole high temperature value with a preset safe temperature benchmark through a built-in logic judgment circuit. When the downhole high temperature value continues to exceed the preset safe temperature benchmark and the duration reaches the preset alarm duration, the physical environment risk sensor outputs a high-risk gain signal to the data entropy increase management unit, causing the comprehensive entropy weight value to shift towards the extremely low entropy range.

[0010] Furthermore, the data entropy increase management unit establishes an information security entropy function to transform the confidentiality, integrity, and availability requirements of data into a numerical information entropy expression. The lower the comprehensive entropy weight, the higher the system's orderliness requirement and the greater the corresponding encryption strength.

[0011] Furthermore, the dynamic phase change control engine defines data whose real-time heat index is greater than a preset heat threshold and whose information sensitivity level is lower than a preset sensitivity level as gaseous data. The communication data encryption device employs a lightweight stream encryption algorithm for the gaseous data. The lightweight stream encryption algorithm has low computational complexity, ensuring low-latency transmission in the weak network environment of the oil and gas Internet of Things.

[0012] Furthermore, the dynamic phase change control engine defines data whose information sensitivity level is within a preset core range, whose real-time heat index is below a preset frequency threshold, or whose environmental risk coefficient exceeds a preset risk warning line as solid-state data.

[0013] Furthermore, for the solid-state data, the communication data encryption device calls the ultra-low entropy encryption template in the dynamic phase change control engine to perform a combined processing based on a high-strength asymmetric encryption algorithm and multiple redundancy checks, transforming the data into a high-density ordered arrangement.

[0014] Furthermore, when storing the solid-state data, the central secure storage server initiates a discretization distribution strategy to split the solid-state data into multiple data fragments and store them in different physical storage media. Each data fragment is associated with an independent index sequence and a dynamically generated logical verification tag.

[0015] Furthermore, the abnormal intrusion prevention terminal includes an abnormal feature database matching unit and a behavior pattern analysis unit. The abnormal feature database matching unit compares the features of real-time communication messages with pre-stored illegal attack feature codes. The behavior pattern analysis unit monitors the entropy fluctuation rate of the data stream. If the entropy value drops or rises abnormally within a predetermined time, it is determined that the system has been subjected to external interference or attack.

[0016] Furthermore, when the abnormal intrusion prevention terminal outputs the phase change trigger signal, the dynamic phase change control engine simulates the solidification process in physicochemistry, immediately withdraws all current lightweight encryption authorizations, calls the global highest security level encryption matrix, and forces all gaseous data and trend state data located in the buffer and transmission channel to be repackaged and highly discretized and mapped, ensuring that the data enters an unresolvable solid-state protection state under extreme risk.

[0017] Furthermore, the edge computing node also includes a local cache scheduler, which is used to temporarily store the real-time collected data packets in a local restricted access space during the interruption of the communication link or the execution of the lock command by the abnormal intrusion prevention terminal. After the system recovers to the preset safe steady state, it will be processed again according to the phase transition parameters regenerated by the data entropy increase management unit.

[0018] Furthermore, the dynamic topology encryption mechanism establishes a logical interconnection topology between edge nodes at different geographical coordinates based on the sparsity of the geographical distribution of oil and gas fields. The dynamic phase change control engine can dynamically adjust the load distribution of encryption computing tasks according to the computing resource reserves of each node. When the computing resource occupancy rate of a certain node exceeds the preset load threshold, it automatically migrates some high-energy-consuming encryption operations to adjacent low-load nodes for execution.

[0019] Furthermore, the data popularity monitoring and analysis module also has a data prediction function. By analyzing historical data access trends, it can predict the data activity in a specific future period and pre-set the corresponding encryption resource allocation weights to achieve pre-sensory adjustment of encryption strategies.

[0020] Furthermore, the central secure storage server has a built-in self-healing verification mechanism that periodically performs integrity self-checks on the stored solid-state data fragments. If some data fragments are found to be damaged or lost, they are automatically repaired using error correction codes stored in the redundant area, ensuring the absolute security of the static core data accumulated over a long period of oil and gas production at the physical storage level.

[0021] Furthermore, when the communication data encryption device performs lightweight stream encryption, its key update frequency is positively correlated with the real-time popularity index. That is, the higher the data access frequency, the shorter the corresponding stream encryption key change cycle. The rapid iterative key logic increases the difficulty for illegal eavesdroppers to crack the data stream.

[0022] Furthermore, when processing multi-dimensional sensor data fusion, the data entropy increase management unit assigns different weight coefficients to data from different sources. For example, the weight coefficient of the downhole pressure sensor is set to be greater than that of the surface ambient temperature sensor, so that changes in key operating condition data can more directly trigger the system's phase change defense response.

[0023] Furthermore, the abnormal intrusion defense terminal is also equipped with a feedback control loop. After successfully intercepting a simulated attack or a real attack, it will automatically feed back the behavioral characteristic parameters of the attack behavior to the dynamic phase change control engine. The dynamic phase change control engine will automatically increase the initial encryption strength benchmark of the corresponding data link to achieve self-evolutionary improvement of security defense performance.

[0024] Compared with the prior art, the present invention has the following beneficial effects: 1. The oil and gas IoT communication data encryption storage management system provided by this invention resolves the inherent contradiction between the computing power setting and security of oil and gas IoT devices by introducing the entropy increase and phase transition model from physical chemistry. Through the synergistic effect of the data heat monitoring and analysis module and the physical environment risk sensor, the system can perceive the dynamic attributes of the data itself and the risk evolution of the external physical environment in real time.

