A data transmission method based on multi-node joint encryption of an internet of things

Abstract

The application relates to the technical field of data transmission and discloses a multi-node joint encryption data transmission method based on an Internet of Things, which comprises the following steps: obtaining real-time environment parameters of candidate nodes in a long link of the Internet of Things through a preset evaluation model, and quantifying the real-time environment parameters into trusted participation weights, link available weights and business timeliness priority weights; assigning function roles to the candidate nodes based on the trusted participation weights, and dividing the candidate nodes into core fragment bearing nodes, auxiliary verification nodes, threshold nodes or pure forwarding transparent nodes. The application adopts a multi-dimensional weight driven function role asymmetric assignment and threshold fragment joint encryption scheme, achieves the technical effect that encryption responsibilities are jointly borne by long link multi-nodes, realizes the transition of a security boundary from single-point protection to full-link cooperative defense, and solves the problem that the security sharply decreases after a node is abandoned due to the fact that a forwarding node does not participate in joint processing in the prior art.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, specifically to a data transmission method based on multi-node joint encryption in the Internet of Things (IoT). Background Technology

[0002] In IoT applications such as cold chain pharmaceutical transportation, power grid inspection, and smart logistics, data transmission often spans warehouse nodes, vehicle nodes, transit nodes, and monitoring edge nodes, forming a complex network topology with long links and multiple hops. Given the extremely high real-time requirements and privacy sensitivity of pharmaceutical temperature control data and power grid status data, how to reduce the overall latency of long-link node processing while ensuring high data transmission security has become a novel and urgent technical problem in the field of IoT security.

[0003] Currently, existing solutions typically employ centralized, single-point encrypted transmission mechanisms or traditional end-to-end encryption techniques. These solutions involve the source node encrypting the original data using a pre-set symmetric or asymmetric key, while intermediate routing nodes, such as vehicle gateways or edge nodes, are responsible for transparent data forwarding or performing digital signature verification at the link layer. Some solutions attempt to introduce lightweight encryption algorithms to balance performance, striving to achieve fast data packet encapsulation and parsing on resource-constrained IoT terminals.

[0004] In existing technologies, this encryption mode is essentially still a passive defense architecture dominated by a single point, with a severe imbalance in the allocation of encryption responsibilities and a lack of effective joint defense cooperation mechanisms. Because intermediate forwarding nodes do not deeply participate in the global joint encryption logic, overall security depends on the protection strength of a single source node or high-privilege gateway node. Once a node in a long link experiences key leakage or trust fluctuations due to environmental changes or attacks, the security of the entire transmission link will rapidly collapse. Existing technologies lack the joint processing capability for normal nodes to collaboratively participate and dynamically fill in to jointly assume encryption responsibilities. To solve the above problems, this invention proposes a multi-node joint encryption data transmission method based on the Internet of Things (IoT). Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a data transmission method based on multi-node joint encryption in the Internet of Things (IoT) to solve the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-node joint encryption data transmission method based on the Internet of Things, wherein the multi-node joint encryption data transmission method includes:

[0007] The real-time environmental parameters of candidate nodes in the long IoT link are obtained by a preset evaluation model and quantified into trusted participation weight, link availability weight and business timeliness priority weight.

[0008] Based on the trusted participation weight, the candidate nodes are assigned functional roles and divided into core shard bearer nodes, auxiliary verification nodes, threshold nodes, or pure forwarding and transparent transmission nodes.

[0009] By combining the priority weight of business timeliness and the result of functional role assignment, the total number of participating nodes and the recovery threshold are determined, and the fragmented life cycle parameters are calculated.

[0010] The data to be transmitted is encrypted into an initial ciphertext, and the recovery key is split into several recovery fragments that are uniquely bound to the core fragment bearer node through a threshold secret sharing algorithm;

[0011] Based on the physical distribution of recovery fragments in long links, the main transmission path and backup redundant path are reconstructed in real time, so that the main transmission path covers the number of recovery fragments that reach the recovery threshold.

[0012] The initial ciphertext is controlled to flow along the main transmission path, and each functional role node performs fragmentation overlay or consistency verification to accumulate recovery capabilities;

[0013] After receiving a number of recovery fragments that meet the recovery threshold, the target receiving end performs a validity check by retrieving the transmission trajectory record. If the check passes, the original data is restored.

[0014] The parameters of the evaluation model are corrected in reverse based on the recovery results, so as to achieve adaptive evolution of system parameters.

[0015] Preferably, the formula for calculating the credible participation weight by the preset evaluation model is:

[0016] ,

[0017] in, For nodes Trusted participation weights, This is the device integrity verification value. For historical online stability, As an abnormal risk factor, This represents the current cache utilization rate. , , , Preset weighting coefficients;

[0018] The quantification of business timeliness priority weight includes: collecting the urgency level label of the business to which the data to be transmitted belongs, mapping the urgency level label to discrete time sensitivity values, and using them as input incentives for the preset evaluation model to adjust the recovery threshold value.

