Edge computing-based encrypted storage method for supply chain data

Abstract

This invention belongs to the field of data encryption and storage technology, specifically relating to a method for encrypted storage of supply chain data based on edge computing. The method includes: encrypting pre-processed supply chain data of edge nodes using a key generated by a double-ratchet algorithm; grouping and packaging the encrypted supply chain data of the edge nodes into multiple data packets and transmitting them to the cloud; wherein, during the key generation process of the double-ratchet algorithm, the ratchet stepping cycle of the algorithm is determined based on the attack level of network communication between the edge nodes and the cloud, as well as the available computing power of the edge nodes, and the ratchet stepping is performed according to the cycle to update the key. This invention can effectively address the challenges posed by limited computing power and complex network environments in edge computing scenarios, achieving a balance between security and efficiency.

Description

Technical Field

[0001] This invention relates to the field of data encryption and storage technology. More specifically, this invention relates to a method for encrypted storage of supply chain data based on edge computing. Background Technology

[0002] With the rapid development of IoT and edge computing technologies, supply chain management is gradually evolving towards digitalization and intelligence. In modern supply chain systems, a large amount of data (such as data from logistics, warehousing, and production) is collected through edge devices and transmitted to the cloud for storage and analysis.

[0003] However, these data often contain sensitive information, thus posing extremely high requirements for data security. Although traditional centralized encryption storage methods can provide a certain degree of security, they face many challenges in edge computing scenarios. In order to address these challenges, researchers have proposed some encryption schemes based on the Double Ratchet Algorithm in recent years.

[0004] The double-ratchet algorithm is an efficient key negotiation protocol that can dynamically generate new session keys between communicating parties, thereby improving the security of data transmission. The core idea of ​​the double-ratchet algorithm is to use two independent ratchet wheels to manage long-term keys and temporary session keys. These two independent ratchet wheels are the symmetric key ratchet and the DH (Diffie-Hellman) ratchet. During communication, the double-ratchet algorithm can ensure that the keys of the two communicating parties are always synchronized and updated by ratcheting these two independent ratchet wheels.

[0005] Although the double ratchet algorithm has high security, it still faces problems such as excessive consumption of computing resources and the contradiction between security and efficiency in edge computing scenarios with limited computing power and complex network environments. Summary of the Invention

[0006] To address the technical challenges posed by the dual-ratchet algorithm in edge computing scenarios with limited computing power and complex network environments, namely excessive computational resource consumption and the trade-off between security and efficiency, this invention provides a supply chain data encryption and storage method based on edge computing. The method includes: collecting supply chain data through edge devices; combining the supply chain data collected by multiple edge devices connected to an edge node into the supply chain data of that edge node; encrypting the preprocessed supply chain data of the edge node using a key generated by the dual-ratchet algorithm; and grouping and packaging the encrypted supply chain data of the edge node into multiple data packets for transmission to the cloud. During the key generation process of the dual-ratchet algorithm, a ratchet step is performed periodically to update the key. The method for obtaining the cycle is as follows: real-time monitoring of key indicators in network communication between edge nodes and the cloud; determination of the relative risk value of each attack type based on each key indicator; weighted summation of the relative risk values ​​of each attack type using their respective weights to obtain the attack severity of network communication between edge nodes and the cloud; calculation of the maximum theoretical computing power of edge nodes based on their hardware configuration; calculation of the real-time load availability of edge nodes based on their current load; calculation of the product of the maximum theoretical computing power and the real-time load availability of edge nodes to obtain the available computing power of edge nodes; and determination of the ratchet stepping cycle of the dual ratchet algorithm based on the attack severity of network communication between edge nodes and the cloud, and the available computing power of edge nodes.

