Data structure adaptive scheduling method and system for passive internet of things access authentication

Abstract

The application belongs to the technical field of Internet of Things security, and specifically discloses a data structure adaptive scheduling method and system for passive Internet of Things access authentication, which comprises the following steps: obtaining authentication requests of multiple passive terminals, grouping the authentication requests to form several policy sub-trees; in each policy sub-tree, the authentication requests are sorted according to the priority order of the energy level of the energy state identifier, the emergency degree and the prediction stability, a sub-tree Merkle tree is constructed, and the aggregated energy label of each intermediate node is generated from bottom to top; the root hash of each sub-tree Merkle tree is combined in the order of the energy collection mode, the root hash of the entire forest is calculated, and the complete authentication Merkle forest structure is output. By adopting the technical scheme, the terminal physical state information is embedded in batch authentication, the formation time and composition of the authentication batch are adjusted according to the dynamic law of passive terminal energy collection, the authentication scheduling optimization is realized, and the overall authentication performance is improved.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) security technology, and relates to an adaptive scheduling method and system for data structures used in passive IoT access authentication. Background Technology

[0002] Existing IoT terminal authentication schemes, especially in distributed systems using threshold signatures, face the following problems:

[0003] The challenge lies in balancing batch authentication efficiency with individual traceability: Traditional Merkle tree batch authentication schemes aggregate multiple terminal requests into a single root tree to improve efficiency. However, in this approach, the tree structure merely serves as a carrier of data integrity and cannot reflect the correlation between the terminal's physical state and the authentication policy. When it is necessary to trace the authentication status of a specific terminal within a batch, existing schemes can only provide proof that the terminal's data is within the batch, but cannot provide information about the terminal's physical state during the authentication process.

[0004] Furthermore, existing batch authentication schemes employ fixed time windows or simple traffic adaptation strategies, ignoring the physical laws governing terminal energy harvesting. This forces terminals with sufficient energy to wait for the fixed window to expire, causing authentication delays; it may also lead to terminals with insufficient energy being hastily included in the batch for authentication, unable to complete subsequent verification processes, resulting in partial failure of the entire batch. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned problems in existing technologies by proposing an adaptive scheduling method and system for data structures used in passive Internet of Things (IoT) access authentication.

[0006] To achieve the above objectives, the basic solution of this invention is: an adaptive scheduling method for data structures for passive IoT access authentication, comprising the following steps:

[0007] Obtain authentication requests from multiple passive terminals. Each authentication request includes a unique identifier of the terminal, a certificate hash, a request timestamp, an energy status identifier, and a location context. Based on the energy harvesting mode field in the energy status identifier, group the authentication requests to form several policy subtrees.

[0008] Within each policy subtree, authentication requests are sorted according to the priority order of energy level, urgency, and predicted stability of the energy state identifier;

[0009] A Merkle tree subtree is constructed based on the sorted authentication requests, and the aggregate energy label (AET) of each intermediate node is generated from bottom to top during the construction process.

[0010] The root hashes of each subtree Merkle tree are combined in order of energy collection mode to calculate the root hash of the entire forest, and the complete certified Merkle forest structure is output.

[0011] The working principle and beneficial effects of this basic solution are as follows: This technical solution achieves batch authentication with physical state awareness by embedding the terminal energy status into the authentication request and grouping, sorting, and constructing subtrees and forest structures according to the energy harvesting mode, thus providing a data structure foundation for subsequent scheduling optimization and traceability.

[0012] By embedding terminal physical state information within the batch authentication framework, authentication decisions are linked to terminal physical characteristics. Based on the dynamic laws of energy harvesting in passive terminals, the timing and composition of authentication batch formation can be adaptively adjusted to optimize authentication scheduling and improve overall authentication performance.

[0013] Furthermore, the fields of the energy state identifier include:

[0014] Energy Level (EL): A 2-digit code indicating the terminal's current remaining energy level: 00: Emergency, 01: Low, 10: Medium, 11: High;

[0015] Energy Harvesting Mode (PM): 2-bit code indicating energy harvesting mode, 00: radio frequency energy, 01: light energy, 10: vibration energy, 11: hybrid;

[0016] Predictive stability (PS): A 1-bit flag indicating the stability based on historical predictions; 0: unstable, 1: stable.