[0019] Preferably, the step of assigning functional roles to the candidate nodes based on the trusted participation weight includes: determining whether the trusted participation weight is greater than a first preset threshold, and when the determination result is yes, granting the node the execution permission to hold and superimpose the recovery shard;

[0020] The functional role assignment includes establishing a dynamic role mutual exclusion mechanism. This mutual exclusion mechanism mandates that in the same transmission task, the same candidate node cannot be simultaneously assigned as a core sharding bearer node and a threshold node, in order to eliminate the risk of collusion by a single node.

[0021] Preferably, determining the total number of participating nodes and the recovery threshold includes: establishing a negative correlation between the service timeliness priority weight and the recovery threshold; when the service timeliness priority weight increases, the recovery threshold is lowered to reduce recovery latency.

[0022] The fragmentation lifecycle parameter is positively correlated with the service timeliness priority weight. When the service timeliness priority weight is at a high level, the core fragmentation bearer node is authorized to automatically destroy the recovery fragment after the fragmentation lifecycle parameter is exceeded.

[0023] Preferably, the step of splitting the recovery key into several recovery fragments using the threshold secret sharing algorithm includes: discretizing the recovery key using a polynomial constructor to generate asymmetric encrypted segments carrying specific node identity identifiers.

[0024] Preferably, the real-time reconstruction of the main transmission path includes: calculating the priority score of each candidate path in the long link, wherein the priority score is determined by the product of the number of core shard bearer nodes contained in the path and the available weight of the link, and selecting the path with the highest score;

[0025] The real-time reconstruction of the main transmission path includes establishing node geofence constraint logic, determining whether the core shard carrying node is in the preset cold chain trajectory range based on the location information in the real-time environmental parameters, and triggering path elimination and recalculation in real time when the determination result is negative.

[0026] Preferably, the consistency verification performed by each of the functional role nodes includes: the auxiliary verification node performing integrity digest matching on the passed initial ciphertext, and reporting a blocking instruction to the regulatory cloud when an abnormality is detected.

[0027] Preferably, the ability to accumulate and recover the data to be transmitted includes: when the core fragment bearer node forwards the initial ciphertext, it appends the recovery fragments it holds to the end of the data packet of the initial ciphertext in a nested encapsulation manner;

[0028] The process of accumulating recovery capabilities includes: the threshold node monitors the number of recovery fragments carried by the current data packet in real time, and before the number of recovery fragments reaches the recovery threshold value, the threshold node refuses to execute the decapsulation instruction for the initial ciphertext.

[0029] Preferably, the legality determination by retrieving transmission trajectory records includes: comparing whether the source node identifier of each recovered fragment is completely consistent with the physical pass node identifier recorded in the main transmission path, and allowing decryption operation when they are consistent.

[0030] Preferably, the step of reversely correcting the parameters of the preset evaluation model based on the recovery result includes: automatically increasing the weight coefficient of the abnormal risk factor in the trusted participation weight calculation formula when the original data recovery fails or the transmission delay exceeds the expected threshold;

[0031] After the original data is restored, the following steps are taken: the target receiving end generates a recovery success receipt with a timestamp and broadcasts it in reverse to each of the core shard bearer nodes, triggering each of the core shard bearer nodes to synchronously release storage space and update node activity evaluation;

[0032] The adaptive evolution includes dynamically adjusting the path switching strategy. When the trusted participation weight of a node on a specific path is detected to decline in a stepwise manner in multiple consecutive tasks, the system forcibly locks the backup redundant path as the temporary primary path.

[0033] This invention provides a multi-node joint encryption data transmission method based on the Internet of Things (IoT). It has the following beneficial effects:

[0034] 1. This invention adopts a multi-dimensional weight-driven functional role asymmetric assignment and threshold fragmentation joint encryption scheme to achieve the technical effect that encryption responsibility is jointly borne by multiple nodes in a long link, realize the leap from single-point protection to full-link collaborative defense of the security boundary, and solve the shortcomings of the existing technology that the security drops sharply after the node fails because the forwarding node does not participate in the joint processing.

[0035] 2. This invention adopts a parameter adaptive game mechanism of business timeliness perception and real-time feedback of link status to achieve the technical effect of dynamic adjustment of the encryption scheme by the business urgency and network fluctuations, realize the Nash balance between security redundancy and transmission latency, and solve the shortcomings of the existing static encryption strategy that cannot take into account both the high timeliness of data and the strength of protection. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the process of the present invention;

[0037] Figure 2 This is a schematic diagram illustrating the effects of the present invention. Detailed Implementation

[0038] To enable those skilled in the art to understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0039] The present invention will now be described in detail with reference to the accompanying drawings:

[0040] Please see the appendix Figure 1 and attached Figure 2 This invention provides a multi-node joint encryption data transmission method based on the Internet of Things (IoT). The multi-node joint encryption data transmission method includes:

[0041] The real-time environmental parameters of candidate nodes in the long IoT link are obtained by a preset evaluation model and quantified into trusted participation weight, link availability weight and business timeliness priority weight.

