Risk early warning data communication method for integrated energy system

Abstract

The invention discloses an integrated energy system risk early warning data communication method, and relates to the technical field of communication, and the communication steps are as follows: S1, obtaining a risk early warning level of an integrated energy system, the risk early warning level being obtained by constructing a risk assessment index system to assess system operation data, dividing into at least three levels from low to high according to severity, and presetting risk early warning levels for matching to obtain early warning level matching; and S2, presetting a matching rule of communication channels, wherein the communication channels comprise a wired private network channel, a 5G emergency channel, a LoRa low-power-consumption channel and an NB-IoT universal channel. According to the method, the risk and channel requirements are accurately matched, a high-real-time channel is preferentially adapted to a high-risk channel, a low-power-consumption channel is selected to a low-risk channel, and the utilization rate of transmission resources is increased; the edge end dynamically monitors the channel state, and by combining with a standby channel switching mechanism, bad channels are avoided, and stable transmission in an extreme scene is guaranteed; and complex operation characteristics of a comprehensive energy system are met.

Description

A Data Communication Method for Risk Early Warning of Integrated Energy Systems Technical Field

[0001] This invention relates to the field of communication technology, specifically to a data communication method for risk early warning of integrated energy systems. Background Technology

[0002] With the acceleration of energy transition, integrated energy systems, as complex systems integrating multiple energy forms such as electricity, heat, gas, and cooling, have become the core carrier for improving energy utilization efficiency and promoting the consumption of renewable energy. However, integrated energy systems are characterized by multi-energy coupling, heterogeneous equipment, and dynamic changes in operating conditions. During operation, they are susceptible to factors such as equipment failure, supply and demand imbalance, extreme weather, and external interference, resulting in operational risks of varying levels. If risk warning information cannot be transmitted in a timely, reliable, and secure manner, it may lead to the spread of risks, causing power outages, equipment damage, or even safety accidents. Therefore, efficient communication of risk warning data is a key support for ensuring the safe and stable operation of integrated energy systems.

[0003] Currently, the communication methods for risk warning data in integrated energy systems largely borrow from the communication schemes of traditional single energy systems (such as power systems and heating systems). A dedicated communication mechanism adapted to the multi-energy coupling characteristics and differentiated risk levels has not yet been developed, revealing numerous shortcomings in practical applications. The selection of communication channels lacks dynamic adaptability to risk levels. Existing technologies mostly use fixed channels to transmit risk warning data of all levels, such as uniformly using wired private networks or general wireless channels, without differentiating channels based on the severity of the risk warning. High-risk warning data (such as system-level emergency alarm commands) has extremely high requirements for real-time transmission and reliability. If low-priority, low-reliability channels are used, transmission delays or interruptions are likely to occur, leading to delayed emergency response. Conversely, using high-energy-consuming, high-bandwidth channels for low-risk warning data (such as routine operation and maintenance information) results in resource waste. Therefore, we propose a communication method for risk warning data in integrated energy systems. Summary of the Invention

[0004] To address the aforementioned technical problems, a data communication method for risk early warning of integrated energy systems is provided. This technical solution solves the problems described above.

[0005] To achieve the above objectives, the technical solution adopted by this invention is as follows: a risk warning data communication method for an integrated energy system, comprising the following communication steps: S1, obtaining the risk warning level of the integrated energy system, wherein the risk warning level is obtained by constructing a risk assessment index system to evaluate the system operation data, and is divided into at least three levels according to severity from low to high, and pre-setting each risk warning level for matching to obtain a warning level match; S2, pre-setting the matching rules of the communication channel, wherein the communication channel includes a wired private network channel, a 5G emergency channel, a LoRa low-power channel, and an NB-IoT general channel; and monitoring the packet loss rate, latency jitter, and signal interference of each channel in real time at the edge. S1. Based on the risk warning level obtained in step S1 and the preset matching health score in step S2, the optimal corresponding communication channel is selected for different risk warnings. S2. The risk warning data is subjected to hierarchical encryption processing and transmitted to the target receiving end through the communication channel selected in step S3. S3. After receiving the warning data, the target receiving end sends back confirmation information with data slice verification fingerprint to the data sending end. The verification fingerprint is generated based on the block hash operation of the warning data and identifies the integrity of each data slice. If no confirmation information is received within a preset time, the data sending end switches to the backup communication channel for retransmission.