[0007] This invention determines the ratchet stepping cycle of the dual ratchet algorithm based on the attack level of network communication between edge nodes and the cloud, as well as the available computing power of the edge nodes. It dynamically adjusts the key update frequency according to the needs of different attack risks and computing power scenarios, which can effectively cope with the challenges brought by limited computing power and complex network environment in edge computing scenarios and achieve a balance between security and efficiency.

[0008] Preferably, the key indicators in the network communication include the latency anomaly ratio, packet loss rate, and encrypted message duplication rate, which are used to monitor three attack types: man-in-the-middle attack, packet loss attack, and return attack, respectively. The latency anomaly ratio, packet loss rate, and encrypted message duplication rate are respectively designated as the first, second, and third key indicators, and the monitoring of man-in-the-middle attack, packet loss attack, and return attack is respectively designated as the first, second, and third attack types.

[0009] Preferably, determining the relative risk value of each attack type based on key indicators includes: setting maximum tolerance values ​​for latency anomaly ratio, packet loss rate, and encrypted message duplication rate; for the first... Key indicators Calculate the first Key indicators and the first The difference between the maximum tolerance values ​​of the three key indicators; if this difference is greater than 0, then the third... The relative risk value of the first attack type is equal to the difference between the first and second attack types. The ratio of the maximum tolerance value of the key indicators; if the difference is not greater than 0, then the... The relative risk value for this type of attack is 0.

[0010] This invention sets a maximum tolerance value for each key indicator and calculates a relative risk value based on the difference between the actual value and the maximum tolerance value, thereby accurately quantifying the risk level of each attack type and providing a real-time basis for security assessment.

[0011] Preferably, the weights of all attack types are greater than 0, and the sum of the weights of all attack types is equal to 1, with the weights of the first, second, and third attack types decreasing sequentially.

[0012] Preferably, the hardware configuration of the edge node is reported by the edge node during registration, including CPU clock speed, number of cores, memory capacity and GPU capability, and are respectively recorded as the first computing power influencing factor, the second computing power influencing factor, the third computing power influencing factor and the fourth computing power influencing factor of the edge node.

[0013] Preferably, the step of calculating the maximum theoretical computing power of the edge node based on its hardware configuration includes: weighting and summing the computing power scores of the first, second, third, and fourth computing power influencing factors of the edge node according to the first, second, third, and fourth weights to obtain the maximum theoretical computing power of the edge node; the first, second, third, and fourth weights are all greater than 0, and the sum of all weights is equal to 1.

[0014] This invention obtains the maximum theoretical computing power of edge nodes by mapping scores and setting weights for various factors affecting computing power, which can accurately evaluate the hardware performance of edge nodes and provide a foundation for subsequent calculations.

[0015] Preferably, the mapping function for the computing power score is as follows: for the first computing power influencing factor, i.e., CPU clock speed, the score is 100 points per 1 GHz; for the second computing power influencing factor, i.e., number of cores, the score is 100 points per core; for the third computing power influencing factor, i.e., memory capacity, the score is 100 points per GB; and for the fourth computing power influencing factor, i.e., GPU capability, the score is 100 points when the edge node's hardware configuration has GPU capability, and 0 points when the edge node's hardware configuration does not have GPU capability.

[0016] Preferably, the step of calculating the real-time load availability of the edge nodes based on their current load includes: each edge node periodically sending its current load to the cloud, the current load including CPU utilization and memory utilization, both of which are percentages; calculating the average of the CPU utilization and memory utilization of the edge nodes, and taking the difference between 1 and the average as the real-time load availability of the edge nodes.

[0017] Preferably, determining the ratchet stepping cycle of the dual ratchet algorithm based on the attack level of the network communication between the edge node and the cloud, and the available computing power of the edge node, includes: calculating the longest acceptable cycle of the edge node based on the available computing power of the edge node; taking the negative number of the attack level of the network communication between the edge node and the cloud, and inputting it into the Sigmoid function; multiplying the output of the Sigmoid function by 2 and then by the longest acceptable cycle of the edge node; rounding up the obtained product; and using the rounded result as the ratchet stepping cycle of the dual ratchet algorithm.