[0025] 2. This invention reduces the computational overhead and communication latency of high-frequency, low-sensitivity data during transmission by defining gaseous data and applying lightweight flow encryption, thus ensuring the real-time requirements of oil and gas production monitoring. 3. This invention ensures the absolute security of core sensitive data at the storage end by defining solid-state data and applying extremely low-entropy, high-strength encryption and discretized storage, preventing data leakage caused by physical damage to the storage medium or the breach of a single node.

[0026] 4. The instantaneous phase transition triggering mechanism constructed in this invention simulates the solidification process of matter, giving the system the ability to react during the day when facing extreme intrusion attacks. It can lock the data in transit of the entire system into a high-strength encrypted state within milliseconds, eliminating the lag in risk response of traditional defense mechanisms.

[0027] 5. This invention combines the typical physical characteristics of the oil and gas Internet of Things, directly incorporating environmental parameters such as downhole high temperature and pressure into encrypted logic calculations, so that information security defense is no longer isolated from the physical environment of production.

[0028] 6. By dynamically allocating computing load and implementing self-healing verification, this invention further enhances the system's business continuity and data integrity under harsh operating conditions, providing a solid security foundation for the digital transformation of the energy industry. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the data entropy increase management unit and dynamic phase change control engine in this invention; Figure 3 This is a logical flowchart of the multidimensional physical quantity data acquisition, data heat monitoring, and environmental risk perception in this invention. Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between the IoT terminal sensing cluster, edge computing nodes and central security storage server in this invention; Figure 5 This is a schematic diagram illustrating the defense principle of the abnormal intrusion defense terminal triggering an instantaneous phase transition of data across the entire system in this invention. Detailed Implementation

[0030] Example 1: Please refer to the appendix Figure 1 To be continued Figure 5 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0031] The oil and gas Internet of Things (IoT) communication data encryption and storage management system includes an IoT terminal sensing cluster, edge computing nodes, communication data encryption devices, a dynamic phase change control engine, a data heat monitoring and analysis module, a physical environment risk sensor, a data entropy increase management unit, a central secure storage server, and an abnormal intrusion prevention terminal.

[0032] The IoT terminal sensing cluster is deployed at the physical site of oil and gas extraction, transportation, and processing. Specifically, it is configured to collect multi-dimensional physical quantity data reflecting the operating conditions of oil and gas production operations in real time through sensor arrays integrated into various industrial equipment. The multi-dimensional physical quantity data includes, but is not limited to, wellbore pressure, wellhead temperature, casing pressure, transfer station flow rate, crude oil water content, and the wall thickness reduction rate of natural gas transmission pipelines.

[0033] The IoT terminal sensing cluster has a built-in analog-to-digital converter circuit and a fieldbus protocol processing chip, which converts the acquired analog signals into digital signals and encapsulates the multidimensional physical quantity data into an initial data packet containing header information, a check field, and payload data according to a preset industrial IoT communication protocol format. The header information includes at least a unique identifier for the acquisition device, geographic coordinate parameters, and a high-precision timestamp. After encapsulation, the initial data packet is sent via wired or wireless communication links to the edge computing nodes deployed at the edge of the work area.

[0034] The edge computing node, serving as an intermediate processing hub connecting the lower perception layer and the upper management layer, is built on a hardware architecture based on a high-performance embedded processor or a field-programmable gate array (FPGA). Upon receiving the initial data packet, the edge computing node first invokes its built-in message parsing protocol stack to perform deep packet unpacking and parsing of the initial data packet to execute data preprocessing operations. These preprocessing operations include data denoising, data alignment, and dimensional unification.

[0035] The edge computing node extracts data features reflecting the statistical characteristics of the preprocessed data by executing a feature extraction algorithm. These features include data variance, growth slope, quantile distribution, and the production process sensitivity weights represented by the data. After extraction, the edge computing node generates a data stream carrying these features and forwards it concurrently to the data heat monitoring and analysis module and the physical environment risk sensor, providing raw input for subsequent dynamic encryption strategy formulation.

[0036] The edge computing node also includes a local cache scheduler. The local cache scheduler is configured to monitor the communication link status between the edge and the remote server in real time. When the communication link bandwidth jitter exceeds a preset jitter threshold, or when the abnormal intrusion prevention terminal issues a global lock command, the local cache scheduler immediately takes over the data flow and temporarily stores the initial data packets generated in real time in a restricted access protected space on the edge computing node.

[0037] The restricted access protected space employs physical isolation or encrypted partitioning technology to prevent unauthorized access. Once the system detects that the communication link has returned to a preset stable state or receives a security unlock command, the local cache scheduler will re-evaluate and re-encapsulate the locally cached data based on the latest phase transition parameters generated in real time by the data entropy increase management unit, and trigger subsequent encrypted transmission processes.

[0038] The data heat monitoring and analysis module runs internally on a high-performance streaming computing engine, and is used to receive and analyze the data stream to be processed. The module employs a configurable sliding time window algorithm to dynamically count the throughput of data packets flowing into the system per unit time.

[0039] The data popularity monitoring and analysis module is configured to calculate, within the current sliding time window, the access frequency of a specific data set, the number of times it is called by external applications, and the remaining lifespan of this type of data in the system logic. Based on the above parameters, the data popularity monitoring and analysis module calculates the weighted sum of the access frequency and the number of calls, and combines this with the inverse gain of the lifespan parameter to finally calculate the real-time popularity index of each data set.