[0006] Preferably, in step S1, the principles and hierarchical framework for classifying early warning levels are defined in advance. The classification principles include the risk-oriented principle, the operability principle, and the dynamic adaptation principle; the risk characteristics and judgment framework for each level are defined; a three-level evaluation index system of target layer, criterion layer, and indicator layer is constructed, in which the comprehensive risk value R of the comprehensive energy system in the target layer is used for the final level determination; the criterion layer is based on the risk source to classify core dimensions, including equipment operation risk criterion, energy supply and demand risk criterion, coupling link risk criterion, and environmental management risk criterion; the weight of the indicators is determined by a subjective + objective combined weighting method, and the collection of operational data and outlier removal and standardization preprocessing are carried out simultaneously; the comprehensive risk value R is calculated through a linear weighted summation model; and the early warning level range is defined by combining historical data, industry standards, and expert judgment to complete the matching of risk value and early warning level.

[0007] Preferably, the formula for calculating the comprehensive risk value R is: Where n is the total number of indicators, Let i be the weight of the i-th indicator. Let i be the standardized value of the i-th indicator. The warning level range is divided into: Level 1 Low Risk: Level 2 Medium Risk: Level 3 High Risk: Substitute the real-time calculated comprehensive risk value R into the threshold range, match the corresponding warning level, and generate the warning level classification result.

[0008] Preferably, in step S2, the channel characteristics and business requirements are first sorted out, business priority + channel adaptability matching rules are preset, and the default channels and backup switching conditions for various services are clarified; an edge monitoring system is built to collect packet loss rate, latency jitter, signal interference and transmission energy consumption indicators for each channel; the indicators are standardized and weighted differently, and a health score of 0-100 points is generated based on weighted summation, which is divided into three levels: excellent, qualified and unqualified.

[0009] Preferably, the edge monitoring system is built by deploying channel monitoring units and data preprocessing units at edge nodes. The monitoring units are directly connected to the transmission links of each channel, supporting multi-protocol data acquisition. The acquisition frequency is set to 1 second / time for channels corresponding to emergency services and 5 seconds / time for channels corresponding to low-power general services. The grading standards are as follows: Excellent range is 80-100 points, all indicators of the channel meet the service requirements, and the transmission status is stable; Qualified range is 60-79 points, the core indicators meet the requirements, the secondary indicators have slight deviations, and there is no significant transmission risk; Unqualified range is <60 points, the core indicators do not meet the requirements, there is a risk of data loss and transmission delay, and a switching mechanism needs to be triggered; the score is updated synchronously with the indicator acquisition frequency.

[0010] Preferably, the step S3 for selecting the optimal corresponding communication channel for different risk warnings is as follows: Retrieve the risk level results from step S1 and the real-time health score of the communication channel from step S2; initially screen and eliminate substandard channels according to the health threshold; sort and re-screen the channels according to priority to determine the primary channel and backup channel with the highest health score; select the channel ranked first and with the highest health score as the optimal communication channel for the current risk level; select the channel ranked second as the backup and set switching trigger conditions; automatically switch to the backup channel when the primary channel's health score is below the threshold for three consecutive times, or when the single packet loss rate is >3% or the latency jitter is >50ms; establish a dynamic monitoring mechanism to continuously track the primary channel's status; immediately trigger an alarm if the primary channel's health score drops below the threshold; seamlessly switch to the backup channel when the trigger conditions are met; re-match the channel if the risk level is downgraded; iteratively optimize the matching rules and health threshold based on historical data.

[0011] Preferably, in step S4, the core principles of level and channel adaptation are first clarified, and a mapping relationship between low / medium / high risk and basic / enhanced / hybrid three-level encryption is established; the warning data is cleaned and organized, and the risk level and channel identifier are marked; encryption is performed according to the corresponding level mapping relationship, with lightweight symmetric encryption used for low risk, high-strength symmetric encryption with integrity verification for medium risk, and asymmetric + symmetric hybrid encryption used for high risk; the selected channel in S3 is adapted to optimize transmission parameters, and the transmission status is monitored in real time; the receiving end decrypts the data by matching the key according to the tag, verifies the data integrity and restores it, and at the same time establishes a hierarchical key management mechanism to regularly optimize the encryption process.

[0012] Preferably, lightweight symmetric encryption retrieves a preset L1-level fixed key from the key management office; encrypts the preprocessed warning data using AES-128; generates an encrypted data packet and appends a simple checksum; high-strength symmetric encryption retrieves an L2-level dynamic key; encrypts the data using AES-256; appends an HMAC-SHA256 integrity checksum; adds a key version identifier to the encrypted data packet; and asymmetric + symmetric hybrid encryption randomly generates a temporary AES-256 session key; encrypts the temporary session key using the receiver's RSA public key; encrypts the warning data using the temporary session key using AES-256; and concatenates the encrypted session key, encrypted data, and HMAC-SHA384 checksum to generate the final encrypted data packet.