[0018] This invention combines the attack level and the longest acceptable cycle to determine the ratchet stepping cycle, thereby shortening the cycle to improve security in high-attack-risk scenarios and extending the cycle to save resources in low-attack-risk scenarios.

[0019] Preferably, the step of calculating the longest acceptable period of an edge node based on its available computing power includes: calculating the difference between the available computing power of the edge node and the average available computing power of all edge nodes; calculating the ratio of the difference to the average available computing power of all edge nodes; taking the negative of the ratio and inputting it into the natural exponential function; and multiplying the output of the natural exponential function by a preset length to obtain the longest acceptable period of the edge node.

[0020] This invention calculates the longest acceptable cycle of an edge node by measuring the deviation of its available computing power from the average computing power of all edge nodes and by using a preset length. It then dynamically adjusts the cycle based on the computing power difference, rationally allocates resources, avoids low-computing-power nodes from updating keys prematurely, and achieves optimal resource utilization.

[0021] The beneficial effects of this invention are as follows:

[0022] This invention determines the ratchet stepping cycle of the dual ratchet algorithm based on the attack level of network communication between edge nodes and the cloud, as well as the available computing power of the edge nodes. It dynamically adjusts the key update frequency according to the needs of different attack risks and computing power scenarios, which can effectively cope with the challenges brought by limited computing power and complex network environment in edge computing scenarios and achieve a balance between security and efficiency. Attached Figure Description

[0023] Figure 1This is a flowchart illustrating the edge computing-based supply chain data encryption and storage method of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] This invention discloses a method for encrypted storage of supply chain data based on edge computing, referring to... Figure 1 This includes steps S1 to S4:

[0027] S1. Collect supply chain data through edge devices, and combine the supply chain data collected by multiple edge devices connected to the edge node into the supply chain data of the edge node; encrypt the preprocessed supply chain data of the edge node according to the key generated by the double ratchet algorithm, and group and package the encrypted supply chain data of the edge node into multiple data packets of the edge node and transmit them to the cloud.

[0028] Edge computing is a distributed computing paradigm that shifts data processing and analysis from a centralized cloud to edge devices or edge nodes closer to the data source. In this way, edge computing can significantly reduce data transmission latency, reduce bandwidth consumption, and improve the real-time performance and reliability of the system.

[0029] Key components of edge computing include edge devices, edge nodes, the cloud, and network infrastructure. Edge devices, such as sensors, cameras, and mobile devices, are located at the source of data generation and are responsible for collecting raw data and transmitting it to edge nodes. Edge nodes, acting as an intermediary layer between edge devices and the cloud, are responsible for data aggregation, preprocessing, and preliminary analysis. The cloud provides global data storage, deep analysis, and model training capabilities. Network infrastructure includes communication technologies such as 5G, Wi-Fi, and LoRa, used to connect edge devices, edge nodes, and the cloud for data transmission.

[0030] Specifically, supply chain data is collected through edge devices, and the supply chain data collected by multiple edge devices connected to the edge node is combined to form the supply chain data of the edge node; the supply chain data of the edge node is preprocessed, encrypted and grouped into multiple data packets of the edge node.

[0031] The supply chain data includes, but is not limited to, data from the logistics, warehousing, and production processes. The logistics data includes the location of transport vehicles (GPS) and the status of goods (temperature, humidity, vibration, etc.). The warehousing data includes inventory quantity, shelf location, and environmental conditions (such as temperature and humidity). The production data includes the status of production equipment and the consumption of raw materials.

[0032] The edge devices include, but are not limited to, various sensors, such as temperature sensors, humidity sensors, vibration sensors, RFID tags, etc.

[0033] The preprocessing includes data cleaning to remove invalid data; then, the preprocessed supply chain data is encrypted using a key generated by a double ratchet algorithm to ensure data security; the grouping and packaging involves packaging supply chain data within the same time period into a single data packet according to the timestamp of the supply chain data, facilitating subsequent transmission and processing.