[0017] Urgency Level (UR): A 1-bit flag indicating the urgency level of the application layer, 0: normal, 1: urgent.

[0018] By using coding design based on energy level, collection mode, predicted stability, and urgency, the physical state of the terminal and application requirements are quantified, providing multi-dimensional trust anchors for authentication decisions and improving the granularity of authentication strategies.

[0019] Furthermore, the authentication request list is grouped according to the Energy Harvesting Mode (PM) field in the Energy Status Identifier, specifically as follows:

[0020] G = {G RF G Light G Vibration G Hybrid},

[0021] Among them, G Light Indicates the light energy group; G RF Indicates radio frequency group, G Vibration G represents a vibration group, where G stands for Group. HybridThis refers to a combination of energy harvesting methods, such as the combination of light energy and vibration.

[0022] For each group G i (i.e. G) RF G Light G Vibration G Hybrid ), sorted by their application urgency (UR): urgent requests first.

[0023] Grouping by energy harvesting mode allows terminals with similar energy patterns to be aggregated in the same subtree, facilitating the adoption of appropriate authentication strategies (such as periodic scheduling for solar power groups) and improving the physical consistency of authentication scheduling.

[0024] Furthermore, the aggregated energy tag includes the minimum energy level in the subtree, the dominant collection mode in the subtree, and the average prediction stability of the subtree.

[0025] Aggregated energy tags transmit the physical state summary of terminals within the subtree in intermediate nodes, enabling upper-layer authentication nodes to quickly perceive the overall energy status of the subtree and provide real-time basis for threshold adjustment, priority judgment, etc.

[0026] Furthermore, before constructing a certified Merkel forest, a batch maturity assessment step is also included:

[0027] For each authentication request r∈R, where R is the set of terminal requests currently pending, the optimal authentication time and confidence level C(r) of the authentication request are predicted using the energy prediction model M.

[0028] Based on the offset between the current time and the optimal authentication time, the confidence level, and the preset decay time constant, calculate the overall maturity of the current set of terminal requests to be processed.

[0029] If the overall maturity level is higher than the first threshold, then immediately construct a certified Merkel forest;

[0030] If the overall maturity is lower than the second threshold, remove the k authentication requests with the lowest maturity and recalculate the overall maturity for the remaining authentication requests.

[0031] If the overall maturity level is between the first and second thresholds, then calculate the average time offset Δt. avg :

[0032] Δt avg = avg(T opt (r) - T now ),

[0033] Among them, T now T is the current system time; opt (r) is the optimal authentication time window predicted by terminal r;

[0034] If Δt avg If the value is less than 0, immediately build and commit the certified Merkel forest; otherwise, wait for Δt. avg Reassess after the duration.

[0035] By predicting the optimal certification time and calculating overall maturity, dynamic and intelligent batch formation decisions are made, avoiding certification failures caused by waiting delays or insufficient energy due to fixed windows, and significantly improving system throughput and success rate.

[0036] Furthermore, calculate the overall maturity of the current set of pending terminal requests. ,for:

[0037] ,

[0038] Where C(r) is the confidence level, ranging from 0 to 1. The higher the value, the stronger the confidence level for T. opt The more reliable the prediction of (r), the better T now T is the current system time; opt (r) is the optimal authentication time window predicted by terminal r; T now —T opt (r) represents the time offset; τ is an adjustable decay time constant, which is a valve that controls the effect of the time offset on maturity. The larger this value is, the greater the time tolerance.

[0039] By introducing an exponential decay function of confidence level and time offset, the overall maturity of terminal authentication timing within a batch is quantified, providing a mathematical basis for scheduling decisions and enhancing the interpretability and controllability of scheduling logic.

[0040] Furthermore, a Merkle tree subtree is constructed based on the sorted authentication requests, employing different tree structures according to the energy harvesting mode:

[0041] For the photoelectric group (G Light ): A balanced binary tree is used;

[0042] For radio frequency group (G RF ): It adopts a binary heap-like structure;

[0043] For vibration group (G) Vibration ): Use a deeper tree (such as an unbalanced binary tree, a sparse Merkle tree, etc.).