[0013] Preferably, step S5 specifically includes: the receiving end restores the fragmented data, divides it into blocks according to preset rules and generates a hash verification fingerprint, and compares it with the fingerprint of the sending end to complete the integrity verification; encapsulates confirmation information containing a fingerprint summary table and receiving status information, and transmits it back first through the original channel; the sending end starts a timer after transmission and sets a differentiated timeout threshold according to the channel characteristics; if no confirmation information is received within the timeout or the information is invalid, the health of the backup channel is checked, and if it meets the standard, it is switched to the backup channel for retransmission. If the same data is retransmitted less than 3 times and still fails, an alarm is triggered; if information is received within the timeout, it is processed according to the receiving status. If some failures occur, only the abnormal slice is retransmitted. If it succeeds, the process is terminated.

[0014] Preferably, the data is divided into blocks according to preset rules and hash verification fingerprints are generated. By following the same standard preset by the sender, the block size is set according to the data type differences. Text-type warning data is divided into blocks of 1KB each, and high-risk data with attachments is divided into blocks of 4KB each. The fingerprint of each block of data is calculated independently, and the overall hash value of the complete data is calculated for global verification. The fingerprint is compared with the fingerprint of the sender by extracting the list of slice fingerprints embedded in the block header of the sender one by one and comparing it with the fingerprints of each block generated by the receiver at the byte level.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes to accurately match risk and channel requirements, prioritizing high-risk, high-real-time channels and selecting low-power channels for low-risk applications, thereby improving the utilization rate of transmission resources; dynamically monitoring channel status at the edge and combining it with a backup channel switching mechanism to avoid faulty channels and ensure stable transmission in extreme scenarios; hierarchical encryption adapts to risk levels, coupled with block hash verification, to build a solid defense for data security and integrity; full-process traceable closed-loop management reduces the difficulty of operation and maintenance troubleshooting and shortens fault response time; and improves the universality and scalability of the method, fitting the complex operating characteristics of integrated energy systems. Attached Figure Description

[0016] Figure 1 is a flowchart of the communication steps of the present invention. Detailed Implementation

[0017] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0018] Referring to Figure 1, a risk warning data communication method for an integrated energy system includes the following communication steps: S1, obtaining the risk warning level of the integrated energy system. The risk warning level is obtained by constructing a risk assessment index system to evaluate the system's operating data, and is divided into at least three levels from low to high severity. Preset matching rules for each risk warning level are used to obtain the warning level matching. S2, pre-setting matching rules for communication channels, including wired private network channels, 5G emergency channels, LoRa low-power channels, and NB-IoT general channels; real-time monitoring of packet loss rate, latency jitter, signal interference, and transmission energy consumption of each channel is conducted at the edge. S3. Based on the risk warning level obtained in step S1 and the preset matching health score in step S2, select the best corresponding communication channel for different risk warnings; S4. Perform hierarchical encryption processing on the risk warning data and transmit it to the target receiving end through the communication channel selected in step S3; S5. After receiving the warning data, the target receiving end sends back confirmation information with data slice verification fingerprint to the data sending end. The verification fingerprint is generated based on the block hash operation of the warning data and identifies the integrity of each data slice; if no confirmation information is received within a preset time, the data sending end switches to the backup communication channel for retransmission.

[0019] This application achieves differentiated and precise allocation of transmission resources by defining risk warning levels in S1 and establishing a risk level-channel characteristic matching mechanism in S3: For high-risk emergency alarms, priority is given to matching high-real-time and high-reliability channels such as 5G emergency channels and wired private network channels to ensure rapid delivery of instructions; for low-risk routine operation and maintenance data, LoRa low-power and NB-IoT general channels are matched to reduce transmission energy consumption and operating costs. This on-demand allocation mode avoids high-level channel resources being occupied by low-risk data and also prevents low-power channels from being unable to support high-risk data transmission, significantly improving the overall utilization efficiency of transmission resources; and real-time monitoring of packet loss rate and latency of each channel is achieved at the edge. By analyzing key indicators such as jitter and generating a dynamic health score, and combining the channel selection logic of S3 with the backup channel switching mechanism of S5, a reliable transmission system with dynamic adaptation and redundant backup is constructed. On the one hand, it can avoid channels with poor conditions (such as high packet loss and strong interference) in real time and prioritize channels with qualified health, reducing the risk of transmission failure from the source. On the other hand, when the main channel transmission times out or fails, it can quickly switch to the preset backup channel for retransmission, and the number of retransmissions of the same data is controllable, which avoids data loss and prevents excessive retransmission from consuming resources. It is especially suitable for outdoor deployment of integrated energy systems and scenarios with complex environmental interference, improving the stability of early warning data transmission under extreme conditions.