[0034] The obtained data packet contains header information, payload, and verification information. The header information includes data packet identifier, sending time, destination address, etc. The payload refers to the actual supply chain data, and the verification information refers to the hash value or CRC code used to verify data integrity.

[0035] It should be noted that collecting supply chain data through edge devices and integrating data from multiple devices into a single dataset can reduce data redundancy and facilitate subsequent processing and analysis; using a key generated by a double ratchet algorithm to encrypt the data ensures data security during transmission; and grouping and packaging the encrypted data into multiple data packets for transmission can reduce the amount of data transmitted in a single transmission, reduce network load, and improve transmission efficiency.

[0036] The core idea of ​​the double ratchet algorithm is to use two independent ratchets to manage long-term keys and temporary session keys. These two independent ratchets are the symmetric key ratchet and the DH (Diffie-Hellman) ratchet. During communication, the double ratchet algorithm ensures that the keys of the two communicating parties are always synchronized and updated by ratcheting these two independent ratchets.

[0037] Before the symmetric key ratchet step, a DH ratchet step is performed to update the chain key; when a message is sent or received, a symmetric key ratchet step of the chain key is performed to derive a new key.

[0038] While the double ratchet algorithm offers high security, each ratchet step requires updating the chain key and generating a new key, which involves complex encryption operations. In edge computing scenarios, edge nodes have limited computing power, and frequent key generation consumes a large amount of computing resources, leading to performance degradation. Furthermore, although frequent ratchet steps improve security, this strategy is too conservative in some low-risk scenarios (such as those with low attack intensity), resulting in wasted resources. Conversely, in high-risk scenarios (such as those with high attack intensity), a fixed stepping cycle may not be sufficient to cope with rapidly changing security threats.

[0039] To address the issues of excessive computational resource consumption and the trade-off between security and efficiency in the double ratchet algorithm, a method is proposed to dynamically adjust the ratchet stepping cycle based on the attack intensity of network communication between edge nodes and the cloud, as well as the available computing power of the edge nodes.

[0040] S2. Monitor key indicators in network communication between edge nodes and the cloud in real time; determine the relative risk value of each attack type based on each key indicator; and sum the weighted relative risk values ​​of each attack type to obtain the attack level of network communication between edge nodes and the cloud.

[0041] It should be noted that in network communication, network latency anomalies, packet loss rate, and encrypted message duplication rate are important indicators for measuring communication quality. They can reflect the degree of risk of network communication being attacked by man-in-the-middle, packet loss, and retry attacks. Therefore, this embodiment assesses the security of network communication by monitoring the proportion of latency anomalies, packet loss rate, and encrypted message duplication rate, quantifies the risk level of each attack type, and comprehensively considers the impact of multiple attack types to obtain a more accurate assessment of the attack level.

[0042] 1. Monitor key indicators in real-time network communication between edge nodes and the cloud.

[0043] Specifically, the key indicators in the network communication include the latency anomaly ratio, packet loss rate, and encrypted message duplication rate, which are used to monitor three attack types: man-in-the-middle attack, packet loss attack, and retry attack, respectively.

[0044] The methods for obtaining the percentage of latency anomalies and the packet loss rate in network communication are as follows:

[0045] Specifically, a normal latency range is set based on historical data or empirical values; for example, in historical data, the average network latency is 50ms and the standard deviation of network latency is 10ms, then the normal latency range is defined as [40, 60], in ms (milliseconds).

[0046] Furthermore, each network communication transmits at least one data packet. By measuring with network monitoring tools, the number of all data packets sent, received, and lost in each network communication is monitored, as well as the network latency for receiving each data packet, in milliseconds (ms).