[0044] Different tree structures (such as balanced tree, heap, and deep tree) are adopted according to the energy harvesting mode to optimize the terminal access efficiency and authentication path under different energy modes, thereby improving the computational efficiency and resource utilization of the authentication process.

[0045] Furthermore, in the process of constructing the Merkle tree subtree, the steps for generating the aggregated energy label of each intermediate node from bottom to top are as follows:

[0046] S81, leaf node initialization, each leaf node corresponds to a terminal request, and its AET is directly parsed from the energy state identifier;

[0047] S82, Internal Node Aggregation: When two child nodes merge into a parent node, the parent node's AET is calculated using the aggregation function: min_EL = min(left child.min_EL, right child.min_EL), where min_EL is the minimum energy level, min is the minimum, left child.min_EL is the minimum energy level of the left child node, and right child.min_EL is the minimum energy level of the right child node;

[0048] S83, repeat steps S81-S82 until the AET of the root node can reflect the energy state summary of all terminals in the entire subtree.

[0049] In the process of constructing the subtree Merkle tree, aggregated energy tags are generated from the bottom up, realizing the layer-by-layer transmission and aggregation of physical state information in the tree structure.

[0050] Furthermore, the root hashes of each subtree Merkle tree are combined sequentially according to their energy harvesting patterns to calculate the root hash of the entire forest (Forest). Root ,for:

[0051] Forest Root = Hash(Root) Light || Root RF || Root Vibration || Root Hybrid ),

[0052] Among them, Root Light The root hash value represents the Merkle tree root of the solar array. RF The root hash value represents the Merkle tree of the radio frequency energy group. Vibration The root hash value represents the Merkle tree root of the vibration group. Hybrid This represents the Merkle root hash value of the mixed group, and || represents the string concatenation operator;

[0053] Record the metadata of each subtree Merkle tree: group type, number of terminals, and average energy level.

[0054] By combining subtree root hashes and recording metadata, a verifiable and traceable global authentication structure is constructed, providing complete data support for subsequent verification, auditing, and scheduling backtracking.

[0055] The present invention also provides an adaptive scheduling system for data structure for passive IoT access authentication, including a processing unit, which is used to obtain authentication requests from multiple passive terminals and execute the method described in the present invention to output a complete authentication Merkle forest structure.

[0056] This system utilizes processing units to achieve fully automated authentication from request retrieval to forest output, providing a deployable system solution for passive IoT access authentication. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating the adaptive scheduling method for data structures used in passive IoT access authentication according to the present invention. Detailed Implementation

[0058] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0059] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0060] In the description of this invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "linking" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two components. They can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0061] This invention discloses an adaptive scheduling method for data structure in passive IoT access authentication. It embeds terminal physical state information within a batch authentication framework, linking authentication decisions with terminal physical characteristics. Based on the dynamic laws of energy harvesting in passive terminals, it adaptively adjusts the timing and composition of authentication batches, thereby optimizing authentication scheduling and improving overall authentication performance.

[0062] like Figure 1As shown, the adaptive scheduling method for data structures used for passive IoT access authentication includes the following steps:

[0063] Obtain authentication requests from multiple passive terminals. Each authentication request includes a unique identifier of the terminal, a certificate hash, a request timestamp, an energy status identifier, and a location context. Based on the energy harvesting mode field in the energy status identifier, group the authentication requests to form several policy subtrees.

[0064] Within each policy subtree, authentication requests are sorted according to the priority order of energy level, urgency, and predicted stability of the energy status identifier. When sorting, energy level is considered first, and terminals with higher energy levels are processed first. If the energy levels are the same, terminals with higher urgency are processed first. If the energy level and urgency are the same, terminals with a predicted stability value of 1 are given priority. If all three are the same, then the requests are sorted according to time.