[0042] Specifically, this step establishes a dynamic trust benchmark for the IoT system through real-time conversion of multi-dimensional perception parameters, enabling the system to break through the limitations of traditional static configuration. It creates a deep profile of nodes based on device integrity, historical stability, and business urgency, ensuring that all encrypted resource allocations are accurately aligned with the current link environment and business needs, significantly improving the scientific nature of decision-making.

[0043] Based on the trusted participation weight, the candidate nodes are assigned functional roles and divided into core shard bearer nodes, auxiliary verification nodes, threshold nodes, or pure forwarding and transparent transmission nodes.

[0044] Specifically, this step transforms the original single-function forwarding node into a collaborative unit with different security levels. By granting differentiated authority, the global sharing of security responsibilities is achieved, avoiding the drawback of concentrating decryption permissions on a single gateway in the traditional architecture. By utilizing the mutual constraints and collaboration mechanisms between roles, a dynamic security barrier is built, so that when a local node is damaged, it will not threaten the overall data security.

[0045] By combining the priority weight of business timeliness and the result of functional role assignment, the total number of participating nodes and the recovery threshold are determined, and the fragmented life cycle parameters are calculated.

[0046] Specifically, this step establishes a dynamic game mechanism between security strength and transmission efficiency. By flexibly adjusting the recovery threshold, the system can automatically shorten the logical link length of data recovery in the face of emergency business in exchange for ultimate real-time performance, while maintaining a very high degree of security redundancy in regular business. This enables the security policy to be elastically scaled up and down according to the business situation awareness, perfectly balancing stringent security requirements and extremely high timeliness response.

[0047] The data to be transmitted is encrypted into an initial ciphertext, and the recovery key is split into several recovery fragments that are uniquely bound to the core fragment bearer node through a threshold secret sharing algorithm;

[0048] Specifically, this step uses mathematical-level sharding to physically discretize and store decryption permissions. By uniquely binding key shards to specific trusted nodes, the security logic of permissions being granted by nodes is established. This makes it exponentially more difficult for any unauthorized node or interceptor to crack the code because they cannot gather enough valid shards. The underlying mathematical logic blocks the channels for brute-force attacks and illegal theft.

[0049] Based on the physical distribution of recovery fragments in long links, the main transmission path and backup redundant path are reconstructed in real time, so that the main transmission path covers the number of recovery fragments that reach the recovery threshold.

[0050] Specifically, this step achieves deep integration of network topology logic and encryption logic. Through dynamic planning algorithms, it ensures that the data flow path always accurately covers the preset number of secure shard holding nodes. In complex IoT fluctuation environments, it can automatically avoid untrusted or severely congested physical areas. By using seamless switching between primary and backup paths, it ensures the continuity of data transmission and significantly enhances the system's survivability in extreme environments.

[0051] The initial ciphertext is controlled to flow along the main transmission path, and each functional role node performs fragmentation overlay or consistency verification to accumulate recovery capabilities;

[0052] Specifically, this step transforms the static encryption state into a dynamic power accumulation process, enabling data packets to gradually acquire the necessary components for recovery during long-link flow. Through real-time intervention of fragmentation and consistency verification, it achieves immediate interception and blocking of data tampering behavior, ensuring that the target receiving end always receives legitimate data endorsed by all links, greatly reducing the ineffective occupation of terminal computing resources by malicious traffic.

[0053] After receiving a number of recovery fragments that meet the recovery threshold, the target receiving end performs a validity check by retrieving the transmission trajectory record. If the check passes, the original data is restored.

[0054] Specifically, this step adds a rigorous physical trajectory audit dimension to the data recovery process. By cross-verifying the logical fragment source with the physical path, it effectively identifies and intercepts attempts to launch replay attacks or man-in-the-middle attacks by forging node identities. It constructs a dual defense mechanism of logical identity + geographical location, ensuring that the recovery operation is executed under the premise of fully complying with the expected transmission strategy, thereby improving the overall anti-spoofing capability of the system.

[0055] The parameters of the evaluation model are corrected in reverse based on the recovery results to achieve adaptive evolution of system parameters;

[0056] Specifically, this step constructs a complete closed-loop learning and feedback system. By deeply reviewing the results of each recovery, it automatically discovers potential risk nodes or performance bottlenecks in the process and uses this to drive the dynamic correction and parameter iteration of the evaluation model. This allows the system to improve and enhance itself in continuous task practice, just like a living organism, and achieve a leapfrog evolution from passive defense to proactive perception and intelligent prediction.