[0047] The network monitoring tool mentioned is a Ping statistics tool. By sending ICMP (Internet Control Message Protocol) requests and recording response times, the Ping statistics tool can help users assess the strength and stability of network connections.

[0048] Furthermore, if the network latency of a data packet exceeds the normal latency range, then the transmission of that data packet has a latency anomaly. The number of all data packets with latency anomalies is counted, and the ratio of the number of all data packets with latency anomalies to the number of all received data packets is taken as the latency anomaly ratio in network communication.

[0049] Furthermore, the packet loss rate in network communication is equal to the ratio of the number of lost packets to the number of packets sent.

[0050] The method for obtaining the repetition rate of encrypted messages in network communication is as follows:

[0051] Specifically, for the data in the data packet, the unique identifier of the data (including but not limited to timestamp) is used to check whether there is duplicate data in all received data; the ratio of the number of duplicate data to the number of all received data is used as the encryption message duplication rate in network communication.

[0052] 2. Set the maximum tolerance values ​​for the delay anomaly rate, data packet loss rate, and encrypted message duplication rate.

[0053] The maximum tolerance values ​​for latency anomaly rate, packet loss rate, and encrypted message duplication rate are standards used to measure network quality.

[0054] Regarding the delay anomaly ratio, since the real-time requirements of supply chain data are relatively low, a slightly higher delay anomaly ratio is acceptable. Therefore, the maximum tolerance value for the delay anomaly ratio is set to 10%. In other embodiments, the maximum tolerance value for the delay anomaly ratio can be set according to the actual application scenario and requirements, and the maximum tolerance value for the delay anomaly ratio is in the range of [1%, 15%].

[0055] Specifically, the maximum tolerance value for packet loss rate is set to 1%; in other embodiments, the maximum tolerance value for packet loss rate can be set according to the actual application scenario and requirements, and the maximum tolerance value for packet loss rate is in the range of (0, 2%).

[0056] Since duplicate messages can lead to resource waste and security risks, the maximum tolerance for encrypted message duplication rate should be as low as possible. Therefore, the maximum tolerance for encrypted message duplication rate is set to 0.1%. In other embodiments, the maximum tolerance for encrypted message duplication rate can be set according to the actual application scenario and requirements, and the maximum tolerance for encrypted message duplication rate can be in the range of (0, 0.5%).

[0057] 3. Determine the relative risk value of each attack type based on key indicators.

[0058] Specifically, key indicators in network communication include the latency anomaly ratio, packet loss rate, and encrypted message duplication rate, which are used to monitor man-in-the-middle attacks, packet loss attacks, and return attacks, respectively. Therefore, the latency anomaly ratio, packet loss rate, and encrypted message duplication rate are referred to as the first, second, and third key indicators, respectively, and the monitoring of man-in-the-middle attacks, packet loss attacks, and return attacks are referred to as the first, second, and third attack types, respectively.

[0059] Furthermore, based on each key indicator and its maximum tolerance value, the relative risk value of each attack type is calculated. The specific calculation formula is as follows:

[0060] ;

[0061] In the formula, Indicates the first The relative risk value of each attack type Indicates the first Key indicators Indicates the first The maximum tolerance value for these key indicators This indicates taking the maximum value.

[0062] It should be noted that when a key indicator does not exceed its maximum tolerance value, ,but =0, meaning that when a key indicator does not exceed its maximum tolerance value, the relative risk value of the attack type corresponding to that key indicator remains equal to 0; when a key indicator exceeds its maximum tolerance value... ,but At this point, the larger the key indicator, the greater the relative risk value of the attack type corresponding to that key indicator.

[0063] 4. The relative risk values ​​of each attack type are weighted and summed to obtain the attack level of network communication between edge nodes and the cloud.