[0020] Step S1 pre-defines the principles and hierarchical framework for classifying early warning levels. These principles include risk-oriented, operability-based, and dynamic adaptation principles. It defines the risk characteristics and judgment framework for each level and constructs a three-tiered evaluation index system: target layer, criterion layer, and indicator layer. The target layer uses the comprehensive energy system risk value R to determine the final level. The criterion layer classifies core dimensions based on risk sources, including equipment operation risk criteria, energy supply and demand risk criteria, coupling link risk criteria, and environmental management risk criteria. A subjective + objective weighting method is used to determine indicator weights, while simultaneously collecting operational data, removing outliers, and performing standardized preprocessing. The comprehensive risk value R is calculated using a linear weighted summation model. Finally, historical data, industry standards, and expert judgment are combined to define early warning level ranges, thus matching risk values ​​with early warning levels.

[0021] This application clearly defines the correspondence between risk severity, impact scope, and response priority. Low-level risks only affect the local operation of a single device and do not require emergency response; medium-level risks affect a single subsystem (such as power distribution or heating subsystems) and require control measures to be initiated within one hour; high-level risks propagate across subsystems and may cause a system-wide power outage, requiring immediate emergency response. All risk characteristics must be quantified to avoid vague descriptions. Pre-defined operating condition adaptation rules allow for lowering the high-risk threshold by 10%-20% for peak load scenarios (such as peak winter heating and peak summer cooling) and extreme weather scenarios (cold waves, typhoons, and high temperatures), improving early warning sensitivity. The application is adapted to system expansion and equipment upgrade scenarios, supporting dynamic updates of the indicator system and level thresholds. Equipment operation risk criteria focus on core equipment such as power generation, energy storage, conversion, and transmission, covering equipment health status and operational stability, with data sources including equipment monitoring sensors and SCADA systems.

[0022] The data focuses on the supply and demand balance of multiple energy media, including electricity, heat, cooling, and gas, covering core issues such as load fluctuations and output deviations. Data sources include smart meters, heat meters, and gas flow meters. For core multi-energy conversion links (such as CHP cogeneration units, heat pumps, and gas-steam combined cycle systems), the data covers coupling characteristics such as conversion efficiency and response delay. Data sources include conversion equipment controllers. The data also covers external environmental interference (extreme temperatures, precipitation, and strong winds) and internal management shortcomings (qualification of maintenance personnel and emergency material reserves). Data sources include meteorological monitoring stations and operation and maintenance management platforms.

[0023] The formula for calculating the overall risk value R is: Where n is the total number of indicators, Let i be the weight of the i-th indicator. Let i be the standardized value of the i-th indicator. The warning level range is divided into: Level 1 Low Risk: Level 2 Medium Risk: Level 3 High Risk: Substitute the real-time calculated comprehensive risk value R into the threshold range, match the corresponding warning level, and generate the warning level classification result.

[0024] This application employs a clear numerical threshold division for the early warning level range, transforming the abstract concept of "risk level" into a concrete numerical range: the boundaries between low, medium, and high risk (0.3, 0.7) are clearly quantifiable. Maintenance personnel do not need subjective judgment; they can directly substitute the real-time calculated R value to quickly match the level, significantly reducing errors and disputes in level determination. Furthermore, the three-level division aligns with the actual needs of integrated energy system operation and maintenance: low risk corresponds to routine maintenance, medium risk to special management, and high risk to emergency response, forming a direct link between "numerical value - level - response measures," thus enhancing the targeted nature of risk management.

[0025] In step S2, the characteristics of the channels and business requirements are first sorted out, and the business priority and channel adaptability matching rules are preset to clarify the default channels and backup switching conditions for various services. An edge monitoring system is built to collect packet loss rate, latency jitter, signal interference and transmission energy consumption indicators for each channel. The indicators are standardized and weighted differently, and a health score of 0-100 points is generated based on weighted summation, which is divided into three levels: excellent, qualified and unqualified.

