Wireless Internet of Things energy transmission security management method based on block chain

Abstract

The invention discloses a wireless Internet of Things energy transmission security management method based on a block chain, and the method comprises the following steps: S1, collecting, encrypting and storing energy state information in real time through deploying equipment with an energy monitoring function; s2, dynamically adjusting an energy transmission strategy by using a distributed elastic energy management algorithm, and determining an optimal transmission path; s3, evaluating a network state in a transmission process, and dynamically adjusting a transmission path and a priority; s4, verifying the path and the data by using a fuzzy adaptive double-layer consensus algorithm; s5, submitting the preliminary consensus result to a global consensus layer for verification and recording; s6, after transmission is completed, the energy state is updated and shared through a block chain; and S7, continuously optimizing the transmission path to cope with the network environment change. According to the invention, the problem of insufficient security management in energy transmission of the existing wireless Internet of Things is effectively solved, and the overall security, transparency and efficiency of the system are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of blockchain, and in particular to a wireless Internet of Things energy transmission security management method based on blockchain. Background Art

[0002] First, with the rapid development of the Internet of Things and blockchain technology, wireless energy transmission between IoT devices has gradually become an urgent problem to be solved. The existing energy transmission management system mainly relies on centralized control nodes. In this mode, the management of energy distribution and transmission paths completely depends on the coordination of the central control node. However, this centralized management model is incapable of coping with the scalability and complexity of the IoT system, which easily leads to single point failures, system bottlenecks and other problems, and cannot meet the needs of modern IoT systems for efficient energy transmission.

[0003] Secondly, in the existing technology, the security management of wireless energy transmission is an important but difficult challenge. Although encryption algorithms are usually used in existing systems to protect data during transmission, the effectiveness of traditional encryption methods is greatly challenged in the face of various attacks in distributed networks. In particular, with the diversification and increase in the number of IoT devices, how to ensure the data security of each node during energy transmission and the security of the transmission path has become an urgent problem to be solved. The existing centralized security management mechanism is difficult to respond to the dynamically changing network environment in real time, causing the system to appear passive and vulnerable when facing new attacks and complex threats.

[0004] Third, the existing wireless IoT energy transmission system has deficiencies in data transparency and integrity. The energy transmission process involves a large amount of data, such as energy status, transmission path, operation instructions between devices, etc. The transparency and immutability of these data are the key to ensuring the security and stability of the system. However, traditional systems mainly rely on centralized databases to store and manage these data. The centralized management model not only has poor data transparency and is difficult to conduct multi-party collaborative verification, but also has the risk of tampering. In addition, existing technologies are difficult to ensure the integrity and authenticity of data in scenarios involving multi-party collaboration.

[0005] In summary, the existing technologies have the following main problems in wireless IoT energy transmission management: single point failure risk and poor system scalability caused by centralized control; traditional encryption technology cannot effectively deal with various security threats in distributed networks; data management methods lack transparency and immutability, making it difficult to meet the IoT system's needs for efficient and secure energy transmission. These problems have seriously restricted the development and application of IoT systems. Therefore, how to provide a blockchain-based wireless IoT energy transmission security management method is an urgent problem that technicians in this field need to solve. Summary of the invention

[0006] One purpose of the present invention is to propose a wireless Internet of Things energy transmission security management method based on blockchain. The present invention makes full use of blockchain technology, distributed elastic energy management algorithm and fuzzy adaptive double-layer consensus algorithm, describes in detail the process of intelligent management of wireless energy transmission, and has the advantages of high security, strong data transparency and high system adaptability.

[0007] A wireless Internet of Things energy transmission security management method based on blockchain according to an embodiment of the present invention includes the following steps:

[0008] S1. Deploy devices with energy monitoring functions in the IoT network to collect and record the energy status of each device in real time, and encrypt and store and share this status information through the blockchain distributed ledger;

[0009] S2. Each IoT device uses a distributed elastic energy management algorithm to dynamically adjust the energy transmission strategy based on the real-time collected energy status and network load, and determines the energy allocation and transmission path through a distributed negotiation mechanism;

[0010] S3. During the transmission process, each IoT device continuously evaluates the network status and energy requirements of neighboring devices through a negotiation mechanism, and dynamically adjusts the energy transmission path and priority;

[0011] S4. Based on the initially determined energy transmission path, a fuzzy adaptive two-layer consensus algorithm is used to perform preliminary verification of the path and transmission data by the selected proxy node. The proxy node evaluates the network environment and device status based on fuzzy logic and forms a preliminary consensus result.

[0012] S5. The proxy node submits the preliminary consensus result to the global consensus layer. The global consensus layer further verifies and confirms the preliminary consensus result to form the final consensus result, which is recorded and stored in the blockchain distributed ledger.

[0013] S6. After the energy transmission is completed, each IoT device uses the distributed elastic energy management algorithm to update its own energy status according to the data recorded during the transmission process, and shares the updated information through the blockchain for subsequent energy scheduling and management;

[0014] S7. Based on the historical transmission data and real-time monitoring information stored in the blockchain, the distributed elastic energy management algorithm is continuously applied to optimize the energy transmission path and strategy.