[0040] The real-time heat index is defined as a core indicator characterizing the kinetic energy attribute of data and serves as the first reference dimension for subsequently defining the physical state of data. If the statistically obtained flux value is greater than a preset flux benchmark threshold, and the access frequency remains within a preset high-frequency continuous interval for a duration exceeding a preset duration, the data heat monitoring and analysis module automatically determines that the data has high kinetic energy attributes, meaning that it tends to exhibit highly active gaseous characteristics in the physicochemical model.

[0041] The data heat monitoring and analysis module also features data prediction capabilities. This prediction function utilizes a built-in long short-term memory model or autoregressive integral moving average model to perform deep learning and time-series modeling of historically stored data access trends. By predicting the evolution of data activity over one or more specific time periods, the data heat monitoring and analysis module can pre-calculate the pre-defined encryption resource allocation weights. This mechanism allows the system to proactively adjust the encryption engine's resource usage ratio based on predicted business peaks or troughs, effectively mitigating system processing problems caused by sudden surges in traffic.

[0042] The physical environment risk sensor, acting as a physical sentinel to detect external physical threats, is physically deployed deep downhole in oil and gas operations, in critical shut-off valve chambers of long-distance pipelines, or at high-risk process nodes. The sensor integrates a high-temperature, high-pressure resistant sensing unit to acquire real-time values ​​of ambient temperature, ambient pressure, and the instantaneous concentration of chemical media in the environment. Internally, the sensor incorporates a dedicated logic circuit configured to perform a real-time differential comparison between the sensed values ​​and pre-stored environmental safety benchmark thresholds in non-volatile memory.

[0043] The physical environment risk sensor calculates an environmental risk coefficient based on the deviation of ambient temperature, pressure, and medium concentration from their respective thresholds. This environmental risk coefficient reflects the potential threat posed by the physical operating environment to the data acquisition and transmission equipment and serves as a second reference dimension for defining the physical state of the data. For example, when the sensed downhole high temperature value continuously exceeds a preset safe temperature threshold, and the duration of this deviation reaches a preset alarm duration, the logic judgment circuit immediately triggers a risk escalation logic, outputting a high-risk gain signal to the data entropy increase management unit. This high-risk gain signal forces intervention in the system's entropy value determination, causing the overall entropy weight of the data to be encrypted to shift towards an extremely low entropy range.

[0044] The data entropy increase management unit is the core brain responsible for security logic decisions in this system. It is configured to establish a dynamic topology evaluation model based on the principle of physicochemical entropy value change, based on the information sensitivity level from the feature information provided by the edge computing nodes, the real-time heat index output by the data heat monitoring and analysis module, and the environmental risk coefficient fed back by the physical environment risk sensor. This data entropy increase management unit transforms the confidentiality, integrity, and availability requirements of data into a numerical expression of information entropy by establishing an information security entropy function.

[0045] The calculation logic for the comprehensive entropy weight is as follows: The data entropy increase management unit normalizes the three input dimension parameters and assigns different weights to each parameter according to the actual business needs of oil and gas production. When processing multi-dimensional sensor data fusion, the data entropy increase management unit is configured to assign differentiated weight coefficients to data from different sources. For example, under drilling conditions, the weight coefficient of the downhole pressure sensor is set to be greater than that of the surface ambient temperature sensor, so that small disturbances in key operating condition data can trigger the system's phase change defense response more directly and quickly.

[0046] The comprehensive entropy weight represents the required degree of orderliness of the data in the current environment. A lower comprehensive entropy weight indicates a higher requirement for the orderliness of the data set, corresponding to greater security and encryption strength requirements. After calculation, the data entropy increase management unit outputs a phase transition instruction for the current data set. This instruction includes a target state identifier and a corresponding set of parameter configurations.

[0047] The dynamic phase transition control engine receives phase transition commands from the data entropy increase management unit. The core logic of this dynamic phase transition control engine lies in simulating the phase transition process of matter under different energy states, and dynamically defining the data to be processed as gaseous data, trending state data, or solid-state data according to the magnitude of the comprehensive entropy weight.

[0048] For low-risk, high-flow data with a real-time heat index greater than a preset heat threshold and an information sensitivity level lower than a preset sensitivity level, the dynamic phase change control engine defines it as gaseous data. For high-value, high-risk data with an information sensitivity level in a preset core range, a real-time heat index lower than a preset frequency threshold, or an environmental risk coefficient exceeding a preset risk warning line, the dynamic phase change control engine defines it as solid-state data. Intermediate-state data between these two is defined as trending-state data.

[0049] For data with different morphologies, the dynamic phase change control engine is configured to automatically match the corresponding encryption protocol stack and storage topology path from a preset protocol library. The dynamic phase change control engine also has a load adjustment function, capable of establishing logical interconnection topologies between edge nodes at different geographical coordinates based on the sparse characteristics of oil and gas field geographical distribution. This dynamic phase change control engine dynamically adjusts the load distribution of encryption computing tasks by monitoring the processor utilization, memory availability, and bandwidth consumption of each edge node in real time.

[0050] When the computing resource utilization rate of a specific edge node exceeds the preset load warning threshold, the dynamic phase change control engine automatically starts the migration logic to offload some high-energy-consuming encryption computing tasks to adjacent nodes in a low-load state, ensuring that the processing latency of the entire system remains within the millisecond range.

[0051] The communication data encryption device, acting as an execution layer entity, performs differentiated encryption algorithm processing on the data to be processed in the transmission link according to the specific instructions issued by the dynamic phase change control engine. For data defined as gaseous, the communication data encryption device invokes a lightweight stream encryption algorithm. This lightweight stream encryption algorithm uses a short initialization vector and a pseudo-random key stream with high-frequency transformations to perform an XOR operation on the data payload. Due to its extremely low computational complexity, it can reduce the consumption of computing resources on edge devices and ensure low-latency data transmission in weak network environments or narrowband satellite links commonly found in the oil and gas IoT.