[0057] The formula for calculating the credible participation weights in the pre-defined evaluation model is:

[0058] ,

[0059] in, For nodes Trusted participation weights, This is the device integrity verification value. For historical online stability, As an abnormal risk factor, This represents the current cache utilization rate. , , , Preset weighting coefficients;

[0060] The quantification of business timeliness priority weights includes: collecting the urgency level label of the business to which the data to be transmitted belongs, mapping the urgency level label to discrete time sensitivity values, and using it as input incentives for adjusting the recovery threshold value of the preset evaluation model.

[0061] Assigning functional roles to candidate nodes based on trusted participation weights includes: determining whether the trusted participation weight is greater than a first preset threshold; if the determination result is yes, granting the node the execution permission to hold and overlay the recovery shard.

[0062] Functional role assignment includes establishing a dynamic role mutual exclusion mechanism. The mutual exclusion mechanism mandates that in the same transmission task, the same candidate node cannot be assigned as both a core sharding bearer node and a threshold node at the same time, in order to eliminate the risk of collusion by a single node.

[0063] Determining the total number of participating nodes and the recovery threshold includes: establishing a negative correlation between business timeliness priority weight and the recovery threshold value; when the business timeliness priority weight increases, the recovery threshold value is lowered to reduce recovery latency.

[0064] The fragmentation lifecycle parameter is positively correlated with the business timeliness priority weight. When the business timeliness priority weight is at a high level, the core fragmentation bearer node is authorized to automatically destroy and restore the fragment after the fragmentation lifecycle parameter is exceeded.

[0065] The recovery key is split into several recovery fragments using a threshold secret sharing algorithm, which includes: discretizing the recovery key using a polynomial constructor to generate asymmetric encrypted segments carrying specific node identities.

[0066] Real-time reconstruction of the main transmission path includes: calculating the priority score of each candidate path in the long link. The priority score is determined by the product of the number of core fragment bearer nodes in the path and the available weight of the link, and the path with the highest score is selected.

[0067] Real-time reconstruction of the main transmission path includes establishing node geofence constraint logic, determining whether the core segment bearer node is in the preset cold chain trajectory range based on the location information in the real-time environmental parameters, and triggering path elimination and recalculation in real time when the determination result is negative.

[0068] Consistency verification is performed by nodes of various functional roles, including: auxiliary verification nodes perform integrity digest matching on the passed initial ciphertext, and report blocking instructions to the regulatory cloud when data anomalies are detected.

[0069] The ability to accumulate and recover data to be transmitted includes: when the core fragment bearer node forwards the initial ciphertext, it appends the recovery fragments it holds to the end of the initial ciphertext data packet in a nested encapsulation manner;

[0070] The process of accumulating recovery capabilities includes: the threshold node monitors the number of recovery fragments carried by the current data packet in real time, and the threshold node refuses to execute the decapsulation instruction for the initial ciphertext before the number of recovery fragments reaches the recovery threshold value.

[0071] The legitimacy determination by retrieving transmission trajectory records includes: comparing whether the source node identifier of each recovered fragment is completely consistent with the physical pass node identifier recorded in the main transmission path; if they are consistent, decryption is allowed.

[0072] The parameters of the preset evaluation model are corrected in reverse based on the recovery results, including: automatically increasing the weight coefficient of the abnormal risk factor in the trusted participation weight calculation formula when the original data recovery fails or the transmission delay exceeds the expected threshold.

[0073] After restoring the original data, the following steps are taken: the target receiving end generates a recovery success receipt with a timestamp and broadcasts it in reverse to each core shard bearer node, triggering each core shard bearer node to synchronously release storage space and update node activity evaluation;

[0074] Adaptive evolution includes dynamically adjusting the path switching strategy. When the trusted participation weight of a node on a specific path declines in a stepwise manner during multiple consecutive tasks, the system forcibly locks the backup redundant path as the temporary primary path.

[0075] The preset evaluation model is a core algorithm logic architecture built into the IoT management terminal or the starting source node. This model integrates pre-set weighted calculation rules to standardize and logically transform the collected multi-dimensional raw information. Its role is to provide a unified and quantifiable measurement standard for complex network environments, so as to transform the abstract node status into a decision basis that can directly guide role allocation and path planning. It is the cornerstone for realizing intelligent resource scheduling and security defense.

[0076] Trusted participation weight is a credit rating indicator calculated based on the node's own hardware integrity status, historical operational stability records, and the current external risk environment. This indicator plays the role of an access credential in the system, used to accurately measure the reliability of each candidate node when undertaking encryption tasks, thereby selecting high-level nodes that can hold core secret assets and ensuring the purity of members participating in joint encryption collaboration from the source.