[0015] Optionally, S2 includes the following steps:

[0016] S21. Each IoT device collects the current energy status E in real time through the integrated multi-dimensional energy sensing module. i(t) and the energy state E of the surrounding equipment j (t), and process these data through adaptive filtering algorithms to eliminate noise and abnormal fluctuations and generate smooth energy state signals

[0017] S22, device i according to the smoothed energy state Energy consumption rate and the relative importance μ of the location of device i in the network topology i , calculate the elastic coefficient of the device through a multivariable function ∈ i (t):

[0018]

[0019] Among them, η is the weighted index of the residual energy, which adjusts the influence of the energy state of the equipment on the elastic coefficient; μ i is the relative importance of device i in the network topology, reflecting the strategic location of the device; is the energy consumption rate of device i, which represents the energy change per unit time; ω ij is the weight coefficient between device i and neighboring device j, reflecting the correlation of the energy states between them; L max is the maximum value of the network load, used for the calculation of the normalized elasticity coefficient;

[0020] S23, device i based on the calculated elastic coefficient ∈ i (t) Determine the energy demand priority P i (t), the priority is obtained by a nonlinear mapping function f(∈ i (t)) yields:

[0021]

[0022] Among them, α, β, and γ are parameters for adjusting the shape of the nonlinear mapping curve, so that the priority P i (t) Be adaptable under different network conditions;

[0023] S24, device i exchanges priority information P with neighboring device j i (t) and P j (t), and generate multiple candidate energy transfer paths R through the negotiation mechanism i (t), device i uses a path optimization algorithm based on graph theory and combines the reliability R of the transmission path rel (R i (t)) and the energy loss coefficient λ(R i (t)), optimize path selection:

[0024]

[0025] Where: R rel (R i (t)) is the path R i (t) reliability, which reflects the stability and connection strength of each node in the path; λ(R i (t)) is the path R i (t) The energy loss coefficient represents the overall efficiency of energy transfer in the path;

[0026] S25, device i along the optimized energy transmission path R opt (t) Perform energy transfer and monitor the energy loss along the path in real time and transmission efficiency Dynamically adjust path selection and energy allocation strategies through blockchain smart contract mechanism;

[0027] S26. After the energy transmission is completed, device i updates the energy status and transmission path data, and records and updates the distributed ledger through the blockchain to support subsequent energy transmission and optimized scheduling.

[0028] Optionally, S3 includes the following steps:

[0029] S31. Each IoT device calculates the network topology position weight ω of the current device based on the historical data in the blockchain distributed ledger and the real-time collected network status information. i (t):

[0030]

[0031] Among them, λ ij (t) is the connection strength between device i and its neighboring device j; E j (t) represents the energy state of device j at time t; ∈ j (t) is the elastic coefficient of the neighboring device j; α and β are parameters to adjust the nonlinear effects;

[0032] S32, device i calculates the network topology position weight ω i (t) and elastic coefficient ∈ i (t), evaluates the adaptability of the current energy transmission path, and adjusts the energy transmission priority P through a nonlinear adjustment function. i (t) Make initial adjustments:

[0033]

[0034] Among them, P i ′(t) is the energy transfer priority after preliminary adjustment; θ and γ are parameters used to control nonlinear regulation;

[0035] S33, device i and neighboring device j are based on the adjusted priority P i ′(t) and P j ′(t) to exchange information and determine the candidate energy transmission path set {R i (t)}, during the negotiation process, each device calculates the comprehensive adaptability coefficient ζ(R i (t)):

[0036]

[0037] Among them, ζ(R i (t)) is the path R i (t) comprehensive adaptability coefficient; η k (t) is the transmission efficiency of device k; λ ik (t) is the connection strength between device i and node k in the path;

[0038] S34, based on the comprehensive adaptability coefficient ζ (R i (t)), device i selects the optimal path R opt (t), combined with the transmission energy loss coefficient λ(R i (t)) weighted optimization of the path;

[0039] S35, device i follows the optimal energy transmission path R opt (t) Perform energy transfer and monitor the energy loss along the path in real time and transmission efficiency If significant changes are detected during the transmission process, the path adjustment algorithm is triggered through the blockchain smart contract mechanism to dynamically optimize the transmission path and strategy;

[0040] S36, during the energy transmission process, device i adapts the transmission process to changes in the network status through real-time monitoring and path adjustment. At the same time, according to the real-time energy status and priority information of the device, the path selection strategy is dynamically updated and coordinated with neighboring devices;

[0041] S37. After the energy transmission is completed, device i updates the energy status, path selection and adjustment data, and records them through the blockchain distributed ledger to support subsequent energy transmission optimization and real-time evaluation of network status;

[0042] Optionally, S4 includes the following steps:

[0043] S41. Preliminary determination of energy transmission path R opt (t), the selected proxy node n i Based on the topological characteristics, energy requirements and transmission environment of the path, a fuzzy logic reasoning system is used to predict the path R. opt(t) Perform multi-dimensional analysis, with input variables including the elastic coefficient of the node 、Path adaptability coefficient ζ(R opt (t)) and real-time transmission efficiency

[0044] S42, proxy node n i Fuzzy the input variables to generate the corresponding fuzzy sets and elastic coefficients. The fuzzification of is defined by the following membership function:

[0045]

[0046] in, is the elastic modulus The degree of membership; is the mean value of the elastic coefficient; σ ∈ is the standard deviation of the elasticity coefficient, indicating the breadth of the distribution of the variable;