[0052] Furthermore, when performing lightweight stream encryption, the key update logic integrated within the communication data encryption device is configured such that the frequency of key updates is positively correlated with the real-time popularity index. That is, the higher the data access frequency and the stronger the data flow, the shorter the corresponding stream encryption key transformation cycle.

[0053] This key update mechanism, which dynamically iterates based on data availability, effectively increases the difficulty for unauthorized eavesdroppers to obtain the payload through brute-force or correlation attacks. For data defined as solid-state, the communication data encryption device invokes an extremely low-entropy encryption template, performing a combination of high-strength asymmetric encryption algorithms and multiple redundancy checks. This process transforms the originally loose data structure into a high-density, ordered arrangement, achieving tight sealing of core sensitive information.

[0054] The central secure storage server, deployed in an enterprise-level data center or private cloud environment, receives encrypted data streams processed by the communication data encryption device. The central secure storage server is configured to dynamically allocate differentiated physical storage space based on the data's state level. For gaseous data, a lightweight streaming storage mode is adopted, prioritizing write speed and retrieval efficiency.

[0055] For solid-state data, the central secure storage server employs a robust discretization distribution strategy. This strategy involves the storage server splitting complete solid-state data packets into multiple non-overlapping data fragments and distributing these fragments across different physical storage media, racks, and even storage nodes in different geographical locations using a distributed storage algorithm. Each data fragment is associated with an independent index sequence and a logical verification tag dynamically generated by the storage engine. This discretization mechanism ensures that even if a single physical storage node is compromised or illegally attacked, an attacker cannot reconstruct the complete original sensitive data from a single fragment.

[0056] The central secure storage server has a built-in self-healing verification mechanism. This mechanism performs integrity checks on stored solid-state data fragments through periodic polling or during system idle periods. If the verification and calculations reveal that some data fragments are at risk of damage or loss, the server automatically repairs them using error-correcting codes stored in the redundant area, ensuring the absolute durability and security of the static core data accumulated over long-term in oil and gas production.

[0057] The abnormal intrusion prevention terminal serves as the dynamic defense command center for the entire system, monitoring access behavior across the entire communication link and storage system in real time. This terminal includes an abnormal signature database matching unit and a behavior pattern analysis unit. The abnormal signature database matching unit has a continuously updated industrial hacker attack signature database built-in. It performs deep packet inspection on the characteristic fields of real-time communication packets and compares them in real-time with pre-stored illegal attack signatures. The behavior pattern analysis unit focuses on monitoring the entropy fluctuation rate of the data stream. If an abnormal and drastic drop or rise in the system's total entropy value is detected within a predetermined time, and this fluctuation cannot be explained by normal production condition changes, it is determined that the system is under external interference attack or potential penetration.

[0058] Upon detecting an unauthorized access request or abnormal traffic fluctuation, the abnormal intrusion prevention terminal immediately generates a transient phase transition trigger signal and broadcasts it to the entire system. Upon receiving this signal, the dynamic phase transition control engine simulates a transient solidification process in physicochemistry, immediately revoking the lightweight encryption authorization currently issued to all edge nodes and invoking the highest-security-level encryption matrix globally. At this point, the system forcibly repackages and performs high-strength discretization mapping on all non-solid-state data located in buffers, edge node caches, and transmission channels. This mechanism ensures that, in the event of an extreme intrusion risk, all data in transit can quickly enter an unresolvable solid-state protection state, blocking any possible data leakage paths.

[0059] The abnormal intrusion prevention terminal is also equipped with a feedback control loop. After successfully intercepting and blocking a simulated or real attack, the terminal automatically extracts the behavioral characteristic parameters of the attack and feeds these parameters back to the dynamic phase change control engine and the data entropy increase management unit. Based on this feedback data, the system automatically increases the initial encryption strength benchmark value of the corresponding data link or business logic, achieving self-evolution and adaptive performance improvement in security defense.

[0060] Example 2: As an alternative or extended implementation of Example 1, this example describes an encrypted storage management system for oil and gas IoT communication data based on a distributed collaborative architecture. This distributed collaborative architecture is particularly suitable for ultra-large-scale oil and gas field IoT environments with extremely wide geographical distribution and tens of thousands of nodes.

[0061] The system also includes an IoT terminal sensing cluster, edge computing nodes, communication data encryption devices, a dynamic phase change control engine, a data heat monitoring and analysis module, a physical environment risk sensor, a data entropy increase management unit, a central secure storage server, and an abnormal intrusion prevention terminal. In this embodiment, the functions of each module have been optimized and enhanced in a distributed manner while maintaining the core logic of Embodiment 1.

[0062] In this embodiment, the IoT terminal sensing cluster adopts a hierarchical group management model. Each sensing group includes a sub-aggregation node with summarization capabilities. This sub-aggregation node is configured to perform preliminary semantic association analysis on the raw data collected by all sensors within the sensing group. For example, in a drilling platform's well control system, the sub-aggregation node maps and associates pressure sensor data with blowout preventer position sensor data, generating a composite data packet with contextual logic. This pre-defined semantic encapsulation reduces the logical burden on subsequent edge computing nodes during feature extraction.

[0063] In this embodiment, the edge computing nodes are designed as a cluster with homogeneous computing capabilities. Multiple edge computing nodes are interconnected via high-speed industrial Ethernet to form a local computing resource pool. When one node experiences processor overheating or processing queue backlog due to performing high-intensity encryption operations, its built-in distributed task scheduler can use a heartbeat mechanism to sense the load status of neighboring nodes and seamlessly migrate the data stream to be processed to the less loaded nodes. This mechanism simulates the thermal conductivity balance in physics, ensuring the stability of the overall system entropy.