[0065] A Merkle tree subtree is constructed based on the sorted authentication requests, and an Aggregated Energy Tag (AET) is generated for each intermediate node from bottom to top during the construction process. Preferably, the AET includes the minimum energy level in the subtree, the dominant collection mode in the subtree, and the average prediction stability of the subtree. For example, (1, 00, 0.8) represents that the subtree has a minimum energy level of 1, a dominant radio frequency energy terminal, and a prediction stability of 0.8. The AET is a data structure containing fields, and the data in the corresponding fields needs to be read from the AET before further processing.

[0066] The root hashes of each subtree Merkle tree are combined in order of energy collection mode to calculate the root hash of the entire forest, and the complete certified Merkle forest structure is output.

[0067] The Authentication Merkel Forest is a two-level, multi-dimensional labeled Merkel tree structure, including enhanced leaf nodes and intermediate nodes. Enhanced leaf nodes: Each leaf node corresponds to an authentication request from a passive terminal, but unlike traditional hashes, its content is multi-dimensionally encoded, as shown in Table 1.

[0068] Table 1 Contents of Enhanced Leaf Nodes

[0069]

[0070] Intermediate Nodes: In a traditional Merkle tree, intermediate nodes are simply concatenated hashes of their child nodes. This invention enhances the strategy of intermediate nodes, storing not only the hash value H but also an Aggregated Energy Tag (AET) for each node. The contents of the energy tag are shown in Table 2.

[0071] Table 2. Contents of the Energy Label

[0072]

[0073] The certified Merkel forest consists of multiple policy subtrees, according to the following rules:

[0074] First-level partitioning: Terminal requests are grouped according to energy harvesting mode (PM). Requests with the same PM are preferentially assigned to the same subtree, so that the tree structure can reflect the classification of energy sources in the physical world.

[0075] Secondary sorting: Within each subtree, leaf nodes are sorted according to priority (EL, UR, PS), with high-energy, high-urgency, and high-stability requests placed closer to the root.

[0076] Ultimately, the root hash of the entire forest (Forest_Root) is calculated from the root hashes of each subtree in PM order.

[0077] In a preferred embodiment of the present invention, the energy state identifier field includes:

[0078] Energy Level (EL): A 2-digit code indicating the terminal's current remaining energy level: 00: Emergency, 01: Low, 10: Medium, 11: High;

[0079] Energy Harvesting Mode (PM): 2-bit code indicating energy harvesting mode, 00: radio frequency energy, 01: light energy, 10: vibration energy, 11: hybrid;

[0080] Predictive stability (PS): A 1-bit flag indicating the stability based on historical predictions; 0: unstable, 1: stable.

[0081] Urgency Level (UR): A 1-bit flag indicating the urgency level of the application layer, 0: normal, 1: urgent.

[0082] The data structure itself has authentication meaning. Energy status serves as the anchor of trust. Terminals with higher energy levels are considered more reliable authentication subjects because they are more likely to complete the full authentication process. This trust level is transmitted and verified in the tree structure through the Energy_Flag of the leaf nodes and the AET of the intermediate nodes.

[0083] The collection mode serves as the basis for scheduling. Terminals using solar energy exhibit a distinct day-night cycle, while radio frequency driven terminals in passive IoT are event-driven. Grouping by PM (Power Generation Process) allows each batch to employ different strategies for different energy modes.

[0084] The urgency level affects the authentication path. Urgent requests are placed shallower in the tree (closer to the root), so they can be accessed faster when partial verification is required.

[0085] In a preferred embodiment of the present invention, the authentication request list is grouped according to the energy harvesting mode (PM) field in the energy state identifier, specifically as follows:

[0086] G = {G RF G Light G Vibration G Hybrid},

[0087] Among them, G Light Indicates the light energy group; G RF Indicates radio frequency group, G Vibration G represents a vibration group, where G stands for Group. Hybrid This refers to a combination of energy harvesting methods, such as the combination of light energy and vibration.

[0088] For each group G i (i.e. G) RF G Light G Vibration G Hybrid ), sorted by their application urgency (UR): urgent requests first.

[0089] On the gateway side (passive IoT terminal read / write device), execute the batch access authentication algorithm:

[0090] Input: The current set of terminal requests to be processed, R; the energy prediction model, M;

[0091] Output: Batch formation decision (form immediately / wait for Δt / remove a request).