[0047] S43, proxy node n i Fuzzy logic reasoning rules are used to reason about fuzzy sets and aggregate all fuzzified inputs to obtain fuzzified output results. The fuzzy logic rule set includes:

[0048] Rule 1: When High and ζ(R opt (t)) is high, path R opt (t) Strong adaptability;

[0049] Rule 2: When Low and When low, path R opt (t) poor adaptability;

[0050] S44, proxy node n i Through the centralized defuzzification method, the fuzzy set is converted into a specific path suitability evaluation value v(R opt (t)):

[0051]

[0052] Among them, μ v (x) is the fuzzy output of the path suitability evaluation; X is the evaluation value range;

[0053] S45, proxy node n i The path suitability evaluation value v(R opt (t)) as input, and use the two-layer consensus algorithm to negotiate with other proxy nodes. During the negotiation process, the preliminary consensus result is calculated using the following weighted consensus formula

[0054]

[0055] Where N is the number of proxy nodes participating in the consensus; v(R j (t)) is the neighboring proxy node n j The path suitability evaluation value of is the neighboring proxy node n j The elastic modulus of

[0056] S46. Preliminary consensus results Submit to the global consensus layer, and the global consensus node verifies based on historical data and real-time network status, and calculates the final global consensus result GCR(t):

[0057]

[0058] Where M is the number of global consensus nodes; is the global consensus node n m The network topology position weight;

[0059] S47. The final global consensus result GCR(t) is recorded in the blockchain and returned to the IoT device as a decision basis. Each device executes the final energy transmission path R according to GCR(t). final (t) and related operations. During the transmission process, each device monitors the path status in real time. If significant changes are found, the smart contract mechanism is triggered to adjust the path and re-consensus.

[0060] Optionally, S5 includes the following steps:

[0061] S51. Forming a preliminary consensus result at the proxy node layer After that, the proxy node n i Submit the preliminary consensus results to the global consensus layer. Each node m in the global consensus layer i First, perform weighted aggregation on the received preliminary consensus results and calculate the weighted aggregation value

[0062]

[0063] Where N is the number of proxy nodes participating in the preliminary consensus; Is the proxy node n j The preliminary consensus results of Is the proxy node n j The network topology position weight; is the global consensus node m i The quantized value of the current network state is used to adjust the influence of weights;

[0064] S52, global consensus node mi Calculate the weighted aggregate value After that, the historical transmission data and the current network status quantization value A multi-dimensional nonlinear mapping analysis was performed to evaluate the credibility of the preliminary consensus results. The multi-dimensional non-linear mapping function is:

[0065]

[0066] in, is the global consensus node m i The credibility evaluation value of the preliminary consensus result; κ is the sensitivity coefficient for adjusting the nonlinear mapping; α and β are the weight coefficients of the impact of historical data and current network status on the preliminary consensus result;

[0067] S53, global consensus node m i The credibility evaluation value of all participating nodes Aggregate to form a global credibility vector Ψ G (t), if Ψ G Any element in (t) is less than the preset threshold ψ th , the global consensus layer will require the proxy node layer to reach consensus again; otherwise, proceed to the next step;

[0068] S54. After confirming that all preliminary consensus results have passed the credibility assessment, the global consensus layer calculates the global consensus result GCR(t). The global consensus result is calculated through a multi-layer weighted composite function, and the formula is as follows:

[0069]

[0070] Where M is the number of nodes in the global consensus layer; is the coefficient that adjusts the influence of network topology weight; is the global consensus node m i The weighted aggregate value of is the global consensus node m i The network topology position weight;

[0071] S55. The global consensus result GCR(t) is recorded and encrypted in the blockchain distributed ledger. The encryption process adopts a double encryption mechanism. First, the global consensus result is encrypted using symmetric encryption, and then the symmetric key is encrypted using public key encryption.

[0072] S56. The encrypted global consensus result GCR(t) is broadcast in the blockchain network, and each node executes the final energy transmission path R according to GCR(t). final(t) If a significant change in the network status is detected during the execution, the smart contract mechanism will be triggered to adjust the path and re-consensus;

[0073] S57. After the energy transmission is completed, the system automatically generates a summary report, including the path execution status, the performance of the consensus results and the matching degree of the network status, and archives it through the blockchain distributed ledger for subsequent optimization analysis.

[0074] The beneficial effects of the present invention are:

[0075] (1) The present invention proposes a blockchain-based wireless Internet of Things energy transmission security management method. By deploying devices with energy monitoring functions in the Internet of Things network, the energy status information of each device is collected and encrypted and stored in real time, and the energy transmission strategy is dynamically adjusted using a distributed elastic energy management algorithm. The risk of single point failure in traditional centralized management is eliminated, and the security and robustness of the system are significantly improved.

[0076] (2) The present invention introduces a fuzzy adaptive two-layer consensus algorithm, which performs multi-dimensional analysis and verification of the energy transmission path and transmission data through selected proxy nodes, uses fuzzy logic reasoning to evaluate the path adaptability, and combines the two-layer consensus mechanism to confirm the path and transmission data. In a complex and changeable network environment, the stability and reliability of the energy transmission process are ensured, and the transparency and data integrity of the system are improved.