[0026] The core objective of step S2 is to establish a precise correlation mechanism between business requirements, channel characteristics, and health monitoring, providing a quantitative basis for S3 channel selection. This involves a detailed analysis of channel characteristics and business requirements: clearly defining the quantitative characteristics of four types of channels—wired private networks, 5G emergency, LoRa low-power, and NB-IoT general—such as transmission rate, latency, and energy consumption. Simultaneously, early warning services are categorized into four types based on real-time and reliability requirements: core emergency, high-volume low-power, general transmission, and high-bandwidth, clarifying the core requirements and priorities of each type of service. A pre-defined "business priority + channel compatibility" dual-guided matching rule is established: a default channel is bound to each type of service, such as the default 5G emergency channel for core emergency services and the default LoRa channel for high-volume low-power services. Compatibility verification standards and backup channel switching conditions are also set, including health thresholds, packet loss rate, and latency triggering conditions, along with corresponding channel priority ranking and switching log recording. Rules; Establish a comprehensive edge channel monitoring system: focusing on four core indicators—packet loss rate, latency jitter, signal interference, and transmission power consumption—and clarifying the business-differentiated thresholds for each indicator; deploy multi-protocol monitoring and data preprocessing units at edge nodes, setting differentiated collection frequencies according to channel importance, and using a dual mechanism to eliminate abnormal data to ensure the effectiveness of monitoring data; construct an indicator processing and health scoring system: using extreme value standardization to map the raw data of the four types of indicators to a 0-100 score range, and assigning indicator weights differently according to the channel application scenario (emergency channels focus on latency and packet loss rate, low-power channels focus on power consumption and interference); calculate the total health score through weighted summation, dividing it into three levels: excellent (80-100 points), qualified (60-79 points), and unqualified (<60 points), achieving an intuitive quantitative assessment of channel status and providing a core basis for subsequent channel selection and switching.

[0027] An edge monitoring system is established by deploying channel monitoring units and data preprocessing units at edge nodes. The monitoring units are directly connected to the transmission links of each channel, supporting multi-protocol data acquisition. The acquisition frequency is set to 1 second / time for channels corresponding to emergency services and 5 seconds / time for channels corresponding to low-power general services. The grading standards are as follows: Excellent (80-100 points): all channel indicators meet business requirements, and transmission is stable; Qualified (60-79 points): core indicators meet requirements, secondary indicators have slight deviations, and there is no significant transmission risk; Unqualified (<60 points): core indicators do not meet requirements, there is a risk of data loss and transmission delay, and a switching mechanism needs to be triggered. The score is updated synchronously with the indicator acquisition frequency.

[0028] Step S3 involves selecting the optimal communication channel for different risk warnings as follows: First, initiate the data retrieval process. From the risk assessment results in Step S1, accurately extract the current risk warning level of the integrated energy system. Simultaneously, associate this level with the corresponding core transmission requirements. For example, high-risk warnings require high-real-time, high-reliability transmission links, while low-risk warnings prioritize low-power, low-cost communication channels. Second, retrieve the real-time health scores and corresponding status levels of the four types of communication channels from the edge monitoring system in Step S2. Ensure the acquired data includes a complete health status description for each type of channel, providing an accurate basis for subsequent selection. Third, based on the current risk warning level, determine the minimum health threshold for the corresponding channel. This threshold is directly linked to the risk level: the health threshold for high-risk warnings is excellent, retaining only channels with excellent health scores; the threshold for medium-risk warnings is good or above, eliminating unqualified channels. The channel selection process is as follows: For low-risk warnings, the threshold is a qualified level; channels with health scores below the qualified line are excluded. This initial screening quickly removes channels that are in poor condition and cannot meet the transmission requirements of the current risk level, reducing unnecessary workload in subsequent sorting and ensuring that all subsequent candidate channels have basic compatibility. After the initial screening, the candidate channels are sorted and re-screened according to the preset risk level-channel priority rules. The core logic of the priority rules is to match the core transmission requirements of the risk warning: For high-risk warnings, the priority is emergency channel, wired private network channel, general channel, and low-power channel, prioritizing real-time transmission and reliability; for medium-risk warnings, the priority is adjusted to general channel, wired private network channel, emergency channel, and low-power channel, balancing reliability and transmission efficiency; for low-risk warnings, the priority is low-power channel and general channel first, prioritizing control of transmission energy consumption and operating costs. After sorting, the channel with the highest health score at the top of the list is selected as the best primary communication channel for the current risk level. Simultaneously, the second-ranked channel is selected as a backup channel to ensure rapid replacement in case of anomalies in the primary channel. To prevent interruption of early warning data transmission due to sudden failure of the primary channel, clear and feasible switching trigger conditions must be set for the backup channel, specifically including three scenarios: First, if the primary channel's health score is below the health threshold corresponding to the current risk level for three consecutive monitoring sessions, it indicates a continuous deterioration in the channel's condition, making it unable to stably support transmission; second, if the packet loss rate of the primary channel in a single monitoring session exceeds 3%, the reliability of data transmission has significantly declined; third, if the latency jitter of the primary channel in a single monitoring session exceeds 50 milliseconds, it cannot meet the requirements for real-time transmission.When any scenario is triggered, the system immediately initiates the primary / backup switchover process. The switchover follows the principle of "connect first, then disconnect," first establishing a communication link between the backup channel and the receiving end. Once the link is confirmed to be functional and data transmission is normal, the connection to the original primary channel is disconnected, ensuring no data loss and uninterrupted transmission. After the primary / backup channel is determined, a full-cycle dynamic monitoring mechanism is activated. The monitoring frequency is consistent with the channel indicator collection frequency in step S2: high-risk warnings correspond to high-frequency monitoring of the primary / backup channels to ensure timely detection of channel status fluctuations; low-risk warnings correspond to channels that can be monitored at a conventional frequency to balance monitoring accuracy and system energy consumption. During monitoring, if the primary channel's health level drops below the threshold, in addition to triggering the switchover process, an alarm message must be immediately pushed to the operation and maintenance management platform, simultaneously recording the channel anomaly time, anomaly indicators, and other information to facilitate subsequent troubleshooting. If the system detects a downgrade in the risk warning level, the aforementioned channel selection process must be re-executed. This involves retrieving the latest risk level, filtering channels that currently meet the health requirements, and prioritizing and determining new primary and backup channels to ensure that channel selection always accurately matches risk needs. Historical data on channel selection and transmission should be regularly compiled, including transmission success rate, data loss rate, number of switchover triggers, and maintenance records for each channel under different risk levels. By analyzing this historical data, channel matching rules can be optimized in a targeted manner. For example, the priority order of channels corresponding to a certain risk level can be adjusted, the health threshold values ​​for different risk levels can be corrected, or channel adaptation rules for special scenarios (such as extreme weather or peak system load) can be added. The optimized rules need to be verified in a small-scale scenario to confirm that they can improve transmission reliability and reduce switchover frequency before being updated to the formal operating system. This forms a closed-loop management of "selection-monitoring-optimization" to continuously improve the accuracy and stability of channel matching.