[0092] In a preferred embodiment of the present invention, a batch maturity assessment step is further included before constructing the certified Merkel Forest:

[0093] For each authentication request r∈R, where R is the set of terminal requests to be processed, the optimal authentication time and confidence level C(r) of the authentication request are predicted using the energy prediction model M (a traditional linear regression model is sufficient; any model that can achieve this function in the existing technology is used).

[0094] Based on the offset between the current time and the optimal authentication time, the confidence level, and the preset decay time constant, calculate the overall maturity of the current set of terminal requests to be processed.

[0095] If the overall maturity level is higher than the first threshold (which can be dynamically adjusted, for example, 0.75), then a certified Merkel forest is immediately constructed.

[0096] If the overall maturity is lower than the second threshold (which can be dynamically adjusted, for example, 0.35), it means that the batch maturity is too low and is not suitable as a whole. In this case, the k certification requests with the lowest maturity (for example, 20% of the current batch) are removed, and the overall maturity is recalculated for the remaining certification requests.

[0097] If the overall maturity level is between the first and second thresholds, then calculate the average time offset Δt. avg :

[0098] Δt avg = avg(T opt (r) - T now ),

[0099] Among them, T now T is the current system time; opt (r) is the optimal authentication time window predicted for terminal r, which can be calculated using the existing energy prediction model M. For example, by collecting the day-night cycle of the solar energy terminal and the random triggering characteristics of the vibration terminal, the time point when the terminal's energy reserve reaches a preset threshold (e.g., remaining energy ≥ 80% of total capacity) can be predicted; or by combining the energy prediction results with historical authentication data, the time point when the terminal has sufficient energy and the communication link quality is optimal (e.g., signal-to-noise ratio ≥ 20dB) can be selected.

[0100] If Δt avg If the value is less than 0, it indicates that most terminals have passed their optimal time. In this case, immediately build and submit the certified Merkel forest; otherwise, wait for Δt. avg Reassess after the duration.

[0101] In a preferred embodiment of the present invention, the overall maturity of the current set of terminal requests to be processed is calculated. ,for:

[0102] ,

[0103] Where C(r) is the confidence level, i.e., the prediction stability of the terminal, ranging from 0 to 1. The higher the value, the better the prediction stability of T. opt The more reliable the prediction of (r), the better T now T is the current system time; opt (r) is the optimal authentication time window predicted by terminal r; T now —T opt (r) represents the time offset; τ is an adjustable decay time constant (selectable from 30 to 120 seconds, preferably 60 seconds), which is the valve that controls the effect of the time offset on maturity. The larger this value is, the greater the time tolerance.

[0104] In a preferred embodiment of the present invention, a Merkle tree subtree is constructed based on the sorted authentication requests, and different tree structures are adopted according to the energy harvesting mode:

[0105] For the photoelectric group (G Lgiht ): A balanced binary tree is used;

[0106] For radio frequency group (G RF ): It adopts a binary heap-like structure;

[0107] For vibration group (G) Vibration ): Use a deeper tree (such as an unbalanced binary tree, a sparse Merkle tree, etc.).

[0108] In a preferred embodiment of the present invention, the step of generating the aggregated energy tag of each intermediate node from bottom to top during the construction of the subtree Merkle tree is as follows:

[0109] S81, leaf node initialization, each leaf node corresponds to a terminal request, and its AET is directly parsed from the energy state identifier;

[0110] S82, Internal Node Aggregation: When two child nodes merge into a parent node, the parent node's AET is calculated using the aggregation function: min_EL = min(left child.min_EL, right child.min_EL), where min_EL is the minimum energy level, min is the minimum, left child.min_EL is the minimum energy level of the left child node, and right child.min_EL is the minimum energy level of the right child node;

[0111] In another preferred embodiment of the present invention, the energy harvesting modes of all terminals in the current subtree can be statistically analyzed (e.g., using a linear regression model), and the type that appears most frequently can be selected as the number-dominant harvesting mode; if the number of occurrences is the same, the mode is determined according to the preset priority of light energy > radio frequency > vibration.