[0064] The data heat monitoring and analysis module incorporates a multi-dimensional time decay factor when calculating the real-time heat index. This module is configured such that if the access frequency of a certain set of data shows a decreasing trend over multiple sliding windows, its contribution to the real-time heat index will be reduced according to an exponential decay function. In this way, the system can more accurately capture the cooling process of data and promptly switch it from a resource-intensive gaseous encryption protocol to a moderately strong trending or solid-state encryption mode.

[0065] In this embodiment, the physical environment risk sensor integrates a multi-sensory fusion judgment algorithm. Besides monitoring conventional physical quantities such as temperature and pressure, the sensor also determines environmental risks by analyzing the electromagnetic interference intensity of on-site communication signals. Abnormally severe disturbances in the electromagnetic environment typically indicate potential illegal wireless interference attacks or large-scale power equipment failures. The physical environment risk sensor reports electromagnetic risk gains, prompting the data entropy increase management unit to classify all data generated in that area as solid-state objects requiring extremely low entropy protection, thus proactively mitigating the risk of data tampering due to unstable communication links.

[0066] In this embodiment, the data entropy increase management unit employs a multi-level entropy evaluation mechanism. Besides calculating entropy for individual data packets, this unit is also configured to calculate the data flow topology entropy for the entire local network. Topology entropy reflects the complexity and orderliness of the data flow. If the topology entropy suddenly increases, indicating an abnormal branching or backflow in the data flow, the data entropy increase management unit will immediately raise the overall system security threshold level, reconstruct the communication path through a dynamic topology encryption mechanism, and encapsulate the data using dynamically generated logical tunnels, making it impossible for attackers to trace the true communication topology.

[0067] The dynamic phase change control engine, in this embodiment, is configured to have phase change hysteresis characteristics. This phase change hysteresis characteristic is achieved by setting an asymmetric phase change trigger threshold. When data changes from a gaseous state with low safety requirements to a solid state with high safety requirements, a lower trigger threshold is used to ensure timely safety response. When the system recovers and the data needs to be restored from a solid to a gaseous state, even more stringent stability conditions and a higher trigger threshold must be met. This design, similar to the hysteresis phenomenon in physics, effectively prevents frequent encryption strategy oscillations at the system's critical point, ensuring the smoothness of the communication process.

[0068] In this embodiment, the communication data encryption device incorporates a hardware acceleration module. This module is based on an application-specific integrated circuit (ASIC) design and integrates hard cores for various standard encryption algorithms. For lightweight stream encryption of gaseous data, the device directly calls the hard core logic to achieve line-speed forwarding. For solid-state data, the device accelerates the generation and verification process of asymmetric keys by invoking a large number arithmetic unit. The communication data encryption device can multiplex multiple logical encryption channels on the same physical link according to the instructions of the dynamic phase-change control engine. Data of different states are mapped to different logical channels, each with its own independent initialization vector space and key management cycle.

[0069] In this embodiment, the central secure storage server employs a heterogeneous storage architecture. The server is backed up by a high-speed solid-state array, a medium-speed hard disk array, and a low-speed optical disc library or cloud archiving space. Based on the status level defined by the dynamic phase-change control engine, the server not only logically allocates physical space but also optimizes the selection of physical storage media. Solid-state data is preferentially stored in the hard disk array with multi-replica fault tolerance and periodically dumped to the optical disc library for permanent offline protection. Gaseous data is stored in a circular buffer within the high-speed solid-state array and is automatically overwritten upon expiration. This media matching strategy based on data properties reduces the total storage cost of massive amounts of data while ensuring the absolute security of core data.

[0070] In this embodiment, the abnormal intrusion prevention terminal enhances the depth of its behavior pattern analysis unit. This behavior pattern analysis unit is configured to perform baseline modeling of the entropy flow of the entire network using an unsupervised learning algorithm. By analyzing the daily and weekly cyclical characteristics of data entropy values ​​under normal operating conditions, the system can identify slow penetration attacks hidden beneath normal traffic. Once the entropy value of a node deviates from the pre-established dynamic baseline curve, even if the deviation does not reach the static threshold, the abnormal intrusion prevention terminal will send a phase transition warning signal to that node and its downstream nodes, causing the relevant data to enter a trending or solid-state protection mode in advance.

[0071] The system in this embodiment also includes a global security posture presentation module. This module receives security metadata from various edge nodes and storage servers and dynamically simulates phase diagrams in physicochemical processes on a visual interface. System administrators can intuitively grasp the evolution of the security posture of the entire oil and gas field IoT by observing the color intensity of different regions in the phase diagram and the movement of phase boundaries. In the event of a large-scale cyberattack, administrators can see the data throughout the system rapidly change from light colors representing gaseous states to dark colors representing solid states, experiencing in real time the comprehensive locking and protection process brought about by the instantaneous phase change defense mechanism.

[0072] This distributed collaborative architecture not only inherits the dynamic defense advantages based on the entropy increase-phase transition model in Example 1, but also further improves the robustness and operational efficiency of the system in complex, harsh and large-scale oil and gas production environments through resource pooling, multi-level entropy assessment and medium matching storage.

[0073] Example 3: As another embodiment of the present invention, this example focuses on an oil and gas Internet of Things communication data encryption storage management system based on deep fusion of edge nodes, and particularly emphasizes adaptive security performance in extremely low bandwidth and extreme operating environments.