[0029] Step S4 first clarifies the core principles of risk level and channel adaptation, establishing a mapping relationship between low / medium / high risk and basic / enhanced / hybrid three-level encryption; it cleans and organizes the warning data, labeling it with risk level and channel identifier; encryption is performed according to the corresponding risk level mapping relationship: lightweight symmetric encryption is used for low risk, high-strength symmetric encryption with integrity verification is used for medium risk, and asymmetric + symmetric hybrid encryption is used for high risk; transmission parameters are optimized for the selected channel in S3, and the transmission status is monitored in real time; the receiving end decrypts the data by matching the key with the tag, verifies data integrity and restores it, and at the same time establishes a hierarchical key management mechanism to regularly optimize the encryption process.

[0030] This application precisely maps risk to encryption levels, using lightweight encryption for low-risk scenarios and hybrid encryption for high-risk scenarios to avoid over-encryption that wastes resources or provides insufficient protection, thus balancing security and efficiency. Data cleaning and labeling improve quality and efficiency, reducing data volume and lowering transmission and encryption costs, while clear labeling avoids process mismatches. Optimized parameters for S3 channels, combined with real-time monitoring, ensure synergy between encrypted data and channel characteristics, enhancing transmission stability. The receiving end accurately decrypts and verifies integrity based on tags, preventing data tampering and loss, and ensuring data trustworthiness. Hierarchical key management and regular optimization reduce the risk of key leakage, improve operational controllability, and adapt to the dynamic operational needs of the system.

[0031] Lightweight symmetric encryption retrieves a preset L1-level fixed key from the key management office; encrypts the pre-processed warning data using AES-128; generates an encrypted data packet and appends a simple checksum. High-strength symmetric encryption retrieves a L2-level dynamic key; encrypts the data using AES-256; appends an HMAC-SHA256 integrity checksum; and adds a key version identifier to the encrypted data packet. Asymmetric + symmetric hybrid encryption randomly generates a temporary AES-256 session key; encrypts the temporary session key using the receiver's RSA public key; encrypts the warning data using the temporary session key using AES-256; and concatenates the encrypted session key, encrypted data, and HMAC-SHA384 checksum to generate the final encrypted data packet.

[0032] The S5 steps specifically include: the receiving end restores the fragmented data, divides it into blocks according to preset rules and generates a hash verification fingerprint, and compares it with the sending end's fingerprint to complete the integrity verification; encapsulates confirmation information containing a fingerprint summary table and receiving status information, and transmits it back via the original channel first; the sending end starts a timer after transmission and sets a differentiated timeout threshold according to the channel characteristics; if no confirmation information is received within the timeout or the information is invalid, the health of the backup channel is checked, and if it meets the standard, it is switched to the backup channel for retransmission. If the same data is retransmitted less than 3 times and still fails, an alarm is triggered; if information is received within the timeout, it is processed according to the receiving status. If some failures occur, only the abnormal slice is retransmitted. If it succeeds, the process is terminated.