[0112] In another preferred embodiment of the present invention, the average prediction stability of the subtree is calculated using a weighted average: the weighted sum of the left and right child nodes is divided by the total number of terminals in the subtree, with the number of terminals in the child node as the weight, to obtain the average prediction stability of the parent node (for example, the left child node contains 5 terminals, PS=0.8, the right child node contains 3 terminals, PS=0.6, and the parent node PS=(5×0.8+3×0.6) / (5+3)=0.725).

[0113] S83, repeat steps S81-S82 until the AET of the root node can reflect the energy state summary of all terminals in the entire subtree.

[0114] In a preferred embodiment of the present invention, the root hashes of each subtree Merkle tree are combined sequentially according to their energy harvesting patterns to calculate the root hash of the entire forest (Forest). Root ,for:

[0115] Forest Root = Hash(Root) Light || Root RF || Root Vibration || Root Hybrid ),

[0116] Among them, Root Light The root hash value represents the Merkle tree root of the solar array. RF The root hash value represents the Merkle tree of the radio frequency energy group. Vibration The root hash value represents the Merkle tree root of the vibration group. Hybrid This represents the Merkle root hash value of the mixed group, and || represents the string concatenation operator;

[0117] Record the metadata of each subtree Merkle tree: group type, number of terminals, and average energy level.

[0118] The present invention also provides an adaptive scheduling system for data structure for passive IoT access authentication, including a processing unit. The processing unit is used to obtain authentication requests from multiple passive terminals and execute the method described in the present invention to output a complete authentication Merkle forest structure.

[0119] This invention encodes the physical energy status of the terminal as a component of the authentication data structure, enabling subsequent authentication decisions (such as threshold adjustment) to directly and efficiently retrieve physical state information from the data structure. By aligning authentication scheduling with the terminal's physical energy cycle, waiting and retries are reduced, authentication latency is lowered, and only terminals with sufficient energy are included in the batch, significantly improving the first-time authentication success rate.

[0120] The specific embodiments described herein are merely illustrative examples of the present invention. Those skilled in the art can make various modifications or additions to the described embodiments or use similar methods to substitute them, without departing from the technology of the present invention or exceeding the scope defined by the appended claims.

Claims

1. An adaptive scheduling method for data structures used in passive Internet of Things (IoT) access authentication, characterized in that, Includes the following steps: Obtain authentication requests from multiple passive terminals. Each authentication request includes a unique identifier for the terminal, a certificate hash, a request timestamp, an energy status identifier, and a location context. The fields of the energy status identifier include energy level (EL), energy harvesting mode (PM), predicted stability (PS), and urgency level (UR). Based on the energy harvesting mode field in the energy status identifier, the authentication requests are grouped to form several policy subtrees. Energy Harvesting Mode PM: 2-bit code indicating the energy harvesting mode, 00: radio frequency energy, 01: light energy, 10: vibration energy, 11: mixed; Within each policy subtree, authentication requests are sorted according to the priority order of urgency, energy level, and predicted stability of the energy state identifier. When sorting, energy level is considered first, and terminals with higher energy levels are processed first. If the energy levels are the same, terminals with higher urgency are processed first. If the energy level and urgency are the same, terminals with a predicted stability value of 1 are given priority. If all three are the same, then sort them according to their chronological order. A Merkle tree subtree is constructed based on the sorted authentication requests, and an aggregated energy label (AET) for each intermediate node is generated from bottom to top during the construction process. The aggregated energy label includes the minimum energy level in the subtree, the dominant collection mode in the subtree, and the average prediction stability of the subtree. The root hashes of each subtree Merkle tree are combined in order of energy harvesting mode to calculate the root hash of the entire forest and output the complete certified Merkle forest structure. A Merkle tree subtree is constructed based on the sorted authentication requests, and different tree structures are used depending on the energy harvesting mode: For the photoelectric group G Light Use a balanced binary tree; For radio frequency group G RF It adopts a binary heap-like structure; For vibration group G Vibration Use an unbalanced binary tree or a sparse Merkle tree; In the process of constructing the Merkle tree subtree, the steps for generating the aggregated energy label of each intermediate node from bottom to top are as follows: S81, leaf node initialization, each leaf node corresponds to a terminal request, and its AET is directly parsed from the energy state identifier; S82, Internal Node Aggregation: When two child nodes merge into a parent node, the parent node's AET is calculated using the aggregation function: min_EL = min(left child.min_EL, right child.min_EL), where min_EL is the minimum energy level, min is the minimum, and left child.min_EL is the minimum energy level of the left child node; `right_child.min_EL`: The minimum energy level of the right child node; S83, repeat steps S81-S82 until the AET of the root node can reflect the energy state summary of all terminals in the entire subtree.