[0074] The system includes an IoT terminal sensing cluster, edge computing nodes, communication data encryption devices, a dynamic phase change control engine, a data heat monitoring and analysis module, a physical environment risk sensor, a data entropy increase management unit, a central secure storage server, and an abnormal intrusion prevention terminal.

[0075] In this embodiment, the IoT terminal sensing cluster communicates with edge computing nodes via a low-power broadband wireless network or restricted application protocols deployed at the oil and gas site. Considering the power supply limitations in remote environments, the terminal sensing cluster is configured with data fusion sampling capabilities. This data fusion sampling capability allows the terminal to dynamically adjust the sampling frequency based on the current environmental risk coefficient. When the physical environment risk sensor detects that the environment is in a steady state, the terminal samples at a low frequency and performs simple lightweight encapsulation; once the risk coefficient increases, the terminal immediately switches to a high-frequency sampling mode and adds an enhanced check field to the initial data packet.

[0076] The edge computing node in this embodiment employs a reinforced protection design. In addition to logical-level security mechanisms, its physical casing integrates anti-tamper sensing circuitry. When the sensing circuit detects physical damage, the edge computing node is configured to immediately send a high-level risk signal to the dynamic phase change control engine, triggering either the instantaneous destruction of locally cached data or secondary high-strength encryption. During data processing, the edge computing node's built-in preprocessing algorithm compresses and samples the oil and gas production data to address the sparsity of the data, reducing the data stream volume while extracting feature information to accommodate limited communication bandwidth.

[0077] In this embodiment, the data popularity monitoring and analysis module introduces a business logic-based popularity assessment. This module not only tracks access frequency but also correlates it with oil and gas production scheduling tasks. If a set of data corresponds to a critical production enhancement operation currently being performed, even if its access frequency is temporarily low, the module will assign it a higher business weight based on business tags, thus increasing its initial popularity index. This business importance-based judgment logic ensures that data from critical production processes receives encryption protection commensurate with its value throughout its entire lifecycle.

[0078] In this embodiment, the physical environment risk sensor is configured to have mutual sensing determination functionality. In the oil and gas pipeline network, multiple geographically adjacent physical environment risk sensors form a monitoring cluster via wireless links. When one sensor detects an anomaly in downhole temperature or pressure, it broadcasts the signal to neighboring sensors for collaborative verification. Only when more than a preset proportion of sensors confirm the anomaly will a high-risk gain signal be output to the data entropy increase management unit. This distributed consensus mechanism effectively filters system-level phase transition disturbances caused by single sensor failures or false alarms.

[0079] In this embodiment, the data entropy increase management unit employs a game theory-based entropy weight optimization strategy. When calculating the comprehensive entropy weight, this unit considers not only current security requirements but also system processing energy consumption and transmission latency. By simulating the game between attackers and defenders, the unit seeks the optimal entropy balance point, maximizing the overall system energy efficiency while meeting preset security probabilities. This has significant engineering application value for remote oil and gas monitoring nodes that rely entirely on solar power.

[0080] In this embodiment, the dynamic phase-change control engine supports both soft and hard phase-change triggering modes. Under normal fluctuation conditions, the engine uses soft phase-change mode, meaning the switching of encryption protocols has a certain transition period, allowing data packets to coexist with multiple state characteristics for a short period of time to smooth transmission jitter. However, when the abnormal intrusion prevention terminal issues an emergency signal, the engine forcibly switches to hard phase-change mode, cutting off all insecure channels within microseconds and achieving instantaneous freezing of the data state.

[0081] In this embodiment, the communication data encryption device integrates a quantum random number generator interface. For solid-state data, the device is configured to use a physically random sequence generated by quantum random numbers as the core perturbation factor in the encryption process. This physical-level randomness, combined with a complex asymmetric algorithm, makes the encrypted data mathematically virtually unbreakable. The device's internal stream encryption logic supports adaptive adjustment based on the signal-to-noise ratio. When significant interference is detected in the communication link, it automatically adds error-correcting redundancy bits to ensure the integrity of gaseous data under harsh communication conditions.

[0082] In this embodiment, the central secure storage server employs a blockchain-based integrity auditing architecture. When storing fragments of solid-state data, the server packages the fragment's hash value, storage location, and access logs onto the blockchain. The self-healing verification mechanism not only checks for physical block corruption but also periodically compares the hash records on the blockchain. If a discrepancy is found between the locally stored fragment hash and the on-chain record, the server immediately initiates self-healing logic, downloading a backup of the original fragment from other redundant nodes for replacement. This storage management scheme based on an immutable ledger provides high credibility for long-term historical data of oil and gas production.

[0083] In this embodiment, the abnormal intrusion defense terminal adds a physical decoy unit. This physical decoy unit is configured to simulate several false gaseous data streams and false edge nodes in the network. These decoys have low defense strength and are designed to induce attackers to conduct trial intrusions. When an attacker touches these decoys, the terminal's behavior pattern analysis unit can detect their attack characteristics in advance and, before the real core data is threatened, instruct the dynamic phase change control engine to complete the phase change lock of the entire system.

[0084] This implementation scheme, based on deep integration of edge nodes, organically combines technologies such as physical security, environmental adaptation, energy efficiency game theory, and distributed consensus to build an oil and gas IoT encrypted storage management system that can maintain high resilience and dynamic security even in extremely remote, resource-constrained, and hostile attack-prone environments.

[0085] Those skilled in the art should understand that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail through the above preferred embodiments, any modifications, equivalent substitutions or improvements made to the present invention without departing from the spirit and scope of the present invention should be included within the protection scope of the present invention. The modules, units, engines and servers described in the system can be implemented in the form of hardware integrated circuits or in the form of software instruction sets running on general-purpose processing devices. The technical features in the various embodiments can be combined with each other without conflict to adapt to the needs of different industrial application scenarios.