[0064] Since different types of attacks pose varying threats to security, it is necessary to define weights for different attack types to represent their severity. The sum of the weights for all attack types equals 1. ,and , , All are greater than 0. , , These represent the weights of attack types 1, 2, and 3, respectively. Since the severity of attack types 1, 2, and 3—namely, man-in-the-middle attack, packet loss attack, and return attack—decreases sequentially, therefore... In this embodiment, the weights of the first, second, and third attack types are... , , The weights are set to 0.43, 0.35, and 0.22 respectively. In other embodiments, the weights of the first, second, and third attack types can be set according to the actual application scenario and requirements.

[0065] S3. Calculate the maximum theoretical computing power of the edge node based on its hardware configuration, calculate the real-time load availability of the edge node based on its current load, and obtain the available computing power of the edge node based on its maximum theoretical computing power and current load.

[0066] 1. Calculate the maximum theoretical computing power of the edge nodes based on their hardware configuration.

[0067] Specifically, each edge node reports its hardware configuration during registration; the hardware configuration includes CPU clock speed, number of cores, memory capacity, and GPU capabilities.

[0068] Furthermore, the cloud calculates the maximum theoretical computing power of the edge nodes based on their hardware configuration, and then establishes a profile of the maximum theoretical computing power of the edge nodes; the maximum theoretical computing power of the edge nodes represents the maximum theoretical computing power of the edge nodes when running at full load, and is related to hardware performance.

[0069] Among them, CPU clock speed, number of cores, memory capacity and GPU capability are respectively denoted as the first computing power influencing factor, the second computing power influencing factor, the third computing power influencing factor and the fourth computing power influencing factor of edge nodes.

[0070] Therefore, the formula for calculating the maximum theoretical computing power of an edge node is:

[0071] ;

[0072] In the formula, This represents the maximum theoretical computing power of the edge nodes. , , , These are the first, second, third, and fourth factors influencing the computing power of edge nodes. The mapping function for computing power score. , , , These are the first weight, the second weight, the third weight, and the fourth weight, respectively. , , , All are greater than 0, and .

[0073] In this embodiment, the first weight, the second weight, the third weight, and the fourth weight are... , , , The values ​​are set to 0.35, 0.3, 0.25, and 0.1 respectively. In other embodiments, the first weight, second weight, third weight, and fourth weight can be set according to the actual application scenario and requirements.

[0074] Mapping function for computing power score The first factor affecting computing power is CPU frequency, with 100 points for every 1GHz; the second factor is the number of cores, with 100 points for every core; the third factor is memory capacity, with 100 points for every 1GB; and the fourth factor is GPU capability, with 100 points for edge nodes with GPU capability and 0 points for edge nodes without GPU capability.

[0075] 2. Calculate the real-time load availability of the edge nodes based on their current load.

[0076] Specifically, each edge node periodically sends its current load to the cloud, which includes CPU utilization and memory utilization, both of which are percentages. The cloud calculates the real-time load availability of the edge node based on its current load. The real-time load availability represents the proportion of the edge node's current remaining resources to its maximum resources, and is used to reflect the load status.

[0077] Therefore, the formula for calculating the real-time load availability of edge nodes is:

[0078] ;

[0079] In the formula, Real-time load availability of edge nodes. , This refers to the CPU and memory usage of the edge nodes.

[0080] It should be noted that by calculating the average of CPU utilization and memory utilization, the real-time load availability can be dynamically reflected, providing a basis for calculating available computing power.

[0081] 3. The cloud calculates the available computing power of the edge nodes based on their maximum theoretical computing power and real-time load availability.

[0082] Specifically, calculate the maximum theoretical computing power of the edge nodes. Real-time load availability of edge nodes The product of these factors yields the available computing power of the edge nodes.

[0083] It should be noted that, taking into account both hardware performance and current load, the maximum theoretical computing power is multiplied by the real-time load availability to obtain the actual available computing resources.

[0084] S4. Based on the attack level of the network communication between the edge node and the cloud and the available computing power of the edge node, determine the ratchet stepping cycle of the dual ratchet algorithm; during the key generation process of the dual ratchet algorithm, ratchet stepping is performed according to the cycle to update the key.