[0077] (3) The present invention further verifies and confirms the preliminary consensus results through the global consensus layer, uses a multi-layer weighted composite function to calculate the global consensus results, and records and encrypts them through the blockchain distributed ledger. This not only effectively prevents data tampering and loss, but also provides a reliable data basis for the optimization of energy transmission paths, thereby enhancing the overall operating efficiency and security capabilities of the system.

[0078] (4) The present invention combines a dynamic path adjustment mechanism to monitor changes in network status during transmission in real time and trigger the smart contract mechanism to adjust the path and re-consensus when necessary, thereby ensuring that the energy transmission process remains efficient and safe in a dynamically changing network environment, significantly improving the adaptability and transmission efficiency of the wireless Internet of Things system. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0080] Figure 1 This is a flowchart of a blockchain-based wireless Internet of Things energy transmission security management method proposed by the present invention;

[0081] Figure 2 A flow chart of each IoT device in the present invention dynamically adjusting the energy transmission strategy using a distributed elastic energy management algorithm;

[0082] Figure 3 This is a flow chart of the fuzzy adaptive two-layer consensus algorithm in the present invention performing preliminary verification of energy transmission paths and data at the proxy node layer and forming a preliminary consensus result. DETAILED DESCRIPTION

[0083] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0084] refer to Figure 1-3 , a wireless Internet of Things energy transmission security management method based on blockchain, comprising the following steps:

[0085] S1. Deploy devices with energy monitoring functions in the IoT network to collect and record the energy status of each device in real time, and encrypt and store and share this status information through the blockchain distributed ledger;

[0086] S2. Each IoT device uses a distributed elastic energy management algorithm to dynamically adjust the energy transmission strategy based on the real-time collected energy status and network load, and determines the energy allocation and transmission path through a distributed negotiation mechanism;

[0087] S3. During the transmission process, each IoT device continuously evaluates the network status and energy requirements of neighboring devices through a negotiation mechanism, and dynamically adjusts the energy transmission path and priority;

[0088] S4. Based on the initially determined energy transmission path, a fuzzy adaptive two-layer consensus algorithm is used to perform preliminary verification of the path and transmission data by the selected proxy node. The proxy node evaluates the network environment and device status based on fuzzy logic and forms a preliminary consensus result.

[0089] S5. The proxy node submits the preliminary consensus result to the global consensus layer. The global consensus layer further verifies and confirms the preliminary consensus result to form the final consensus result, which is recorded and stored in the blockchain distributed ledger.

[0090] S6. After the energy transmission is completed, each IoT device uses the distributed elastic energy management algorithm to update its own energy status according to the data recorded during the transmission process, and shares the updated information through the blockchain for subsequent energy scheduling and management;

[0091] S7. Based on the historical transmission data and real-time monitoring information stored in the blockchain, the distributed elastic energy management algorithm is continuously applied to optimize the energy transmission path and strategy.

[0092] In this implementation, S2 includes the following steps:

[0093] S21. Each IoT device collects the current energy status E in real time through the integrated multi-dimensional energy sensing module. i (t) and the energy state E of the surrounding equipment j (t), and process these data through adaptive filtering algorithms to eliminate noise and abnormal fluctuations and generate smooth energy state signals

[0094] S22, device i according to the smoothed energy state Energy consumption rate and the relative importance μ of the location of device i in the network topology i , calculate the elastic coefficient of the device through a multivariable function ∈ i (t):

[0095]

[0096] Among them, η is the weighted index of the residual energy, which adjusts the influence of the energy state of the equipment on the elastic coefficient; μ i is the relative importance of device i in the network topology, reflecting the strategic location of the device; is the energy consumption rate of device i, which represents the energy change per unit time; ω ij is the weight coefficient between device i and neighboring device j, reflecting the correlation of the energy states between them; L max is the maximum value of the network load, used for the calculation of the normalized elasticity coefficient;

[0097] S23, device i based on the calculated elastic coefficient ∈ i (t) Determine the energy demand priority P i (t), the priority is obtained by a nonlinear mapping function f(∈ i (t)) yields:

[0098]

[0099] Among them, α, β, and γ are parameters for adjusting the shape of the nonlinear mapping curve, so that the priority P i (t) Be adaptable under different network conditions;

[0100] S24, device i exchanges priority information P with neighboring device j i (t) and P j(t), and generate multiple candidate energy transfer paths R through the negotiation mechanism i (t), device i uses a path optimization algorithm based on graph theory and combines the reliability R of the transmission path rel (R i (t)) and the energy loss coefficient λ(R i (t)), optimize path selection:

[0101]

[0102] Where: R rel (R i (t)) is the path R i (t) reliability, which reflects the stability and connection strength of each node in the path; λ(R i (t)) is the path R i (t) The energy loss coefficient represents the overall efficiency of energy transfer in the path;

[0103] S25, device i follows the optimized energy transmission path R opt (t) Perform energy transfer and monitor the energy loss along the path in real time and transmission efficiency Dynamically adjust path selection and energy allocation strategies through blockchain smart contract mechanism;

[0104] S26. After the energy transmission is completed, device i updates the energy status and transmission path data, and records and updates the distributed ledger through the blockchain to support subsequent energy transmission and optimized scheduling.