[0033] This application's hash verification fingerprint mechanism accurately determines the integrity of data slices, preventing tampered or missing data from flowing into subsequent stages and ensuring the authenticity and reliability of early warning information. Confirmation information feedback containing key details allows the sender to clearly understand the receiving status, providing a basis for differentiated processing. Differentiated timeout thresholds adapt to different channel characteristics, avoiding misjudgments or resource waste caused by uniform standards. Backup channel switching and retransmission limit ensure uninterrupted high-risk data transmission while preventing excessive retransmissions from consuming bandwidth. Partial retransmission mechanisms target only abnormal slices, significantly reducing energy consumption and transmission costs. Full-process status traceability facilitates rapid fault diagnosis by operations and maintenance personnel, improving problem-solving efficiency and strengthening the last line of defense for early warning data transmission.

[0034] The data is divided into blocks according to preset rules, and hash verification fingerprints are generated. By following the same standard preset by the sender, the block size is set according to the data type. Text-type warning data is divided into blocks of 1KB each, and high-risk data with attachments is divided into blocks of 4KB each. The fingerprint of each block of data is calculated independently, and the overall hash value of the complete data is calculated for global verification. The fingerprint is compared with the fingerprint of the sender by extracting the list of slice fingerprints embedded in the block header of the sender and comparing them with the fingerprints of each block generated by the receiver at the byte level.

[0035] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A data communication method for risk early warning of integrated energy systems, characterized in that, The communication steps are as follows: S1. Obtain the risk warning level of the integrated energy system. The risk warning level is obtained by constructing a risk assessment index system to evaluate the system operation data. It is divided into at least three levels from low to high according to the severity. Preset each risk warning level and match it to obtain the warning level matching. S2. Preset matching rules for communication channels, including wired private network channels, 5G emergency channels, LoRa low-power channels, and NB-IoT general channels; generate dynamic health scores for each channel by real-time monitoring of packet loss rate, latency jitter, signal interference, and transmission energy consumption at the edge; S3. Based on the risk warning level obtained in step S1, and combined with the preset matching health scores in step S2, select the best corresponding communication channel for different risk warnings; S4. Perform hierarchical encryption on the risk warning data and transmit it to the target receiving end through the communication channel selected in step S3; S5. After receiving the warning data, the target receiving end sends back confirmation information with data slice verification fingerprints to the data sending end. The verification fingerprints are generated based on the block hash operation of the warning data and identify the integrity of each data slice. If no confirmation is received within the preset time, the data sender switches to the backup communication channel to retransmit.

2. The integrated energy system risk early warning data communication method according to claim 1, characterized in that: Step S1 pre-defines the principles and hierarchical framework for classifying early warning levels. The classification principles include the risk-oriented principle, the operability principle, and the dynamic adaptation principle; and defines the risk characteristics and judgment framework for each level. A three-level evaluation index system was constructed, consisting of a target layer, a criterion layer, and an indicator layer. The comprehensive risk value R of the comprehensive energy system at the target layer is used to determine the final level. The criteria layer is divided into core dimensions based on the source of risk, including equipment operation risk criteria, energy supply and demand risk criteria, coupling link risk criteria, and environmental management risk criteria; The weights of the indicators are determined by a combination of subjective and objective weighting methods, while simultaneously collecting operational data, removing outliers, and performing standardized preprocessing. The comprehensive risk value R is calculated using a linear weighted summation model; the warning level range is defined by combining historical data, industry standards, and expert judgment, thus completing the matching between the risk value and the warning level.

3. The integrated energy system risk early warning data communication method according to claim 2, characterized in that: The formula for calculating the overall risk value R is: Where n is the total number of indicators, Let i be the weight of the i-th indicator. Let i be the standardized value of the i-th indicator. ； The warning level range is divided into: Level 1 Low Risk: Level 2 Medium Risk: Level 3 High Risk: ； The real-time calculated comprehensive risk value R is substituted into the threshold range, matched with the corresponding warning level, and the warning level classification result is generated.

4. The integrated energy system risk early warning data communication method according to claim 1, characterized in that: In step S2, the characteristics of the channels and business requirements are first sorted out, and the business priority and channel adaptability matching rules are preset to clarify the default channels and backup switching conditions for various services. An edge monitoring system is built to collect packet loss rate, latency jitter, signal interference and transmission energy consumption indicators for each channel. The indicators are standardized and weighted differently, and a health score of 0-100 points is generated based on weighted summation, which is divided into three levels: excellent, qualified and unqualified.