[0085] It should be noted that in high-computing-power scenarios, frequent key updates are permissible; in high-attack-risk scenarios, the frequency of key updates needs to be increased to enhance data security; in low-attack-risk or low-computing-power scenarios, the frequency of key updates needs to be reduced to decrease computational overhead. Moreover, the higher the frequency of key updates, the shorter the maximum acceptable cycle of the corresponding edge node. Therefore, this embodiment determines the ratchet stepping cycle of the dual ratchet algorithm based on the attack level of network communication between the edge node and the cloud and the available computing power of the edge node, and dynamically adjusts the ratchet stepping cycle according to actual needs to achieve a balance between security and efficiency.

[0086] Specifically, based on the attack severity of network communication between the edge node and the cloud, and the available computing power of the edge node, the ratchet stepping cycle of the dual ratchet algorithm is determined, and the calculation formula is as follows:

[0087] ;

[0088] In the formula, This represents the period of ratchet stepping in the double ratchet algorithm. This indicates the severity of attacks on network communication between edge nodes and the cloud. This represents the Sigmoid function. This represents the longest acceptable period for an edge node. This represents the available computing power of the edge nodes. This represents the average available computing power of all edge nodes. Indicates the preset length. This represents the natural exponential function. This indicates rounding up to the nearest integer.

[0089] Here, "all edge nodes" refers to all edge nodes communicating with the same cloud. The average available computing power of all edge nodes represents the overall level of available computing power across all edge nodes communicating with the same cloud. This is used to measure the deviation of the current edge node's available computing power from the average computing power of all edge nodes, thereby determining the edge node's longest acceptable period and the edge node's available computing power. The greater the overall level of available computing power, the shorter the longest acceptable period for edge nodes, and the higher the frequency of key updates.

[0090] In addition, the frequency of key updates is directly proportional to the severity of the attack. That is, the higher the severity of the attack, the more frequently the key needs to be updated to ensure security. Therefore, the greater the severity of the attack on the network communication between the edge node and the cloud, the shorter the ratchet stepping cycle of the double ratchet algorithm.

[0091] It should be noted that the maximum acceptable period for an edge node is calculated by the deviation of its available computing power from the average computing power of all edge nodes and a preset length. This allows for the rational allocation of resources based on the differences in computing power among edge nodes, preventing low-computing-power nodes from updating their keys prematurely.

[0092] The specific value of the preset length can be set according to the actual application scenario and requirements, and the range of the preset length is [1, 10]. In this invention, the preset length is set to 6.

Claims

1. A method for encrypted storage of supply chain data based on edge computing, characterized in that, include: By collecting supply chain data through edge devices, the supply chain data collected by multiple edge devices connected to the edge node is combined to form the supply chain data of the edge node; Based on the key generated by the double ratchet algorithm, the preprocessed supply chain data of the edge nodes is encrypted, and the encrypted supply chain data of the edge nodes is grouped and packaged into multiple data packets of the edge nodes and transmitted to the cloud. In the dual-ratchet algorithm for key generation, the ratchet step is performed periodically to update the key. The method for obtaining the ratchet step period is as follows: Real-time monitoring of key indicators in network communication between edge nodes and the cloud; determination of the relative risk value of each attack type based on each key indicator; weighted summation of the relative risk values ​​of each attack type to obtain the attack severity of network communication between edge nodes and the cloud. Based on the hardware configuration of the edge node, calculate the maximum theoretical computing power of the edge node; based on the current load of the edge node, calculate the real-time load availability of the edge node; calculate the product of the maximum theoretical computing power of the edge node and the real-time load availability of the edge node to obtain the available computing power of the edge node. Based on the attack severity of network communication between edge nodes and the cloud, and the available computing power of the edge nodes, the ratchet stepping cycle of the dual-ratchet algorithm is determined, including: Based on the available computing power of the edge nodes, calculate the longest acceptable cycle time for each edge node, including: Calculate the difference between the available computing power of the edge node and the average available computing power of all edge nodes, calculate the ratio of this difference to the average available computing power of all edge nodes, take the negative of the ratio, and input it into the natural exponential function. Multiply the output of the natural exponential function by a preset length to obtain the longest acceptable period of the edge node. Take the negative value of the attack level of the network communication between the edge node and the cloud, and input it into the Sigmoid function. Multiply the output of the Sigmoid function by 2, then multiply it by the longest acceptable cycle of the edge node. Round up the product and use the rounded result as the cycle of the ratchet algorithm.