[0105] In this implementation, S3 includes the following steps:

[0106] S31. Each IoT device calculates the network topology position weight ω of the current device based on the historical data in the blockchain distributed ledger and the real-time collected network status information. i (t):

[0107]

[0108] Among them, λ ij (t) is the connection strength between device i and its neighboring device j; E j (t) represents the energy state of device j at time t; ∈ j (t) is the elastic coefficient of the neighboring device j; α and β are parameters to adjust the nonlinear effects;

[0109] S32, device i calculates the network topology position weight ω i (t) and elastic coefficient ∈ i(t), evaluates the adaptability of the current energy transmission path, and adjusts the energy transmission priority P through a nonlinear adjustment function. i (t) Make initial adjustments:

[0110]

[0111] Among them, P i ′(t) is the energy transfer priority after preliminary adjustment; θ and γ are parameters used to control nonlinear regulation;

[0112] S33, device i and neighboring device j are based on the adjusted priority P i ′(t) and P j ′(t) to exchange information and determine the candidate energy transmission path set {R i (t)}, during the negotiation process, each device calculates the comprehensive adaptability coefficient ζ(R i (t)):

[0113]

[0114] Among them, ζ(R i (t)) is the path R i (t) comprehensive adaptability coefficient; η k (t) is the transmission efficiency of device k; λ ik (t) is the connection strength between device i and node k in the path;

[0115] S34, based on the comprehensive adaptability coefficient ζ (R i (t)), device i selects the optimal path R opt (t), combined with the transmission energy loss coefficient λ(R i (t)) weighted optimization of the path;

[0116] S35, device i follows the optimal energy transmission path R opt (t) Perform energy transfer and monitor the energy loss along the path in real time and transmission efficiency If significant changes are detected during the transmission process, the path adjustment algorithm is triggered through the blockchain smart contract mechanism to dynamically optimize the transmission path and strategy;

[0117] S36, during the energy transmission process, device i adapts the transmission process to changes in the network status through real-time monitoring and path adjustment. At the same time, according to the real-time energy status and priority information of the device, the path selection strategy is dynamically updated and coordinated with neighboring devices;

[0118] S37. After the energy transmission is completed, device i updates the energy status, path selection and adjustment data, and records them through the blockchain distributed ledger to support subsequent energy transmission optimization and real-time evaluation of network status;

[0119] In this implementation, S4 includes the following steps:

[0120] S41. Preliminary determination of energy transmission path R opt (t), the selected proxy node n i Based on the topological characteristics, energy requirements and transmission environment of the path, a fuzzy logic reasoning system is used to predict the path R. opt (t) Perform multi-dimensional analysis, with input variables including the elastic coefficient of the node Path adaptability coefficient ζ(R opt (t)) and real-time transmission efficiency

[0121] S42, proxy node n i Fuzzy the input variables to generate the corresponding fuzzy sets and elastic coefficients. The fuzzification of is defined by the following membership function:

[0122]

[0123] in, is the elastic modulus The degree of membership; is the mean value of the elastic coefficient; σ ∈ is the standard deviation of the elasticity coefficient, indicating the breadth of the distribution of the variable;

[0124] S43, proxy node n i Fuzzy logic reasoning rules are used to reason about fuzzy sets and aggregate all fuzzified inputs to obtain fuzzified output results. The fuzzy logic rule set includes:

[0125] Rule 1: When High and ζ(R opt (t)) is high, path R opt (t) Strong adaptability;

[0126] Rule 2: When Low and When low, path R opt (t) poor adaptability;

[0127] S44, proxy node n i Through the centralized defuzzification method, the fuzzy set is converted into a specific path suitability evaluation value v(R opt (t)):

[0128]

[0129] Among them, μ v (x) is the fuzzy output of the path suitability evaluation; X is the evaluation value range;

[0130] S45, proxy node n i The path suitability evaluation value v(R opt (t)) as input, and use the two-layer consensus algorithm to negotiate with other proxy nodes. During the negotiation process, the preliminary consensus result is calculated using the following weighted consensus formula

[0131]

[0132] Where N is the number of proxy nodes participating in the consensus; v(R j (t)) is the neighboring proxy node n j The path suitability evaluation value of is the neighboring proxy node n j The elastic modulus of

[0133] S46. Preliminary consensus results Submit to the global consensus layer, and the global consensus node verifies based on historical data and real-time network status, and calculates the final global consensus result GCR(t):

[0134]

[0135] Where M is the number of global consensus nodes; is the global consensus node n m The network topology position weight;

[0136] S47. The final global consensus result GCR(t) is recorded in the blockchain and returned to the IoT device as a decision basis. Each device executes the final energy transmission path R according to GCR(t). final (t) and related operations. During the transmission process, each device monitors the path status in real time. If significant changes are found, the smart contract mechanism is triggered to adjust the path and re-consensus.