[0077] Link availability weight is a quantitative value that reflects the transmission performance and resource load status of a specific channel in a long IoT link. This indicator is obtained by real-time monitoring of the current cache usage and bandwidth throughput of nodes. It is mainly used to evaluate the physical smoothness of each candidate path when reconstructing the transmission path, ensuring that the data flow can avoid congested areas or performance bottleneck nodes, and ensuring the stability of large-scale data packets flowing in multi-hop networks.

[0078] The business timeliness priority weight is a sensitivity value generated based on the urgency label of the business to which the data to be transmitted belongs. This weight directly reflects the tolerance limit of a specific task for transmission latency and plays a lever role in coordinating security redundancy and response speed in the system. By inputting this weight into the evaluation model, the system can dynamically trigger the reduction of the recovery threshold or the shortening of the life cycle.

[0079] The core sharding bearer nodes are special functional units with the highest level of trust selected by the functional role assignment program. These nodes are authorized to hold and manage key shards generated by the threshold secret sharing algorithm in their local storage space. During transmission, they are responsible for performing sharding and overlay operations on the ciphertext that flows through them. Through physically distributed storage, decryption permissions are distributed to different nodes in the link to eliminate the risk of global data leakage caused by the failure of a single node.

[0080] The auxiliary verification node is a supervisory functional unit specifically assigned to perform data integrity verification and process compliance review. This node does not directly hold recovery fragments, but can perform digest matching and anomaly detection on the passed initial ciphertext. Its role is to build a real-time monitoring network across the entire link. Once it detects that a data packet has been tampered with or that there is an unexpected illegal flow during transmission, it will immediately trigger a blocking command to alert the regulatory end, thereby improving the system's online anti-attack capability.

[0081] The threshold node is a key logical control unit with the function of determining the final decapsulation permission. This node is mainly responsible for monitoring whether the recovery capability accumulated by the data packet before it reaches the end has reached a preset threshold. When the accumulated fragment number is insufficient, the threshold node will forcibly refuse to execute any operation instructions on the initial ciphertext. This kind of mandatory logical checkpoint ensures that the data recovery process must meet the preset security cooperation conditions, which is the core manifestation of the threshold security mechanism at the physical execution layer.

[0082] Pure forwarding and transparent transmission nodes are basic connection units assigned to perform the function of physical forwarding of data packets. These nodes usually do not participate in any encryption operations or fragmentation processing due to low trust or limited computing resources. Their role is to fill the physical topology gaps in long links, ensure that data can be transmitted across hops in areas without encryption processing capabilities, and ensure the coverage integrity of long IoT links in complex geographical environments.

[0083] The fragmentation lifecycle parameter is a dynamic logical threshold with time constraints set for each independent recovery fragment. This parameter is generated by associating business timeliness weight with system timestamps, which forcibly stipulates the legal residence time of key fragments in node memory. Once the time range is exceeded, the node will automatically trigger a self-destruct command to clear the relevant data, effectively preventing attackers from trying to crack the original data by long-term listening and slowly accumulating historical fragments.

[0084] The initial ciphertext is a code stream that cannot be directly read, generated after the original data to be transmitted is processed by a preliminary encryption algorithm at the source. This ciphertext serves as the basic carrier for threshold secret sharing and collaboration. It continuously absorbs the recovery capabilities superimposed by each node during long-link flow. The design purpose is to ensure that the data is always in a protected state after leaving the source. That is, if it is illegally intercepted during transmission, it will appear as meaningless and messy information without the recovery fragments being collected.

[0085] Recovery fragments are logical segments generated by mathematically splitting the original recovery key using a threshold secret sharing algorithm. Each fragment contains partial information about the original key and is uniquely bound to a specific core fragment carrying node. Fragments themselves do not have independent decryption capabilities; the key can only be reconstructed through the aggregation operation of several fragments. This design achieves the physical division of permissions and is the smallest technical unit to achieve multi-node joint encryption.

[0086] The primary transmission path and the backup redundant path are node forwarding sequences with differentiated strategies generated based on node geofencing constraints and priority scoring algorithms. The primary path is responsible for performing regular, efficient transmission tasks and must ensure coverage of the number of nodes that meet the threshold requirements. The backup path, as a fault-tolerance mechanism, is in a real-time ready state. The existence of both ensures that when the primary path experiences node failure, geographical offset, or a step-by-step decline in security level, the system can instantly switch transmission channels to maintain service continuity.

[0087] Recovery power refers to the mathematical potential energy that gradually accumulates towards a recoverable state during the flow of the initial ciphertext along the main transmission path through the superposition operations of each core fragment carrying node. This power is attached to the end of the data packet in the form of nested encapsulation. The accumulation process of power is essentially the process of key fragments continuously converging. When the power accumulates to the level that meets the threshold requirement, the target receiving end has a legitimate basis to trigger the decryption operation.