2. The adaptive scheduling method for data structures for passive IoT access authentication according to claim 1, characterized in that, Energy Level EL: A 2-digit code indicating the terminal's current remaining energy level: 00: Emergency, 01: Low, 10: Medium, 11: High; Predictive stability PS: A 1-bit flag indicating stability based on historical predictions; 0: unstable, 1: stable. Urgency Level UR: A 1-bit flag indicating the urgency level of the application layer, 0: normal, 1: urgent.

3. The adaptive scheduling method for data structures for passive IoT access authentication according to claim 1, characterized in that, Based on the Energy Harvesting Mode (PM) field in the Energy Status Identifier, the authentication request list is grouped as follows: G = {G RF , G Light , G Vibration , G Hybrid }, Among them, G Light Indicates the light energy group; G RF Indicates radio frequency group, G Vibration G represents a vibration group, where G stands for Group. Hybrid Indicates a mixed group; For each group G i G RF G Light G Vibration G Hybrid URs are sorted by their application urgency: urgent requests come first.

4. The adaptive scheduling method for data structures for passive IoT access authentication according to claim 1, characterized in that, Before building a certified Merkel forest, a batch maturity assessment step is also included: For each authentication request r∈R, where R is the set of terminal requests currently pending, the optimal authentication time and confidence level C(r) of the authentication request are predicted using the energy prediction model M. Based on the offset between the current time and the optimal authentication time, the confidence level, and the preset decay time constant, calculate the overall maturity of the current set of terminal requests to be processed. If the overall maturity level is higher than the first threshold, then immediately construct a certified Merkel forest; If the overall maturity is lower than the second threshold, remove the k authentication requests with the lowest maturity and recalculate the overall maturity for the remaining authentication requests. If the overall maturity level is between the first and second thresholds, then calculate the average time offset Δt. avg : Δt avg = avg(T opt (r) - T now ), Among them, T now T is the current system time; opt (r) is the optimal authentication time window predicted by terminal r; If Δt avg If the value is less than 0, immediately build and commit the certified Merkel forest; otherwise, wait for Δt. avg Reassess after duration; Calculate the overall maturity of the current set of pending terminal requests. ,for: ， Where C(r) is the confidence level, ranging from 0 to 1. The higher the value, the stronger the confidence level for T. opt The more reliable the prediction of (r), the better T now T is the current system time; opt (r) is the optimal authentication time window predicted by terminal r; T now —T opt (r) represents the time offset; τ is an adjustable decay time constant, which is a valve that controls the effect of the time offset on maturity. The larger this value is, the greater the time tolerance.

5. The adaptive scheduling method for data structures for passive IoT access authentication according to claim 1, characterized in that, The root hashes of each Merkle tree subtree are combined in order of energy harvesting mode to calculate the root hash of the entire forest, Forest. Root ,for: Forest Root = Hash( Root Light || Root RF || Root Vibration || Root Hybrid ), Among them, Root Light The root hash value represents the Merkle tree root of the solar array. RF The root hash value represents the Merkle tree of the radio frequency energy group. Vibration The root hash value represents the Merkle tree root of the vibration group. Hybrid This represents the Merkle root hash value of the mixed group, and || represents the string concatenation operator; Record the metadata of each subtree Merkle tree: group type, number of terminals, and average energy level.

6. A data structure adaptive scheduling system for passive Internet of Things (IoT) access authentication, characterized in that, The system includes a processing unit, which is used to obtain authentication requests from multiple passive terminals and execute the method described in any one of claims 1-5 to output a complete authentication Merkle forest structure.

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