[0137] In this implementation, S5 includes the following steps:

[0138] S51. Forming a preliminary consensus result at the proxy node layer After that, the proxy node n i Submit the preliminary consensus results to the global consensus layer. Each node m in the global consensus layer i First, perform weighted aggregation on the received preliminary consensus results and calculate the weighted aggregation value

[0139]

[0140] Where N is the number of proxy nodes participating in the preliminary consensus; Is the proxy node n j The preliminary consensus results of Is the proxy node n j The network topology position weight; is the global consensus node m i The quantized value of the current network state is used to adjust the influence of weights;

[0141] S52, global consensus node m i Calculate the weighted aggregate value After that, the historical transmission data and the current network status quantization value A multi-dimensional nonlinear mapping analysis was performed to evaluate the credibility of the preliminary consensus results. The multi-dimensional non-linear mapping function is:

[0142]

[0143] in, is the global consensus node m i The credibility evaluation value of the preliminary consensus result; K is the sensitivity coefficient for adjusting the nonlinear mapping; α and β are the weight coefficients of the impact of historical data and current network status on the preliminary consensus result;

[0144] S53, global consensus node m i The credibility evaluation value of all participating nodes Aggregate to form a global credibility vector Ψ G (t), if Ψ G Any element in (t) is less than the preset threshold ψ th , the global consensus layer will require the proxy node layer to reach consensus again; otherwise, proceed to the next step;

[0145] S54. After confirming that all preliminary consensus results have passed the credibility assessment, the global consensus layer calculates the global consensus result GCR(t). The global consensus result is calculated through a multi-layer weighted composite function, and the formula is as follows:

[0146]

[0147] Where M is the number of nodes in the global consensus layer; is the coefficient that adjusts the influence of network topology weight; is the global consensus node m i The weighted aggregate value of is the global consensus node m i The network topology position weight;

[0148] S55. The global consensus result GCR(t) is recorded and encrypted in the blockchain distributed ledger. The encryption process adopts a double encryption mechanism. First, the global consensus result is encrypted using symmetric encryption, and then the symmetric key is encrypted using public key encryption.

[0149] S56. The encrypted global consensus result GCR(t) is broadcast in the blockchain network, and each node executes the final energy transmission path R according to GCR(t). final (t) If a significant change in the network status is detected during the execution, the smart contract mechanism will be triggered to adjust the path and re-consensus;

[0150] S57. After the energy transmission is completed, the system automatically generates a summary report, including the path execution status, the performance of the consensus results and the matching degree of the network status, and archives it through the blockchain distributed ledger for subsequent optimization analysis.

[0151] Embodiment 1:

[0152] In a large-scale intelligent manufacturing industrial park, there are a large number of wireless IoT devices. These devices are mainly used to monitor the energy status, environmental data and production progress of production equipment in real time to ensure the safety and efficiency of the entire production process. However, due to the large number of devices and their wide distribution, traditional energy transmission management methods are difficult to cope with complex network environments. Centralized energy management methods not only have the risk of single point failures, but also have difficulty in coping with real-time changes in energy demand between devices, and are prone to system bottlenecks under high load conditions. What's more serious is that there is a risk of data tampering and malicious attacks on the transmission path during energy transmission, which greatly threatens the production safety of the entire industrial park.

[0153] In order to overcome the above problems, the industrial park decided to introduce the blockchain-based wireless IoT energy transmission security management method of the present invention. First, devices with energy monitoring functions are deployed on each IoT node in the park. These devices can collect energy status information of themselves and surrounding devices in real time, and encrypt and store and share this information through the blockchain distributed ledger. During the production process, the system uses a distributed elastic energy management algorithm to dynamically adjust the energy transmission strategy to ensure efficient distribution of energy between different devices.

[0154] For example, when a production device A is running at high load, its energy demand increases dramatically. The system will evaluate the network status and the energy demand of other devices in real time through the negotiation mechanism, and dynamically adjust the energy transmission path and priority of device A. This process not only takes into account the current energy status of device A, but also combines the elasticity coefficients of other devices and the network topology position weights to ultimately determine the optimal energy transmission path. Through the fuzzy adaptive two-layer consensus algorithm, the system can perform multi-dimensional verification of paths and data to ensure the security of energy transmission and the integrity of data.

[0155] The global consensus layer further verifies the preliminary consensus results submitted by each proxy node, forms the final consensus result, and stores it in encrypted form through the blockchain. During the transmission process, if a significant change in the network status is detected, the system will automatically trigger the smart contract mechanism to adjust the transmission path to respond to possible security threats or load changes, ensuring that the energy transmission process of the entire industrial park is always efficient, safe, and reliable.

[0156] Concrete data proves our beneficial effects

[0157] To verify the effectiveness of this method, the industrial park conducted a three-month test and compared it with the traditional centralized energy management method. The core indicators of the test included energy transmission efficiency, safety incident rate, system response time and equipment failure rate.

[0158] During the test, the industrial park deployed 500 IoT nodes, covering all aspects of the entire production process. Data collection time is from 8:00 to 20:00 every day, and the location is each production workshop in the industrial park. The following is the data results of the comparative analysis:

[0159] Table 1 Data table

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[0162] These data demonstrate the significant advantages of the present invention in solving the problems of the prior art and further prove its effectiveness and feasibility in practical applications.