[0088] Transmission trajectory records are physical flow logs automatically generated by each participating node during the long link hop process of data packets. These records are synchronously delivered to the target receiving end by the data packets and serve as the ultimate audit basis for determining the legality of transmission. By cross-comparing the actual sequence of physical nodes that the data packets pass through with the preset encrypted logical path, the system can accurately identify abnormal behaviors that bypass security checkpoints or are hijacked by man in the middle.

[0089] Example 1:

[0090] In this embodiment, the data transmission context is remote automated power inspection. Data packets need to travel from the high-voltage line monitoring terminal across several tower relay nodes and vehicle-mounted mobile edge nodes before finally reaching the power grid management center. This scenario primarily addresses the risk of single-point failure caused by the lack of encryption participation by forwarding nodes in existing technologies.

[0091] Global Status Awareness and Role Assignment: After the system starts, the preset evaluation model first collects real-time environmental parameters of all candidate tower nodes and vehicle nodes in the long link through the wireless sensing network, including the hardware firmware version verification value of each node, historical online duration, and whether there is a risk of illegal instruction injection. The model then calculates the trusted participation weight of each node according to the preset weighting formula.

[0092] Dynamic role mutual exclusion constraint: Based on the calculated weights, the system assigns the pole node with the highest credibility as the core segment bearing node and the vehicle gateway with high-performance computing capabilities as the threshold node. At the same time, a dynamic role mutual exclusion mechanism is activated, which forces the above-mentioned pole nodes not to perform the threshold function in this inspection task, so as to separate the segment holding right and the decryption execution right in both physical and logical dimensions.

[0093] Threshold key splitting and physical binding: The monitoring terminal generates encrypted data of the high-voltage equipment status, uses a threshold secret sharing algorithm to split the recovery key into independent recovery fragments, and uses asymmetric encryption technology to send each fragment to a selected core fragment bearer node, ensuring that no single node holds complete information that can recover data.

[0094] Forced Path Coverage and Capacity Accumulation: The system reconstructs the main transmission path, forcing the data stream to pass through the tower nodes sequentially. The initial ciphertext is fragmented and nested during the flow, allowing the data packets to gradually accumulate and recover capacity during physical displacement. Finally, the threshold node can issue a decapsulation command only after confirming that the capacity has reached the threshold requirement. Through this end-to-end collaborative defense mechanism, even if the tower nodes are physically damaged or the key is extracted, attackers will still be unable to crack the core power data because they cannot obtain the auxiliary fragments from other nodes.

[0095] Example 2:

[0096] In this embodiment, the data transmission context is the cold chain transportation of biological products with extremely high time requirements. The data packets contain real-time temperature compensation data and need to maintain transmission in a complex mobile network environment. This scenario focuses on solving the problem of the inability to dynamically match encryption strength with link status and service urgency.

[0097] Business timeliness perception input: When the vehicle temperature control sensor detects a step-like abnormal fluctuation in the temperature inside the vehicle, the system automatically identifies the extremely high urgency label of the business and maps it to a high-level business timeliness priority weight, which serves as the core input incentive for adjusting the encryption threshold value of the preset evaluation model.

[0098] A flexible balance between security and timeliness: When the preset evaluation model receives a high timeliness requirement, it automatically triggers parameter adjustment instructions to lower the originally high recovery threshold value in a step-by-step manner. At the same time, it uses the available weight of the link to detect long links in real time and identify the congestion section caused by tunnel obstruction or signal attenuation.

[0099] Dynamic path reconstruction and geofencing constraints: The system establishes geofencing constraint logic based on location information, and determines in real time whether the core segment carrying node currently performing transmission deviates from the preset cold chain logistics trajectory range. Once a node is found to have a geographical offset or the available weight of the link declines, the system immediately triggers the instantaneous switching of the backup redundant path to ensure that cold chain alarm data is always transmitted along the shortest and most stable channel.

[0100] Feedback-driven adaptive evolution: After successful data recovery, the target receiver broadcasts a timestamped success receipt to each bearer node. Based on the actual latency performance of this transmission, the system reverse-corrects the risk factor weighting coefficients in the evaluation model. If unexpected latency is frequently detected in a specific route segment, a highly available path is forcibly locked in the next task. Through this closed-loop feedback mechanism, this invention achieves maximum reduction in processing latency for cold chain sensitive data while ensuring necessary security levels, completely resolving the industry pain point of the disconnect between encryption logic and real-time status.

[0101] Example 3:

[0102] In this embodiment, the data transmission context is a distributed energy station and environmental monitoring system within a smart park. Data packets need to traverse several fixed base stations, mobile inspection robots, and park edge gateways over an operating cycle of several days or even longer. This scenario primarily addresses the shortcomings of existing technologies in terms of self-healing capabilities and dynamic strategy adjustment capabilities when facing gradual declines in node performance or long-term environmental fluctuations.