Claims

1. An oil and gas Internet of Things (IoT) communication data encryption and storage management system, comprising an IoT terminal sensing cluster, edge computing nodes, a communication data encryption device, a dynamic phase change control engine, a data heat monitoring and analysis module, a physical environment risk sensor, a data entropy increase management unit, a central secure storage server, and an abnormal intrusion prevention terminal, characterized in that: The IoT terminal sensing cluster is used to collect multi-dimensional physical quantity data from the oil and gas production site in real time, and encapsulate the physical quantity data into an initial data packet containing a unique identifier of the collecting device, geographic coordinate parameters and a high-precision timestamp, and send it to the edge computing node. The edge computing node is used to preprocess the received initial data packet, extract data feature information reflecting the statistical characteristics of the data, and forward the data stream to be processed carrying the data feature information to the data heat monitoring and analysis module and the physical environment risk sensor. The data heat monitoring and analysis module is used to receive the data stream to be processed, calculate the real-time heat index reflecting the data kinetic attributes, and use it as the first reference dimension for defining the physical state of the data. The physical environment risk sensor is deployed downhole or at key nodes of the pipeline network in oil and gas operations to acquire environmental parameters and calculate environmental risk coefficients, which are used as a second reference dimension for defining the physical state of the data. The data entropy increase management unit is used to establish a dynamic topology evaluation model based on entropy value changes according to the data feature information, the real-time heat index and the environmental risk coefficient, and output a phase transition command by calculating the comprehensive entropy weight of the data to be encrypted. The dynamic phase change control engine is used to receive the phase change conversion command, dynamically define the data as gaseous data, trending state data or solid data according to the comprehensive entropy weight, and match the encryption protocol stack and storage topology path. The communication data encryption device is used to perform differentiated encryption algorithm processing on the data to be processed in the transmission link according to the instructions of the dynamic phase change control engine. The central secure storage server is used to receive processed data and allocate physical storage space according to the data's status level. The abnormal intrusion prevention terminal is used to monitor the access behavior of the communication link and the storage system. When an anomaly is detected, it sends a phase change trigger signal to the dynamic phase change control engine to force the non-solid-state data in transmission to be converted into solid-state storage mode.

2. The oil and gas IoT-based communication data encryption and storage management system according to claim 1, characterized in that: The edge computing node is built on a high-performance embedded processor or field-programmable gate array, and the edge computing node includes a local cache scheduler, which is configured to monitor the communication link status between the edge side and the remote server in real time. When the communication link bandwidth jitter is detected to exceed the preset jitter threshold, or when the abnormal intrusion prevention terminal issues a global lock command, the local cache scheduler takes over the data flow and temporarily stores the initial data packet in a restricted access protected space on the edge computing node that uses physical isolation or encrypted partitioning technology. Once the system detects that the communication link has returned to a preset stable state or receives a security unlock command, the local cache scheduler re-evaluates and re-encapsulates the locally cached data according to the phase transition parameters regenerated by the data entropy increase management unit, and triggers the encrypted transmission process.

3. The oil and gas IoT-based communication data encryption and storage management system according to claim 2, characterized in that: The data heat monitoring and analysis module uses a sliding time window algorithm with configurable step size to dynamically count the throughput of data packets flowing into the system per unit time. The data heat monitoring and analysis module is configured to calculate the access frequency, the number of times it is called by external applications, and the life cycle parameter of the data set within the current sliding time window. It calculates the real-time heat index of each set of data by calculating the weighted sum of the access frequency and the number of calls, combined with the inverse gain of the life cycle parameter. If the flux value is greater than the preset flux benchmark threshold, and the access frequency lasts for more than the preset duration within the preset high-frequency continuous interval, then the data is automatically determined to have gaseous characteristics. The data heat monitoring and analysis module also has a data prediction function. By calling the built-in long short-term memory model, it performs time-series modeling of the access trends of historically stored data, predicts the evolution of data activity in future time periods, and calculates the preset encryption resource allocation weights in advance to achieve pre-sensory adjustment of encryption strategies.

4. The oil and gas IoT-based communication data encryption and storage management system according to claim 3, characterized in that: The physical environment risk sensor integrates a high-temperature and high-pressure resistant sensing unit and a logic judgment circuit, which is used to acquire real-time values ​​of ambient temperature, ambient pressure, and the instantaneous concentration of chemical media in the environment. The logic judgment circuit is configured to compare the real-time sensed values ​​with the pre-stored environmental safety benchmark thresholds in real time, and calculate the environmental risk coefficient based on the degree of deviation of the environmental temperature, pressure and medium concentration from their respective thresholds. When the sensed downhole high temperature value continuously exceeds the preset safe temperature threshold and the duration reaches the preset alarm duration, the logic judgment circuit triggers the risk escalation logic, outputs a high-risk gain signal to the data entropy increase management unit, forcibly intervenes in the determination of the system entropy value, and causes the comprehensive entropy weight of the data to be encrypted to shift to the extremely low entropy range. Multiple geographically adjacent physical environment risk sensors form a monitoring cluster through wireless links and collaboratively verify abnormal signals through a distributed consensus mechanism.