2. The supply chain data encryption and storage method based on edge computing according to claim 1, characterized in that, The key indicators in the network communication include the latency anomaly ratio, packet loss rate, and encrypted message duplication rate, which are used to monitor three attack types: man-in-the-middle attack, packet loss attack, and replay attack, respectively. The latency anomaly ratio, packet loss rate, and encrypted message duplication rate are denoted as the first, second, and third key indicators, respectively, and the monitoring of man-in-the-middle attack, packet loss attack, and replay attack is denoted as the first, second, and third attack types, respectively.

3. The supply chain data encryption and storage method based on edge computing according to claim 2, characterized in that, The determination of the relative risk value for each attack type based on key indicators includes: Set the maximum tolerance values ​​for the delay anomaly rate, packet loss rate, and encrypted message duplication rate; For the Key indicators Calculate the first Key indicators and the first The difference between the maximum tolerance values ​​of the three key indicators; if this difference is greater than 0, then the third... The relative risk value of the first attack type is equal to the difference between the first and second attack types. The ratio of the maximum tolerance value of the key indicators; if the difference is not greater than 0, then the... The relative risk value for this type of attack is 0.

4. The supply chain data encryption and storage method based on edge computing according to claim 2, characterized in that, The weights of all attack types are greater than 0, and the sum of the weights of all attack types is equal to 1. The weights of the first, second, and third attack types decrease sequentially.

5. The supply chain data encryption and storage method based on edge computing according to claim 1, characterized in that, The hardware configuration of the edge node is reported by the edge node during registration, including CPU frequency, number of cores, memory capacity and GPU capability, and is respectively recorded as the first computing power influencing factor, the second computing power influencing factor, the third computing power influencing factor and the fourth computing power influencing factor of the edge node.

6. The supply chain data encryption and storage method based on edge computing according to claim 5, characterized in that, The calculation of the maximum theoretical computing power of the edge node based on its hardware configuration includes: Based on the first, second, third, and fourth weights, the computing power scores of the first, second, third, and fourth computing power influencing factors of the edge node are weighted and summed to obtain the maximum theoretical computing power of the edge node. Among them, the first weight, the second weight, the third weight, and the fourth weight are all greater than 0, and the sum of all weights is equal to 1.

7. The supply chain data encryption and storage method based on edge computing according to claim 5, characterized in that, The mapping function for the computing power score is: For the first factor affecting computing power, CPU clock speed, 100 points are awarded for every 1 GHz. For the second factor, number of cores, 100 points are awarded for every core. For the third factor, memory capacity, 100 points are awarded for every 1 GB. For the fourth factor, GPU capability, 100 points are awarded when the edge node's hardware configuration has GPU capability, and 0 points are awarded when the edge node's hardware configuration does not have GPU capability.

8. The method for encrypted storage of supply chain data based on edge computing according to claim 1, characterized in that, The step of calculating the real-time load availability of edge nodes based on their current load includes: Each edge node periodically sends its current load to the cloud, which includes CPU utilization and memory utilization, both of which are percentages. Calculate the average CPU utilization and memory utilization of the edge nodes, and take the difference between 1 and this average value as the real-time load availability of the edge nodes.

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