[0103] Long-term observation and quantification of multi-dimensional weights: The system maintains normal operation in the industrial park. The preset evaluation model continuously monitors the real-time environmental parameters of each node in the long IoT link, collects instantaneous device integrity verification values, and focuses on recording the fluctuation trend of the historical online stability and cache utilization of nodes over several weeks. Through a preset weighting formula, these long-term indicators are quantified into dynamically changing trusted participation weights and link availability weights.

[0104] Dynamic recalculation of path priority scores: Based on long-term observation data, the system calculates the priority scores of each candidate path in a long link in real time. When it is found that a specific node in a traditional main transmission path has a declining trend in the available weight of the link due to environmental interference or hardware aging, the priority score algorithm will automatically reduce the ranking of the path, and select the node combination with the best score from the backup redundant path library to achieve smooth early warning switching of the path.

[0105] The release of fragment lifecycle and activity level are linked: After each round of inspection data transmission tasks is completed, the target receiving end generates a recovery success receipt with a high-precision timestamp and broadcasts it in reverse to all core fragment carrying nodes that participated in the task. This triggers these nodes to release expired storage space synchronously and update the node activity evaluation indicators. If a node fails to complete the accumulation of rights within the fragmentation lifecycle due to network jitter, the fragments held by that node will be automatically destroyed to prevent potential long-term memory overflow risks.

[0106] Reverse self-healing correction of model parameters: In multiple consecutive transmission tasks, the system automatically compares the expected transmission delay with the actual recovery result. If it detects a step-like decline in the trusted participation weight of nodes in a specific area, leading to an increase in the retransmission rate, the system will automatically increase the weight coefficient of the abnormal risk factor in the evaluation model, forcibly remove the node in that area from the core function sequence, and lock the backup redundant path as the new temporary main path. Through this adaptive evolution mechanism, the system can achieve self-repair and strategy iteration in response to environmental degradation without human intervention, ensuring the long-term reliable transmission of industrial park monitoring data.

[0107] Example 1: By using a multi-node function assignment and threshold sharding mechanism in the power inspection scenario, the problem of node failure caused by the concentration of encryption responsibilities in the existing technology is solved. The encryption authority is logically diffused from a single-point gateway to the entire link collaborative unit. The advantage is that by building extremely high defense resilience through dynamic role mutual exclusion and physical sharding binding, that is, if a local node is compromised, the attacker will not be able to crack the core data because he cannot gather the necessary shards required by the mathematical threshold, thus completely eliminating the risk of security collapse caused by single point failure.

[0108] Example 2: By using a business timeliness perception and parameter elastic game mechanism in the pharmaceutical cold chain scenario, the problem of the disconnect between the static encryption strategy and the real-time environment of the Internet of Things in the existing technology is solved. The security strength and transmission latency are adaptively adjusted according to the business urgency label. The advantage is that the system can dynamically lower the recovery threshold and reconstruct the optimal path according to high timeliness requirements such as temperature compensation. While ensuring necessary security redundancy, the time loss of data processing and circulation is greatly reduced, perfectly balancing the contradiction between high data privacy and extreme timeliness.

[0109] Example 3: By using a closed-loop feedback and adaptive evolution mechanism under long-term monitoring of industrial parks, the problem of existing technologies lacking self-repair capabilities when facing node performance fluctuations or long-term environmental degradation is solved. The system strategy achieves intelligent iteration based on historical operation trajectory records and recovery results feedback. The advantage is that it automatically identifies and isolates nodes with declining credibility by using reverse correction logic, and ensures the long-term stability of the system through smooth switching of primary and backup paths and dynamic cleanup of the life cycle, enabling long-term IoT links to have self-healing and evolutionary characteristics similar to organisms.

[0110] Embodiments of the present invention have been presented and described. It will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data transmission method based on multi-node joint encryption of Internet of Things, characterized in that, The multi-node joint encryption data transmission method includes: The real-time environmental parameters of candidate nodes in the long IoT link are obtained by a preset evaluation model and quantified into trusted participation weight, link availability weight and business timeliness priority weight. Based on the trusted participation weight, the candidate nodes are assigned functional roles and divided into core shard bearer nodes, auxiliary verification nodes, threshold nodes, or pure forwarding and transparent transmission nodes. By combining the priority weight of business timeliness and the result of functional role assignment, the total number of participating nodes and the recovery threshold are determined, and the fragmented life cycle parameters are calculated. The data to be transmitted is encrypted into an initial ciphertext, and the recovery key is split into several recovery fragments that are uniquely bound to the core fragment bearer node through a threshold secret sharing algorithm; Based on the physical distribution of recovery fragments in long links, the main transmission path and backup redundant path are reconstructed in real time, so that the main transmission path covers the number of recovery fragments that reach the recovery threshold. The initial ciphertext is controlled to flow along the main transmission path, and each functional role node performs fragmentation overlay or consistency verification to accumulate recovery capabilities; After receiving a number of recovery fragments that meet the recovery threshold, the target receiving end performs a validity check by retrieving the transmission trajectory record. If the check passes, the original data is restored. The parameters of the evaluation model are corrected in reverse based on the recovery results, so as to achieve adaptive evolution of system parameters.