5. The oil and gas IoT-based communication data encryption and storage management system according to claim 4, characterized in that: The data entropy increase management unit transforms the data confidentiality, integrity, and availability requirements into a numerical expression of information entropy by establishing an information security entropy function. The data entropy increase management unit normalizes the input parameters and assigns different weights to each parameter according to the needs of oil and gas production operations. When processing multi-dimensional sensor data fusion, the data entropy increase management unit assigns differentiated weight coefficients to data from different sources, setting the weight coefficient of the downhole pressure sensor to be greater than that of the surface ambient temperature sensor. The comprehensive entropy weight represents the required degree of order of the data in the current environment. The lower the comprehensive entropy weight, the greater the security requirements and encryption strength requirements of the system for the data set. The data entropy increase management unit also adopts an entropy weight optimization strategy based on game theory. By simulating the game process between the attacker and the defender, it seeks the entropy balance point that satisfies the preset security probability and has the highest system processing energy efficiency.

6. The oil and gas IoT-based communication data encryption and storage management system according to claim 5, characterized in that: The dynamic phase change control engine executes the state definition based on the magnitude of the comprehensive entropy weight; Data whose real-time heat index is greater than the preset heat threshold and whose information sensitivity level is lower than the preset sensitivity level is defined as gaseous data. Data whose information sensitivity level is within the preset core range, or whose real-time heat index is lower than the preset frequency threshold, or whose environmental risk coefficient exceeds the preset risk warning line, are defined as solid data. The dynamic phase change control engine has a load adjustment function, establishes a logical interconnection topology between edge nodes at different geographical coordinates based on the geographical distribution of oil and gas fields, and monitors the processor utilization, memory availability and bandwidth consumption of each edge node in real time. When the computing resource utilization rate of an edge node exceeds the preset load warning threshold, some encryption computing tasks are automatically migrated to adjacent nodes that are under low load for execution. The dynamic phase change control engine achieves phase change hysteresis characteristics by setting an asymmetric phase change trigger threshold, ensuring the stability of the communication process during data state recovery.

7. The oil and gas IoT-based encrypted storage management system for communication data as described in claim 6, characterized in that: The communication data encryption device calls a lightweight stream encryption algorithm for gaseous data, and performs an XOR operation on the data payload using a short initialization vector and a pseudo-random key stream with high-frequency changes. The key update logic integrated within the communication data encryption device is configured such that the frequency of key updates is positively correlated with the real-time popularity index, that is, the higher the data access frequency, the shorter the corresponding stream encryption key change cycle. For solid-state data, the communication data encryption device calls an extremely low-entropy encryption template and performs a combined processing based on a high-strength asymmetric encryption algorithm and multiple redundancy checks to transform the loose data structure into a high-density ordered arrangement. The communication data encryption device also integrates a quantum random number generator interface, uses the physical random sequence generated by quantum random numbers as the encryption perturbation factor, and supports adaptive adjustment based on the signal-to-noise ratio, automatically increasing error correction redundancy bits when the communication link is detected to be highly interfered with.

8. The oil and gas IoT-based encrypted storage management system for communication data as described in claim 7, characterized in that: The central secure storage server employs a lightweight streaming storage mode for gaseous data and a discrete distribution strategy for solid-state data. The discretization distribution strategy splits the complete solid data packet into multiple non-overlapping data fragments, and uses a distributed storage algorithm to disperse the data fragments in storage nodes on different physical storage media, racks or geographical locations. Each data fragment is associated with an independent index sequence and a dynamically generated logical verification tag. The central secure storage server has a built-in self-healing verification mechanism that periodically performs integrity self-checks on stored solid-state data fragments and automatically repairs them using error correction codes stored in the redundant area. The central security storage server adopts a heterogeneous storage architecture. Based on the status level, solid-state data is stored in a mechanical hard disk array with multi-replica fault tolerance and periodically transferred to the optical disk library. Gaseous data is stored in the circular buffer of the high-speed solid-state array.

9. The oil and gas IoT-based communication data encryption and storage management system according to claim 8, characterized in that: The abnormal intrusion prevention terminal includes an abnormal feature database matching unit and a behavior pattern analysis unit; The anomaly feature database matching unit performs deep packet inspection on real-time communication messages and compares them with pre-stored illegal attack features. The behavior pattern analysis unit monitors the entropy fluctuation rate of the data stream. If it detects that the total entropy of the system drops or rises abnormally within a predetermined time, it determines that the system has been subjected to external interference attack. The abnormal intrusion prevention terminal is equipped with a feedback control loop. After successfully intercepting an attack, it extracts the behavioral feature parameters of the attack behavior and feeds them back to the dynamic phase change control engine, which automatically increases the initial encryption strength benchmark value of the corresponding data link. The abnormal intrusion defense terminal is also equipped with a physical decoy unit, which simulates false data streams and false edge nodes with low defense strength in the network to induce and capture the behavioral characteristics of attackers, and realizes the early command of the dynamic phase change control engine to complete the phase change locking of the entire system.

10. The oil and gas IoT-based communication data encryption and storage management system according to claim 9, characterized in that: The system adopts a distributed collaborative architecture. The IoT terminal sensing cluster includes sub-aggregation nodes with aggregation functions. The sub-aggregation nodes are configured to perform semantic association analysis on the raw data collected by all sensors in the sensing group and generate composite data packets with contextual logic. The central secure storage server adopts an integrity audit architecture based on blockchain technology, which packages the hash value, storage location and access log of data fragments onto the chain. The self-healing verification mechanism identifies data tampering behavior by comparing the hash evidence on the chain. The system also includes a global security situation presentation module, which receives security metadata from each edge node and storage server. On the visualization interface, it dynamically simulates the evolution of the security situation of the entire oil and gas field Internet of Things through phase diagrams. The color depth of the regions in the phase diagram represents the level of entropy, and the movement of the phase boundary line represents the dynamic defense response process of the system.

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