[0163] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A wireless Internet of Things energy transmission security management method based on blockchain, characterized in that: The steps include: S1. Deploy devices with energy monitoring functions in the IoT network to collect and record the energy status of each device in real time, and encrypt and store and share this status information through the blockchain distributed ledger; S2. Each IoT device uses a distributed elastic energy management algorithm to dynamically adjust the energy transmission strategy based on the real-time collected energy status and network load, and determines the energy allocation and transmission path through a distributed negotiation mechanism; S3. During the transmission process, each IoT device continuously evaluates the network status and energy requirements of neighboring devices through a negotiation mechanism, and dynamically adjusts the energy transmission path and priority; S4. Based on the initially determined energy transmission path, a fuzzy adaptive two-layer consensus algorithm is used to perform preliminary verification of the path and transmission data by the selected proxy node. The proxy node evaluates the network environment and device status based on fuzzy logic and forms a preliminary consensus result. S5. The proxy node submits the preliminary consensus result to the global consensus layer. The global consensus layer further verifies and confirms the preliminary consensus result to form the final consensus result, which is recorded and stored in the blockchain distributed ledger. S6. After the energy transmission is completed, each IoT device uses the distributed elastic energy management algorithm to update its own energy status according to the data recorded during the transmission process, and shares the updated information through the blockchain for subsequent energy scheduling and management; S7. Based on the historical transmission data and real-time monitoring information stored in the blockchain, the distributed elastic energy management algorithm is continuously applied to optimize the energy transmission path and strategy.

2. According to a blockchain-based wireless Internet of Things energy transmission security management method according to claim 1, it is characterized in that: The S2 comprises the following steps: S21. Each IoT device collects the current energy status E in real time through the integrated multi-dimensional energy sensing module. i (t) and the energy state E of the surrounding equipment j (t), and process these data through adaptive filtering algorithms to eliminate noise and abnormal fluctuations and generate smooth energy state signals S22, device i according to the smoothed energy state Energy consumption rate and the relative importance μ of the location of device i in the network topology i , calculate the elastic coefficient of the device through a multivariable function ∈ i (t): Among them, η is the weighted index of the residual energy, which adjusts the influence of the energy state of the equipment on the elastic coefficient; μ i is the relative importance of device i in the network topology, reflecting the strategic location of the device; is the energy consumption rate of device i, which represents the energy change per unit time; ω ij is the weight coefficient between device i and neighboring device j, reflecting the correlation of the energy states between them; L max is the maximum value of the network load, used for the calculation of the normalized elasticity coefficient; S23, device i based on the calculated elastic coefficient ∈ i (t) Determine the energy demand priority P i (t), the priority is obtained by a nonlinear mapping function f(∈ i (t)) yields: Among them, α, β, and γ are parameters for adjusting the shape of the nonlinear mapping curve, so that the priority P i (t) Be adaptable under different network conditions; S24, device i exchanges priority information P with neighboring device j i (t) and P j (t), and generate multiple candidate energy transfer paths R through the negotiation mechanism i (t), device i uses a path optimization algorithm based on graph theory and combines the reliability R of the transmission path rel (R i (t)) and the energy loss coefficient λ(R i (t)), optimize path selection: Where: R rel (R i (t)) is the path R i (t) reliability, which reflects the stability and connection strength of each node in the path; λ(R i (t)) is the path R i (t) The energy loss coefficient represents the overall efficiency of energy transfer in the path; S25, device i along the optimized energy transmission path R opt (t) Perform energy transfer and monitor the energy loss along the path in real time and transmission efficiency Dynamically adjust path selection and energy allocation strategies through blockchain smart contract mechanism; S26. After the energy transmission is completed, device i updates the energy status and transmission path data, and records and updates the distributed ledger through the blockchain to support subsequent energy transmission and optimized scheduling.

3. According to a blockchain-based wireless Internet of Things energy transmission security management method according to claim 1, it is characterized in that: The S3 comprises the following steps: S31. Each IoT device calculates the network topology position weight ω of the current device based on the historical data in the blockchain distributed ledger and the real-time collected network status information. i (t): Among them, λ ij (t) is the connection strength between device i and its neighboring device j; E j (t) represents the energy state of device j at time t; ∈ j (t) is the elastic coefficient of the neighboring device j; α and β are parameters to adjust the nonlinear effects; S32, device i calculates the network topology position weight ω i (t) and elastic coefficient ∈ i (t), evaluates the adaptability of the current energy transmission path, and adjusts the energy transmission priority P through a nonlinear adjustment function. i (t) Make initial adjustments: Among them, P i '(t) is the energy transfer priority after preliminary adjustment; θ and γ are parameters used to control nonlinear regulation; S33, device i and neighboring device j are based on the adjusted priority P i '(t) and P j '(t) to exchange information and determine the candidate energy transmission path set {R i (t)}, during the negotiation process, each device calculates the comprehensive adaptability coefficient ζ(R i (t)): Among them, ζ(R i (t)) is the path R i (t) comprehensive adaptability coefficient; η k (t) is the transmission efficiency of device k; λ ik (t) is the connection strength between device i and node k in the path; S34, based on the comprehensive adaptability coefficient ζ (R i (t)), device i selects the optimal path R opt (t), combined with the transmission energy loss coefficient λ(R i (t)) weighted optimization of the path; S35, device i follows the optimal energy transmission path R opt (t) Perform energy transfer and monitor the energy loss along the path in real time and transmission efficiency If significant changes are detected during the transmission process, the path adjustment algorithm is triggered through the blockchain smart contract mechanism to dynamically optimize the transmission path and strategy; S36, during the energy transmission process, device i adapts the transmission process to changes in the network status through real-time monitoring and path adjustment. At the same time, according to the real-time energy status and priority information of the device, the path selection strategy is dynamically updated and coordinated with neighboring devices; S37. After the energy transmission is completed, device i updates the energy status, path selection and adjustment data, and records them through the blockchain distributed ledger to support subsequent energy transmission optimization and real-time evaluation of network status.