2. The multi-node joint encryption data transmission method of claim 1, wherein, The formula for calculating the credible participation weight by the preset evaluation model is as follows: ， wherein, is a trust participation weight of a node , is a device integrity check value, is a historical online stability, is an abnormal risk factor, is a current cache occupancy rate, , , , is a preset weighting coefficient; The quantification of business timeliness priority weight includes: collecting the urgency level label of the business to which the data to be transmitted belongs, mapping the urgency level label to discrete time sensitivity values, and using them as input incentives for the preset evaluation model to adjust the recovery threshold value.

3. The multi-node joint encryption data transmission method according to claim 1, characterized in that, The step of assigning functional roles to the candidate nodes based on the trusted participation weight includes: determining whether the trusted participation weight is greater than a first preset threshold, and when the determination result is yes, granting the node the execution permission to hold and superimpose the recovery shard; The functional role assignment includes establishing a dynamic role mutual exclusion mechanism. This mutual exclusion mechanism mandates that in the same transmission task, the same candidate node cannot be simultaneously assigned as a core sharding bearer node and a threshold node, in order to eliminate the risk of collusion by a single node.

4. The multi-node joint encryption data transmission method according to claim 1, characterized in that, The determination of the total number of participating nodes and the recovery threshold includes: establishing a negative correlation between the service timeliness priority weight and the recovery threshold value; when the service timeliness priority weight increases, the recovery threshold value is lowered to reduce recovery latency. The fragmentation lifecycle parameter is positively correlated with the service timeliness priority weight. When the service timeliness priority weight is at a high level, the core fragmentation bearer node is authorized to automatically destroy the recovery fragment after the fragmentation lifecycle parameter is exceeded.

5. The multi-node joint encryption data transmission method according to claim 1, characterized in that, The step of splitting the recovery key into several recovery fragments using the threshold secret sharing algorithm includes: discretizing the recovery key using a polynomial constructor to generate asymmetric encrypted segments carrying specific node identity identifiers.

6. The multi-node joint encryption data transmission method according to claim 1, characterized in that, The real-time reconstruction of the main transmission path includes: calculating the priority score of each candidate path in the long link, wherein the priority score is determined by the product of the number of core shard bearer nodes contained in the path and the available weight of the link, and selecting the path with the highest score; The real-time reconstruction of the main transmission path includes establishing node geofence constraint logic, determining whether the core shard carrying node is in the preset cold chain trajectory range based on the location information in the real-time environmental parameters, and triggering path elimination and recalculation in real time when the determination result is negative.

7. The multi-node joint encryption data transmission method according to claim 1, characterized in that, The consistency verification performed by each of the aforementioned functional role nodes includes: the auxiliary verification node performing integrity digest matching on the passed initial ciphertext, and reporting a blocking instruction to the regulatory cloud when an abnormality is detected.

8. The multi-node joint encryption data transmission method according to claim 1, characterized in that, The ability to accumulate and recover the data to be transmitted includes: when the core fragment bearer node forwards the initial ciphertext, it appends the recovery fragments it holds to the end of the data packet of the initial ciphertext in a nested encapsulation manner; The process of accumulating recovery capabilities includes: the threshold node monitors the number of recovery fragments carried by the current data packet in real time, and before the number of recovery fragments reaches the recovery threshold value, the threshold node refuses to execute the decapsulation instruction for the initial ciphertext.

9. The multi-node joint encryption data transmission method according to claim 1, characterized in that, The legality determination by retrieving transmission trajectory records includes: comparing whether the source node identifier of each recovered fragment is completely consistent with the physical pass node identifier recorded in the main transmission path, and allowing decryption operation when they are consistent.

10. The multi-node joint encryption data transmission method according to claim 1, characterized in that, The step of reversing the parameters of the preset evaluation model based on the recovery result includes: automatically increasing the weight coefficient of the abnormal risk factor in the trusted participation weight calculation formula when the original data recovery fails or the transmission delay exceeds the expected threshold. After the original data is restored, the following steps are taken: the target receiving end generates a recovery success receipt with a timestamp and broadcasts it in reverse to each of the core shard bearer nodes, triggering each of the core shard bearer nodes to synchronously release storage space and update node activity evaluation; The adaptive evolution includes dynamically adjusting the path switching strategy. When the trusted participation weight of a node on a specific path is detected to decline in a stepwise manner in multiple consecutive tasks, the system forcibly locks the backup redundant path as the temporary primary path.

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