5. The integrated energy system risk early warning data communication method according to claim 4, characterized in that: An edge monitoring system is established by deploying channel monitoring units and data preprocessing units at edge nodes. The monitoring units are directly connected to the transmission links of each channel, supporting multi-protocol data acquisition. The acquisition frequency is set to 1 second / time for channels corresponding to emergency services and 5 seconds / time for channels corresponding to low-power general services. The grading standards are as follows: Excellent (80-100 points): all channel indicators meet business requirements, and transmission is stable; Qualified (60-79 points): core indicators meet requirements, secondary indicators have slight deviations, and there is no significant transmission risk; Unqualified (<60 points): core indicators do not meet requirements, there is a risk of data loss and transmission delay, and a switching mechanism needs to be triggered. The score is updated synchronously with the indicator acquisition frequency.

6. The integrated energy system risk early warning data communication method according to claim 1, characterized in that, The steps in S3 for selecting the optimal communication channel for different risk warnings are as follows: Retrieve the risk level results from S1 and the real-time health scores of the communication channels from S2; initially screen and eliminate substandard channels according to the health threshold; sort and re-screen the channels according to priority to determine the primary and backup channels with the highest health scores; select the channel ranked first and with the highest health score as the optimal communication channel for the current risk level; select the second-ranked channel as the backup and set switching trigger conditions; automatically switch to the backup channel when the primary channel's health score is below the threshold for three consecutive times, or when the single packet loss rate is >3% or the latency jitter is >50ms; establish a dynamic monitoring mechanism to continuously track the primary channel's status; if the primary channel's health score drops below the threshold, immediately trigger an alarm; seamlessly switch to the backup channel when the trigger conditions are met; re-match the channel if the risk level is downgraded. Based on historical data, the matching rules and health threshold values ​​are iteratively optimized.

7. The integrated energy system risk early warning data communication method according to claim 1, characterized in that: The S4 step first clarifies the core principles of level adaptation and channel adaptation, and establishes a mapping relationship between low / medium / high risk and basic / enhanced / hybrid three-level encryption; Clean and organize the early warning data, and label it with risk level and channel identifier; Encryption is performed according to the corresponding level mapping relationship. Lightweight symmetric encryption is used for low risk, high-strength symmetric encryption with integrity verification is used for medium risk, and asymmetric + symmetric hybrid encryption is used for high risk. The transmission parameters are optimized for the selected S3 channel and the transmission status is monitored in real time. The receiving end decrypts the data by matching the key with the tag, verifies the data integrity and restores it. At the same time, a hierarchical key management mechanism is established and the encryption process is optimized regularly.

8. The integrated energy system risk early warning data communication method according to claim 7, characterized in that: Lightweight symmetric encryption retrieves a preset L1-level fixed key from the key management office; encrypts the pre-processed warning data using AES-128; generates an encrypted data packet and appends a simple checksum. High-strength symmetric encryption retrieves a L2-level dynamic key; encrypts the data using AES-256; appends an HMAC-SHA256 integrity checksum; and adds a key version identifier to the encrypted data packet. Asymmetric + symmetric hybrid encryption randomly generates a temporary AES-256 session key; encrypts the temporary session key using the receiver's RSA public key; encrypts the warning data using the temporary session key using AES-256; and concatenates the encrypted session key, encrypted data, and HMAC-SHA384 checksum to generate the final encrypted data packet.

9. The integrated energy system risk early warning data communication method according to claim 1, characterized in that, The S5 steps specifically include: the receiving end restores the fragmented data, divides it into blocks according to preset rules and generates a hash verification fingerprint, and compares it with the sending end's fingerprint to complete the integrity verification; encapsulates confirmation information containing a fingerprint summary table and receiving status information, and transmits it back via the original channel first; the sending end starts a timer after transmission and sets a differentiated timeout threshold according to the channel characteristics; if no confirmation information is received within the timeout or the information is invalid, the health of the backup channel is checked, and if it meets the standard, it is switched to the backup channel for retransmission. If the same data is retransmitted less than 3 times and still fails, an alarm is triggered; if information is received within the timeout, it is processed according to the receiving status. If some failures occur, only the abnormal slice is retransmitted. If it succeeds, the process is terminated.

10. The integrated energy system risk early warning data communication method according to claim 9, characterized in that: The data is divided into blocks according to preset rules and hash verification fingerprints are generated. By following the same standard preset by the sender, the block size is set according to the data type. Text-type warning data is divided into blocks of 1KB each, and high-risk data with attachments is divided into blocks of 4KB each. The fingerprint of each block of data is calculated independently, and the overall hash value of the complete data is calculated for global verification. The fingerprint comparison with the sending end is performed by extracting the list of slice fingerprints embedded in the slice header of the sending end one by one and comparing them with each block fingerprint generated by the receiving end at the byte level.

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