4. According to a blockchain-based wireless Internet of Things energy transmission security management method according to claim 1, it is characterized in that: The S4 comprises the following steps: S41. Preliminary determination of energy transmission path R opt (t), the selected proxy node n i Based on the topological characteristics, energy requirements and transmission environment of the path, a fuzzy logic reasoning system is used to predict the path R. opt (t) Perform multi-dimensional analysis, with input variables including the elastic coefficient of the node Path adaptability coefficient ζ(R opt (t)) and real-time transmission efficiency S42, proxy node n i Fuzzy the input variables to generate the corresponding fuzzy sets and elastic coefficients. The fuzzification of is defined by the following membership function: in, is the elastic modulus The degree of membership; is the mean value of the elastic coefficient; σ ∈ is the standard deviation of the elasticity coefficient, indicating the breadth of the distribution of the variable; S43, proxy node n i Fuzzy logic reasoning rules are used to reason about fuzzy sets and aggregate all fuzzified inputs to obtain fuzzified output results. The fuzzy logic rule set includes: Rule 1: When High and ζ(R opt (t)) is high, path R opt (t) Strong adaptability; Rule 2: When Low and When low, path R opt (t) poor adaptability; S44, proxy node n i The fuzzy set is converted into a specific path suitability evaluation value ν(R opt (t)): Among them, μ ν (x) is the fuzzy output of the path suitability evaluation; X is the evaluation value range; S45, proxy node n i The path adaptability evaluation value ν(R opt (t)) as input, and use the two-layer consensus algorithm to negotiate with other proxy nodes. During the negotiation process, the preliminary consensus result is calculated using the following weighted consensus formula Where N is the number of proxy nodes participating in the consensus; ν(R j (t)) is the neighboring proxy node n j The path suitability evaluation value of is the neighboring proxy node n j The elastic modulus of S46. Preliminary consensus results Submit to the global consensus layer, and the global consensus node verifies based on historical data and real-time network status, and calculates the final global consensus result GCR(t): Where M is the number of global consensus nodes; is the global consensus node n m The network topology position weight; S47. The final global consensus result GCR(t) is recorded in the blockchain and returned to the IoT device as a decision basis. Each device executes the final energy transmission path R according to GCR(t). final (t) and related operations. During the transmission process, each device monitors the path status in real time. If significant changes are found, the smart contract mechanism is triggered to adjust the path and re-consensus.

5. According to a blockchain-based wireless Internet of Things energy transmission security management method according to claim 1, it is characterized in that: The S5 comprises the following steps: S51. Forming a preliminary consensus result at the proxy node layer After that, the proxy node n i Submit the preliminary consensus results to the global consensus layer. Each node m in the global consensus layer i First, perform weighted aggregation on the received preliminary consensus results and calculate the weighted aggregation value Where N is the number of proxy nodes participating in the preliminary consensus; is the proxy node n j The preliminary consensus results of is the proxy node n j The network topology position weight; is the global consensus node m i The quantized value of the current network state is used to adjust the influence of weights; S52, global consensus node m i Calculate the weighted aggregate value After that, the historical transmission data and the current network status quantization value A multi-dimensional nonlinear mapping analysis was performed to evaluate the credibility of the preliminary consensus results. The multi-dimensional non-linear mapping function is: in, is the global consensus node m i The credibility evaluation value of the preliminary consensus result; κ is the sensitivity coefficient for adjusting the nonlinear mapping; α and β are the weight coefficients of the impact of historical data and current network status on the preliminary consensus result; S53, global consensus node m i The credibility evaluation value of all participating nodes Aggregate to form a global credibility vector Ψ G (t), if Ψ G Any element in (t) is less than the preset threshold ψ th , the global consensus layer will require the proxy node layer to reach consensus again; otherwise, proceed to the next step; S54. After confirming that all preliminary consensus results have passed the credibility assessment, the global consensus layer calculates the global consensus result GCR(t). The global consensus result is calculated through a multi-layer weighted composite function, and the formula is as follows: Where M is the number of nodes in the global consensus layer; is the coefficient that adjusts the influence of network topology weight; is the global consensus node m i The weighted aggregate value of is the global consensus node m i The network topology position weight; S55. The global consensus result GCR(t) is recorded and encrypted in the blockchain distributed ledger. The encryption process adopts a double encryption mechanism. First, the global consensus result is encrypted using symmetric encryption, and then the symmetric key is encrypted using public key encryption. S56. The encrypted global consensus result GCR(t) is broadcast in the blockchain network, and each node executes the final energy transmission path R according to GCR(t). final (t) If a significant change in the network status is detected during the execution, the smart contract mechanism will be triggered to adjust the path and re-consensus; S57. After the energy transmission is completed, the system automatically generates a summary report, including the path execution status, the performance of the consensus results and the matching degree of the network status, and archives it through the blockchain distributed ledger for subsequent optimization analysis.

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